Alchemical and structural distribution based representation for improved QML

Abstract

We introduce a representation of any atom in any chemical environment for the generation of efficient quantum machine learning (QML) models of common electronic ground-state properties. The representation is based on scaled distribution functions explicitly accounting for elemental and structural degrees of freedom. Resulting QML models afford very favorable learning curves for properties of out-of-sample systems including organic molecules, non-covalently bonded protein side-chains, (H${}_2$O)${}_{40}$-clusters, as well as diverse crystals. The elemental components help to lower the learning curves, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training, as evinced for single, double, and triple bonds among main-group elements.

References in corpus (2)

• Machine Learning Unifies the Modelling of Materials and Molecules
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Cited by in corpus (110)

• Machine Learning Force Fields
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Data-driven materials science: status, challenges and perspectives
• Physics-inspired structural representations for molecules and materials
• Deep neural network solution of the electronic Schrödinger equation
• Machine learning a general purpose interatomic potential for silicon
• SchNetPack: A Deep Learning Toolbox For Atomistic Systems
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Machine learning for electronically excited states of molecules
• FCHL revisited: faster and more accurate quantum machine learning
• An Accurate and Transferable Machine Learning Potential for Carbon
• Perspective on integrating machine learning into computational chemistry and materials science
• On the Completeness of Atomic Structure Representations
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
• Atom-Density Representations for Machine Learning
• Machine-learned multi-system surrogate models for materials prediction
• Quantum Machine Learning for Chemistry and Physics
• Efficient nonparametric $n$-body force fields from machine learning
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
• Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• 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
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
• OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
• Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
• Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry
• 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
• SchNetPack 2.0: A neural network toolbox for atomistic machine learning
• Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
• Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
• Chemical diversity in molecular orbital energy predictions with kernel ridge regression
• Recursive evaluation and iterative contraction of $N$-body equivariant features
• PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
• BIGDML: Towards Exact Machine Learning Force Fields for Materials
• Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
• Equation of state of fluid methane from first principles with machine learning potentials
• Neural networks and kernel ridge regression for excited states dynamics of CH${}_2$NH${}_2^{+}$: From single-state to multi-state representations and multi-property machine learning models
• Machine learning and excited-state molecular dynamics
• Gaussian Process-Based Refinement of Dispersion Corrections
• Phase Transitions of Zirconia: Machine-Learned Force Fields Beyond Density Functional Theory
• Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
• Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
• Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations
• High-Dimensional Potential Energy Surfaces for Molecular Simulations
• Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone
• Machine learning configuration interaction for ab initio potential energy curves
• Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
• Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
• Efficient implementation of atom-density representations
• An assessment of the structural resolution of various fingerprints commonly used in machine learning
• Lifelong Machine Learning Potentials
• Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
• Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
• Machine learning the computational cost of quantum chemistry
• Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
• $\alpha$-$\beta$ phase transition of zirconium predicted by on-the-fly machine-learned force field
• ML Models of Vibrating H${}_2$CO: Comparing Reproducing Kernels, FCHL and PhysNet
• Perspective: Non-conventional Force Fields for Applications in Spectroscopy and Chemical Reaction Dynamics
• Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• Rapid and accurate molecular deprotonation energies from quantum alchemy
• Multi-Fidelity Machine Learning for Excited State Energies of Molecules
• An orbital-based representation for accurate Quantum Machine Learning
• Atomistic structure search using local surrogate mode
• Controlled exploration of chemical space by machine learning of coarse-grained representations
• A community-powered search of machine learning strategy space to find NMR property prediction models
• Resolution limit of data-driven coarse-grained models spanning chemical space
• 3DReact: Geometric deep learning for chemical reactions
• Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations
• Effects of perturbation order and basis set on alchemical predictions
• Critical Benchmarking of the G4(MP2) Model, the Correlation Consistent Composite Approach and Popular Density Functional Approximations on a Probabilistically Pruned Benchmark Dataset of Formation Enthalpies
• SPA${}^{\mathrm{H}}$M: the Spectrum of Approximated Hamiltonian Matrices representations
• Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery
• Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design
• Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
• Ab initio machine learning of phase space averages
• The fundamentals of quantum machine learning
• GPU-Accelerated Approximate Kernel Method for Quantum Machine Learning
• Wasserstein metric for improved QML with adjacency matrix representations
• Learning the energy curvature versus particle number in approximate density functionals
• Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
• Mean-Field Density Matrix Decompositions
• Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
• Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations
• Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
• SPA${}^{\mathrm{H}}$M(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
• 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"
• Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules
• Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
• Efficient ensemble uncertainty estimation in Gaussian Processes Regression
• Decomposed Mean-Field Simulations of Local Properties in Condensed Phases
• Molecular relaxation by reverse diffusion with time step prediction
• Molecular Hessian matrices from a machine learning random forest regression algorithm
• Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
• Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
• Machine-Learned Potentials for Solvation Modeling
• Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design
• Atomistic Global Optimization X: A Python package for optimization of atomistic structures
• Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
• Integer linear programming for unsupervised training set selection in molecular machine learning
• REANN: A PyTorch-based End-to-End Multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems
• HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks
• Compressing local atomic neighbourhood descriptors