Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry

Abstract

A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.

arXiv commentGuest editorial for the Journal of Chemical Physics Special Topic on Data-enabled Theoretical Chemistry

References in corpus (23)

• SchNet - a deep learning architecture for molecules and materials
• Kernel methods in machine learning
• Less is more: sampling chemical space with active learning
• Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
• Alchemical and structural distribution based representation for improved QML
• Hierarchical modeling of molecular energies using a deep neural network
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Sparse learning of stochastic dynamic equations
• Extending the Accuracy of the SNAP Interatomic Potential Form
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• Machine learning of molecular properties: locality and active learning
• Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
• Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
• Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
• Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
• Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
• Building machine learning force fields for nanoclusters
• Compositional descriptor-based recommender system accelerating the materials discovery
• Ground-State Energy as a Simple Sum of Orbital Energies in Kohn-Sham Theory: A Shift in Perspective through a Shift in Potential
• Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors
• Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation

Cited by in corpus (23)

• Machine learning and the physical sciences
• A Transferable Machine-Learning Model of the Electron Density
• Machine Learning the Voltage of Electrode Materials in Metal-ion Batteries
• Ab initio vibrational free energies including anharmonicity for multicomponent alloys
• Chemical diversity in molecular orbital energy predictions with kernel ridge regression
• Data-driven Material Models for Atomistic Simulation
• Equation of state of fluid methane from first principles with machine learning potentials
• IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations
• A Perspective on Deep Learning for Molecular Modeling and Simulations
• Machine learning the computational cost of quantum chemistry
• Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
• Predicting Gas-Particle Partitioning Coefficients of Atmospheric Molecules with Machine Learning
• Optimized Multifidelity Machine Learning for Quantum Chemistry
• Wasserstein metric for improved QML with adjacency matrix representations
• Mean-Field Density Matrix Decompositions
• Accurate Prediction of Bonding Properties by A Machine Learning-based Model using Isolated States Before Bonding
• Computational design of metal-supported molecular switches: Transient ion formation during light- and electron-induced isomerisation of azobenzene
• Predicting trajectory behaviour via machine-learned invariant manifolds
• Physical machine learning outperforms "human learning" in Quantum Chemistry
• Ab initio machine learning in chemical compound space
• Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric top molecules
• Multiscale Approach to Fluid-Solid Interfaces: An overview of methodologies coupling fluid mechanics to molecular dynamics and quantum theory
• A Kernel-based Machine Learning Approach to Computing Quasiparticle Energies within Many-Body Green's Functions Theory