On representing chemical environments

Abstract

We review some recently published methods to represent atomic neighbourhood environments, and analyse their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces. The crucial properties that such representations (sometimes called descriptors) must have are differentiability with respect to moving the atoms, and invariance to the basic symmetries of physics: rotation, reflection, translation, and permutation of atoms of the same species. We demonstrate that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighbourhood density function. Using the example system of small clusters, we quantitatively show that this expansion needs to be carried to higher and higher wave numbers as the number of neighbours increases in order to obtain a faithful representation, and that variants of the descriptors converge at very different rates. We also propose an altogether new approach, called Smooth Overlap of Atomic Positions (SOAP), that sidesteps these difficulties by directly defining the similarity between any two neighbourhood environments, and show that it is still closely connected to the invariant descriptors. We test the performance of the various representations by fitting models to the potential energy surface of small silicon clusters and the bulk crystal.

13 pages, 11 figures

References in corpus (1)

• Racah's method for general subalgebra chains: Coupling coefficients of SO(5) in canonical and physical bases

Cited by in corpus (578)

• Machine learning and the physical sciences
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine Learning Force Fields
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Machine learning for molecular simulation
• Molecular Contrastive Learning of Representations via Graph Neural Networks
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Comparing molecules and solids across structural and alchemical space
• Machine Learning Unifies the Modelling of Materials and Molecules
• Data-driven materials science: status, challenges and perspectives
• Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
• Active learning of linearly parametrized interatomic potentials
• Machine learning prediction errors better than DFT accuracy
• On-the-fly machine learning force field generation: Application to melting points
• Machine-learning based interatomic potential for amorphous carbon
• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
• Physics-inspired structural representations for molecules and materials
• How to represent crystal structures for machine learning: towards fast prediction of electronic properties
• Opportunities and Challenges for Machine Learning in Materials Science
• Machine learning a general purpose interatomic potential for silicon
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Machine learning for electronically excited states of molecules
• Machine-learning interatomic potentials for materials science
• Insightful classification of crystal structures using deep learning
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• FCHL revisited: faster and more accurate quantum machine learning
• Alchemical and structural distribution based representation for improved QML
• Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• Representation of compounds for machine-learning prediction of physical properties
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Unified Representation of Molecules and Crystals for Machine Learning
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
• Autonomous discovery in the chemical sciences part II: Outlook
• Chemical Shifts in Molecular Solids by Machine Learning
• An Accurate and Transferable Machine Learning Potential for Carbon
• A Transferable Machine-Learning Model of the Electron Density
• A critical examination of compound stability predictions from machine-learned formation energies
• Applying machine learning techniques to predict the properties of energetic materials
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• A deep potential model with long-range electrostatic interactions
• Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
• De novo exploration and self-guided learning of potential-energy surfaces
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• Data-driven learning of total and local energies in elemental boron
• Extending the Accuracy of the SNAP Interatomic Potential Form
• A Machine Learning Potential for Graphene
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• A learning scheme to predict atomic forces and accelerate materials simulations
• On the Completeness of Atomic Structure Representations
• Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
• Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Machine-learned multi-system surrogate models for materials prediction
• Database of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
• Orbital Graph Convolutional Neural Network for Material Property Prediction
• End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
• Gaussian Process Regression for Geometry Optimization
• Machine-learning interatomic potential for radiation damage and defects in tungsten
• Efficient nonparametric $n$-body force fields from machine learning
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics
• Learning the electronic density of states in condensed matter
• Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
• Metrics for measuring distances in configuration spaces
• Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
• A critical examination of robustness and generalizability of machine learning prediction of materials properties
• Cormorant: Covariant Molecular Neural Networks
• Neural Network Potentials for Chemistry: Concepts, Applications and Prospects
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• DPA-2: a large atomic model as a multi-task learner
• How to validate machine-learned interatomic potentials
• Ab-initio quantum chemistry with neural-network wavefunctions
• Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
• Machine Learning on Neutron and X-Ray Scattering
• Automated Structure Discovery in Atomic Force Microscopy
• A fingerprint based metric for measuring similarities of crystalline structures
• Unified Graph Neural Network Force-field for the Periodic Table
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• Atomic cluster expansion of scalar, vectorial and tensorial properties and including magnetism and charge transfer
• Operators in Machine Learning: Response Properties in Chemical Space
• Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
• Discovering the Building Blocks of Atomic Systems using Machine Learning
• Accelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface
• Maximizing information from chemical engineering data sets: Applications to machine learning
• The Many-Body Expansion Combined with Neural Networks
• Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
• A sparse representation for potential energy surface
• A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
• Machine learning determination of atomic dynamics at grain boundaries
• Updates to the DScribe Library: New Descriptors and Derivatives
• Transfer learning for materials informatics using crystal graph convolutional neural network
• Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality
• Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
• Gaussian Process Regression for Transition State Search
• Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
• High-Dimensional Neural Network Potentials for Magnetic Systems Using Spin-Dependent Atom-Centered Symmetry Functions
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Deep Learning of Accurate Force Field of Ferroelectric HfO${}_2$
• Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
• Gaussian approximation potentials for body-centered-cubic transition metals
• Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
• First-principles interatomic potentials for ten elemental metals via compressed sensing
• Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
• Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
• Sparse Gaussian Process Potentials: Application to Lithium Diffusivity in Superionic Conducting Solid Electrolytes
• Stratified construction of neural network based interatomic models for multicomponent materials
• Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
• Wavelet Scattering Regression of Quantum Chemical Energies
• Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
• Deep learning inter-atomic potential model for accurate irradiation damage simulations
• Ultra-fast interpretable machine-learning potentials
• Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
• Tutorial: How to Train a Neural Network Potential
• Thermodynamics and dielectric response of ${\text{BaTiO}}_3$ by data-driven modeling
• Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
• High-throughput property-driven generative design of functional organic molecules
• Graph similarity drives zeolite diffusionless transformations and intergrowth
• Chemical diversity in molecular orbital energy predictions with kernel ridge regression
• PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
• The Materials Simulation Toolkit for Machine Learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials research
• Recursive evaluation and iterative contraction of $N$-body equivariant features
• BIGDML: Towards Exact Machine Learning Force Fields for Materials
• Band gap prediction for large organic crystal structures with machine learning
• Complex ${\mathrm{Ga}}_2{\mathrm{O}}_3$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials
• Hybrid neural network potential for multilayer graphene
• Global optimization of atomistic structure enhanced by machine learning
• A Multi-scale Approach for Simulations of Kelvin Probe Force Microscopy with Atomic Resolution
• Machine-learning based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies
• Navigating at Will on the Water Phase Diagram
• Deep learning generative model for crystal structure prediction
• Simple machine-learned interatomic potentials for complex alloys
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
• MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
• Data-driven Material Models for Atomistic Simulation
• Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
• Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and $\mathbf{GW}$
• Equation of state of fluid methane from first principles with machine learning potentials
• IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations
• Calculation of phase diagrams in the multithermal-multibaric ensemble
• Interatomic potentials: Achievements and challenges
• Learning molecular energies using localized graph kernels
• i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
• Accurate large-scale simulations of siliceous zeolites by neural network potentials
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
• NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing
• Gaussian Process-Based Refinement of Dispersion Corrections
• Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
• Ranking the information content of distance measures
• PANNA: Properties from Artificial Neural Network Architectures
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
• Detection of Topological Materials with Machine Learning
• Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C${}_{60}$
• Discrepancies and Error Evaluation Metrics for Machine Learning Interatomic Potentials
• A Perspective on Deep Learning for Molecular Modeling and Simulations
• Extensive deep neural networks for transferring small scale learning to large scale systems
• Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character
• High-Dimensional Potential Energy Surfaces for Molecular Simulations
• Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning
• Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations
• Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential
• Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
• An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
• Conceptual and practical bases for the high accuracy of machine learning interatomic potential
• Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
• Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
• MAISE: Construction of neural network interatomic models and evolutionary structure optimization
• Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
• A local order metric for condensed phase environments
• Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
• Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
• Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
• Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
• Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
• Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
• Convolutional neural networks for atomistic systems
• Efficient implementation of atom-density representations
• A Novel Approach to Describe Chemical Environments in High Dimensional Neural Network Potentials
• An assessment of the structural resolution of various fingerprints commonly used in machine learning
• Predicting tensorial molecular properties with equivariant machine-learning models
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
• The Robustness of Cluster Expansion: Assessing the Roles of Relaxation and Numerical Error
• Quantum dynamics using path integral coarse-graining
• Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr${}_3$ a phonon-liquid with Cs rattlers
• Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water
• Lifelong Machine Learning Potentials
• Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
• A reactive neural network framework for water-loaded acidic zeolites
• Machine-learning structural reconstructions for accelerated point defect calculations
• Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies
• Prediction of Atomization Energy Using Graph Kernel and Active Learning
• Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
• Global optimization of atomic structures with gradient-enhanced Gaussian process regression
• Developments and Further Applications of Ephemeral Data Derived Potentials
• On Machine Learning Force Fields for Metallic Nanoparticles
• Combining phonon accuracy with high transferability in Gaussian approximation potential models
• $\alpha$-$\beta$ phase transition of zirconium predicted by on-the-fly machine-learned force field
• Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
• Atomic energy mapping of neural network potential
• Machine learning potentials for multicomponent systems: The Ti-Al binary system
• Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
• Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
• A New Kind of Atlas of Zeolite Building Blocks
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds
• High pressure hydrogen by machine learning and quantum Monte Carlo
• Tensor-reduced atomic density representations
• Symmetry-adapted modeling for molecules and crystals
• Ab initio random structure searching for battery cathode materials
• Can Symmetric Tilt Grain Boundaries Represent Polycrystals?
• Efficiency, Accuracy, and Transferability of Machine Learning Potentials: Application to Dislocations and Cracks in Iron
• PyXtal FF: a Python Library for Automated Force Field Generation
• Roadmap on machine learning glassy dynamics
• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
• Neural Network Kinetics for Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials
• Dimensionality reduction of local structure in glassy binary mixtures
• Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys
• Exploring a potential energy surface by machine learning for characterizing atomic transport
• Structural Dynamics Descriptors for Metal Halide Perovskites
• Layer-by-layer phase transformation in Ti${}_3$O${}_5$ revealed by machine learning molecular dynamics simulations
• Atomistic Structure Learning Algorithm with surrogate energy model relaxation
• Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
• Hierarchical Inter-Message Passing for Learning on Molecular Graphs
• Predicting the phase diagram of titanium dioxide with random search and pattern recognition
• E(n)-Equivariant Cartesian Tensor Passing Potential
• Machine learning approach to genome of two-dimensional materials with flat electronic bands
• Hydration free energies from kernel-based machine learning: Compound-database bias
• Learning from the Density to Correct Total Energy and Forces in First Principle Simulations
• Machine Learning Characterization of Structural Defects in Amorphous Packings of Dimers and Ellipses
• Advances in modeling complex materials: The rise of neuroevolution potentials
• Robustness of Local Predictions in Atomistic Machine Learning Models
• Molecule-specific Uncertainty Quantification in Quantum Chemical Studies
• Unsupervised landmark analysis for jump detection in molecular dynamics simulations
• Permutation-invariant distance between atomic configurations
• Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX${}_3$ (X=I, Br, Cl) perovskites
• Analysis of Trajectory Similarity and Configuration Similarity in On-the-Fly Surface-Hopping Simulation on Multi-Channel Nonadiabatic Photoisomerization Dynamics
• Descriptors-free Collective Variables From Geometric Graph Neural Networks
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• Development of a physically-informed neural network interatomic potential for tantalum
• MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
• Unsupervised learning of atomic environments from simple features
• Predicting the Charge Density Response in Metal Electrodes
• Computer Simulation of the Early Stages of Self-Assembly and Thermal Decomposition of ZIF-8
• A spectral-neighbour representation for vector fields: machine-learning potentials including spin
• ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training
• Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions
• Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime
• Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures
• FLAME: a library of atomistic modeling environments
• Matching Crystal Structures Atom-to-Atom
• Arrested phase separation in double-exchange models: machine-learning enabled large-scale simulation
• Phonon-induced localization of excitons in molecular crystals from first principles
• An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields
• Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
• Formula graph self-attention network for representation-domain independent materials discovery
• Towards fully ab initio simulation of atmospheric aerosol nucleation
• Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling
• KLIFF: A framework to develop physics-based and machine learning interatomic potentials
• Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water
• Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions
• Completeness of Atomic Structure Representations
• Generating equilibrium molecules with deep neural networks
• Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics
• Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
• Electronic structure based descriptor for characterizing local atomic environments
• Modeling electronic response properties with an explicit-electron machine learning potential
• Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence
• Integrating Machine Learning with Physics-Based Modeling
• Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations
• Structure and Bonding in Amorphous Red Phosphorus
• Exploring the configurational space of amorphous graphene with machine-learned atomic energies
• Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
• Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
• Design guidelines for two-dimensional transition metal dichalcogenide alloys
• TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time-variations of SOAP spectra
• An orbital-based representation for accurate Quantum Machine Learning
• Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory
• UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning
• Atomistic structure search using local surrogate mode
• Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
• Innate Dynamics and Identity Crisis of a Metal Surface Unveiled by Machine Learning of Atomic Environments
• Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations
• Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
• Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
• Describe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters
• Ephemeral ice-like local environments in classical rigid models of liquid water
• Structural Simplicity as a Restraint on the Structure of Amorphous Silicon
• Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments
• Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations
• Machine learning nonequilibrium electron forces for adiabatic spin dynamics
• A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces and nanoparticles
• Optimal radial basis for density-based atomic representations
• Tracing Ion Migration in Halide Perovskites with Machine Learned Force Fields
• In operando active learning of interatomic interaction during large-scale simulations
• Uncertainty quantification and estimation in differential dynamic microscopy
• Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power
• Machine learning density functionals from the random-phase approximation
• Searching for iron nanoparticles with a general-purpose Gaussian approximation potential
• 3DReact: Geometric deep learning for chemical reactions
• Lattice dynamics and elastic properties of alpha-U at high-temperature and high-pressure by machine learning potential simulations
• Visualising energy landscapes through manifold learning
• Modeling the Ga/As binary system across temperaturesand compositions from first principles
• Physics-informed Bayesian inference of external potentials in classical density-functional theory
• Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials
• A mapping space Odyssey: characterising the statistical and metric properties of reduced representations of macromolecules
• Machine-learning accelerated identification of exfoliable two-dimensional materials
• SPA${}^{\mathrm{H}}$M: the Spectrum of Approximated Hamiltonian Matrices representations
• Quantum Energy Regression using Scattering Transforms
• Ab Initio Generalized Langevin Equation
• Creation of crystal structure reproducing X-ray diffraction pattern without using database
• Phase Transition of Two-Dimensional Ferroelectric and Paraelectric Ga2O3 Monolayer: A Density Functional Theory and Machine-Learning Study
• Machine Learning Diffusion Monte Carlo Energies
• Classical and Machine Learning Interatomic Potentials for BCC Vanadium
• Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery
• Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
• Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations
• Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction
• Modeling of many-body interactions between elastic spheres through symmetry functions
• Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population
• Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances
• ZundEig: The Structure of the Proton in Liquid Water From Unsupervised Learning
• Quantum chemical roots of machine-learning molecular similarity descriptors
• Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat
• Computational study of crystal defects formation in Mo by machine learned molecular dynamics simulations
• Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol
• Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning
• The fundamentals of quantum machine learning
• Machine Learning for compositional disorder: A Comparison Between Different Descriptors and Machine Learning Frameworks
• Ab initio machine learning of phase space averages
• Wasserstein metric for improved QML with adjacency matrix representations
• Optimized Multifidelity Machine Learning for Quantum Chemistry
• Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon
• Machine learning for phase ordering dynamics of charge density waves
• Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
• A machine learning potential for simulating infrared spectra of nanosilicate clusters
• A transferable artificial neural network model for atomic forces in nanoparticles
• Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries
• GPU-Accelerated Approximate Kernel Method for Quantum Machine Learning
• Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
• A collinear-spin machine learned interatomic potential for Fe${}_7$Cr${}_2$Ni alloy
• Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy
• Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance
• Anisotropic molecular coarse-graining by force and torque matching with neural networks
• Learning the energy curvature versus particle number in approximate density functionals
• Dynamical theory of angle-resolved electron energy loss and gain spectroscopies of phonons and magnons in transmission electron microscopy including multiple scattering effects
• Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
• Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
• "Layer-by-layer" Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical Systems
• High-pressure phase behaviors of titanium dioxide revealed by a $\Delta$-learning potential
• Machine learning predictions for local electronic properties of disordered correlated electron systems
• Pattern Matching and Classification of Clusters in Collision Cascades
• Phase Transition Pathway Sampling via Swarm Intelligence and Graph Theory
• Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
• Hydrogen-induced degradation dynamics in silicon heterojunction solar cells via machine learning
• Machine-learning of atomic-scale properties based on physical principles
• Augmentation of Universal Potentials for Broad Applications
• Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
• Mean-Field Density Matrix Decompositions
• Predicting hot-electron free energies from ground-state data
• Improved decision making with similarity based machine learning: Applications in chemistry
• Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics
• Generating candidates in global optimization algorithms using complementary energy landscapes
• Computation of forces and stresses in solids: Towards accurate structural optimization with auxiliary-field quantum Monte Carlo
• Variational deep learning of equilibrium transition path ensembles
• Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
• Transferable empirical pseudopotenials from machine learning
• Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal Element Alloy
• Machine learning phases and criticalities without using real data for training
• Phase transitions of LaMnO${}_3$ and SrRuO${}_3$ from DFT + U based machine learning force fields simulations
• Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
• Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
• Machine learning forces trained by Gaussian process in liquid states: Transferability to temperature and pressure
• Accelerating Material Property Prediction using Generically Complete Isometry Invariants
• Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations
• Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO${}_2$
• A deep learning model for chemical shieldings in molecular organic solids including anisotropy
• Inorganic Crystal Structure Prototype Database based on Unsupervised Learning of Local Atomic Environments
• Building nonparametric $n$-body force fields using Gaussian process regression
• Multitask methods for predicting molecular properties from heterogeneous data
• Classifying the age of a glass based on structural properties: A machine learning approach
• Grain boundary interstitial segregation in substitutional binary alloys
• A Hessian-Based Assessment of Atomic Forces for Training Machine Learning Interatomic Potentials
• Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations
• Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
• Efficient force field and energy emulation through partition of permutationally equivalent atoms
• Spectral Neural Network Potentials for Binary Alloys
• SPA${}^{\mathrm{H}}$M(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
• Training-free hyperparameter optimization of neural networks for electronic structures in matter
• Imeall: A Computational Framework for the Calculation of the Atomistic Properties of Grain Boundaries
• Prediction rigidities for data-driven chemistry
• Machine learning for structure-property relationships: Scalability and limitations
• Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
• Machine learning at the atomic-scale
• Crystal-Structure Matches in Solid-Solid Phase Transitions
• Decoding Energy Decomposition Analysis: Machine-Learned Insights on the Impact of the Density Functional on the Bonding Analysis
• Adaptive energy reference for machine-learning models of the electronic density of states
• Machine Learning and Materials Informatics: Recent Applications and Prospects
• Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
• Materials Expert-Artificial Intelligence for Materials Discovery
• A smooth basis for atomistic machine learning
• Compressing physical properties of atomic species for improving predictive chemistry
• Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four body interactions"
• Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
• Advancing descriptor search in materials science: feature engineering and selection strategies
• Five-fold Symmetry in Au-Si Metallic Glass
• Cluster-based multidimensional scaling embedding tool for data visualization
• Machine learning frontier orbital energies of nanodiamonds
• Fast proper orthogonal descriptors for many-body interatomic potentials
• Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches
• Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
• Reliable computational prediction of supramolecular ordering of complex molecules under electrochemical conditions
• $\Delta$-model correction of Foundation Model based on the models own understanding
• Electronic Descriptors for Supervised Spectroscopic Predictions
• Stability and distortion of fcc-LaH${}_{10}$ with path-integral molecular dynamics
• Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C
• Shifting computational boundaries for complex organic materials
• Efficient ensemble uncertainty estimation in Gaussian Processes Regression
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
• A neural network potential with self-trained atomic fingerprints: a test with the mW water potential
• Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
• Free energy of (CoxMn1-x)3O4 mixed phases from machine-learning-enhanced ab initio calculations
• Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
• Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation
• Critical assessment of machine-learned repulsive potentials for the Density Functional based Tight-Binding method: a case study for pure silicon
• Understanding Phonon Transport Properties Using Classical Molecular Dynamics Simulations
• On the detection and classification of material defects in crystalline solids after energetic particle impact simulations
• A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics
• EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties
• Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer
• The impact of static distortion waves on superlubricity
• Self-interaction and transport of solvated electrons in molten salts
• Change Point Detection of Events in Molecular Simulations using dupin
• Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
• Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
• Inversion of the chemical environment representations
• Accelerating structure search using atomistic graph-based classifiers
• A simple approach to rotationally invariant machine learning of avector quantity
• ClasSOMfier: A neural network for cluster analysis and detection of lattice defects
• Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
• Embedding quantum statistical excitations in a classical force field
• Quantification of Crystal Packing Similarity from Spherical Harmonic Transform
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
• Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
• Titanium-hydrogen interaction at megabar pressure
• Relevant, hidden, and frustrated information in high-dimensional analyses of complex dynamical systems with internal noise
• Data-driven assessment of optimal spatiotemporal resolutions for information extraction in noisy time series data
• Topological descriptors for the electron density of inorganic solids
• Interstitials as a key ingredient for P segregation to grain boundaries in polycrystalline $\alpha$-Fe
• Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
• Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
• High-performance descriptor for magnetic materials: Accurate discrimination of magnetic structure
• Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys
• GPR_calculator: An On-the-Fly Surrogate Model to Accelerate Massive Nudged Elastic Band Calculations
• Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schrödinger Equation
• A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information
• Investigating 3D Atomic Environments for Enhanced QSAR
• Predicting electronic screening for fast Koopmans spectral functional calculations
• Ab initio machine learning in chemical compound space
• Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid
• A Fuzzy Classification Framework to Identify Equivalent Atoms in Complex Materials and Molecules
• Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids
• Rapid Exploration of Optimization Strategies on Advanced Architectures using TestSNAP and LAMMPS
• Atomistic Graph Neural Networks for metals: Application to bcc iron
• Persistent Homology for Structural Characterization in Disordered Systems
• Transformative Applications of Machine Learning for Chemical Reactions
• RAFFLE: Active learning accelerated interface structure prediction
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions
• Machine Learning Force Field for Thermal Oxidation of Silicon
• Geographic-style maps with a local novelty distance help navigate the materials space
• Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
• Maximum Information Extraction Via Clustering and Minimization of Shannon Entropy
• Learning from metastable grain boundaries
• Zero Shot Molecular Generation via Similarity Kernels
• MADAS -- A Python framework for assessing similarity in materials-science data
• AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment
• Spectral Operator Representations
• Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning
• Structure-Driven Prediction of Magnetic Order in Uranium Compounds
• Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon
• A general formalism for machine-learning models based on multipolar-spherical harmonics
• Radiation damage and phase stability of Al${}_x$CrCuFeNi${}_y$ alloys using a machine-learned interatomic potential
• FaVAD: A software workflow for characterisation and visualizing of defects in crystalline structures
• Towards Improved Quantum Machine Learning for Molecular Force Fields
• Machine-Learned Potentials for Solvation Modeling
• Interpretable machine learned predictions of adsorption energies at the metal--oxide interface
• Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations
• Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms
• Edge Dynamics in Iron-Cluster Catalyzed Growth of Single-Walled Carbon Nanotubes Revealed by Molecular Dynamics Simulations based on a Neural Network Potential
• An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride
• k-Means Clustering in Fingerprint-Based Configuration Selection for Fitting Interatomic Potentials
• Considerations in the use of ML interaction potentials for free energy calculations
• Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis
• Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascades
• High-pressure melting and elastic behavior of vanadium and niobium based on ab initio and machine learning molecular dynamics
• A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning
• On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations
• Convergence of Body-Orders in Linear Atomic Cluster Expansions
• ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features
• Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation
• Quantifying Free-volume Topology in Atomistic Structures Through a Combination of Voxelization and Graph Theory
• From Local Atomic Environments to Molecular Information Entropy
• Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations
• Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
• Active Δ-learning with universal potentials for global structure optimization
• Emulating the First Principles of Matter: A Probabilistic Roadmap
• Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
• Unified theory of atom-centered representations and message-passing machine-learning schemes
• Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
• Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
• First principles interatomic potential for tungsten based on Gaussian process regression
• Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
• TBHubbard: tight-binding and extended Hubbard model database for metal-organic frameworks
• Representing spherical tensors with scalar-based machine-learning models
• Atomistic Global Optimization X: A Python package for optimization of atomistic structures
• Impact of Metal Cation on Chiral Properties of 2D Halide Perovskites
• Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations
• Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
• Machine-learning force-field models for dynamical simulations of metallic magnets
• Nanocrystal energetics via quantum similarity measures
• Systematic Study of Machine Learning Classification Algorithms of Zeolitic Imidazolate Framework Polymorphs
• Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning
• Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
• Integer linear programming for unsupervised training set selection in molecular machine learning
• Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe${}_2$
• Efficient Data Selection Methods for the Development of Machine Learned Potentials
• Simulations of water and hydrophobic hydration using a neural network potential
• Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure
• Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon
• Efficient discovery of multiple minimum action pathways using Gaussian process
• libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications
• Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
• Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
• REANN: A PyTorch-based End-to-End Multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems
• Machine learning of electronic structure and atomistic properties from the external potential
• Predicting co-segregation in alloys with solute-solute interactions
• Geometry-Based Neural-Network Prediction of Electron Localization Function Topology in Dense Hydrogen
• Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Active Learning of a Neural Network Potential for Large-Scale Atomistic Simulations of Polymer Electrolyte Membranes
• Machine learning approach for vibronically renormalized electronic band structures
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization
• Learning electron densities in the condensed phase
• Evaluating approaches for on-the-fly machine learning interatomic potential for activated mechanisms sampling with the activation-relaxation technique nouveau
• Compressing local atomic neighbourhood descriptors
• Simulations of Crystal Nucleation from Solution at Constant Chemical Potential
• Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning
• Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
• Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies