Efficient selection of linearly independent atomic features for accurate machine learning potentials

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials

(2021) Qidong Lin et al.   Journal of Chemical Theory and Computation

Mechanical Vibrational Relaxation of NO Scattering from Metal and Insulator Surfaces: When and Why They Are Different

(2021) Rongrong Yin et al.   PHYSICAL REVIEW LETTERS

Neural Network Representations for Studying Gas‐Surface Reaction Dynamics: Beyond the Born‐Oppenheimer Static Surface Approximation †

(2021) Xueyao Zhou et al.   CHINESE JOURNAL OF CHEMISTRY

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy

(2020) Qidong Lin et al.   JOURNAL OF CHEMICAL PHYSICS

Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms

(2020) Rongjun Chen et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

(2020) V. Zaverkin et al.   Journal of Chemical Theory and Computation

Efficient Construction of Excited-State Hessian Matrices with Machine Learning Accelerated Multilayer Energy-Based Fragment Method

(2020) Wen-Kai Chen et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties

(2020) Yaolong Zhang et al.   JOURNAL OF PHYSICAL CHEMISTRY B

High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning

(2020) Bin Jiang et al.   Journal of Physical Chemistry Letters

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration

(2020) Pei-Lin Kang et al.   ACCOUNTS OF CHEMICAL RESEARCH

Recursive evaluation and iterative contraction of N-body equivariant features

(2020) Jigyasa Nigam et al.   JOURNAL OF CHEMICAL PHYSICS

Dynamics of H2O Adsorption on Pt(110)-(1 × 2) Based on a Neural Network Potential Energy Surface

(2020) Ce Hu et al.   Journal of Physical Chemistry C

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

(2020) Behnam Parsaeifard et al.   JOURNAL OF CHEMICAL PHYSICS

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

Symmetry-Adapted High Dimensional Neural Network Representation of Electronic Friction Tensor of Adsorbates on Metals

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry C

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

(2018) M. Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces

(2018) Chen Qu et al.   Journal of Chemical Theory and Computation

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

(2017) Akira Takahashi et al.   PHYSICAL REVIEW MATERIALS

Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach

(2016) Bin Jiang et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Communication: Fitting potential energy surfaces with fundamental invariant neural network

(2016) Kejie Shao et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

(2016) Brian Kolb et al.   JOURNAL OF CHEMICAL PHYSICS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Automated construction of potential energy surfaces

(2015) Moumita Majumder et al.   MOLECULAR PHYSICS

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Effects of reactant rotational excitation on reactivity: Perspectives from the sudden limit

(2014) Bin Jiang et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems

(2013) Jun Li et al.   JOURNAL OF CHEMICAL PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

CUR matrix decompositions for improved data analysis

(2009) Michael W. Mahoney et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA