Machine-learned potentials for next-generation matter simulations

New Perspectives on CO2–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface

(2020) Marcos del Cueto et al.   Journal of Physical Chemistry C

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

(2019) Christoph Bannwarth et al.   Journal of Chemical Theory and Computation

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Predicting Molecular Energy using Force-Field Optimized Geometries and Atomic Vector Representations Learned from Improved Deep Tensor Neural Network

(2019) Jianing Lu et al.   Journal of Chemical Theory and Computation

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5

(2019) Marco Eckhoff et al.   Journal of Chemical Theory and Computation

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Iterative-Learning Strategy for the Development of Application-Specific Atomistic Force Fields

(2019) Tran Doan Huan et al.   Journal of Physical Chemistry C

Atomic energy mapping of neural network potential

(2019) Dongsun Yoo et al.   Physical Review Materials

Gaussian Process-Based Refinement of Dispersion Corrections

(2019) Jonny Proppe et al.   Journal of Chemical Theory and Computation

Recent Advances and Perspectives on Nonadiabatic Mixed Quantum–Classical Dynamics

(2018) Rachel Crespo-Otero et al.   CHEMICAL REVIEWS

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

Machine learning of molecular properties: Locality and active learning

(2018) Konstantin Gubaev et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith 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

Constant size descriptors for accurate machine learning models of molecular properties

(2018) Christopher R. Collins et al.   JOURNAL OF CHEMICAL PHYSICS

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

DeePCG: Constructing coarse-grained models via deep neural networks

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions

(2018) Qinghua Liu et al.   Journal of Physical Chemistry C

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

(2018) Deping Hu et al.   Journal of Physical Chemistry Letters

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

(2018) Joseph C. R. Thacker et al.   MOLECULAR SIMULATION

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

(2018) Miguel A. Caro et al.   PHYSICAL REVIEW LETTERS

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

Local protein solvation drives direct down-conversion in phycobiliprotein PC645 via incoherent vibronic transport

(2018) Samuel M. Blau et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

(2018) Benjamin Meyer et al.   Chemical Science

Machine learning of correlated dihedral potentials for atomistic molecular force fields

(2018) Pascal Friederich et al.   Scientific Reports

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Accelerating the discovery of materials for clean energy in the era of smart automation

(2018) Daniel P. Tabor et al.   Nature Reviews Materials

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

(2018) Benjamin Nebgen et al.   Journal of Chemical Theory and Computation

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

(2018) Gregor N. Simm et al.   Journal of Chemical Theory and Computation

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

(2018) Felix C. Mocanu et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Discovering a Transferable Charge Assignment Model Using Machine Learning

(2018) Andrew E. Sifain et al.   Journal of Physical Chemistry Letters

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories

(2018) Florian Häse et al.   Chemical Science

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Phoenics: A Bayesian Optimizer for Chemistry

(2018) Florian Häse et al.   ACS Central Science

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

Adaptive coupling of a deep neural network potential to a classical force field

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Process Regression for Transition State Search

(2018) Alexander Denzel et al.   Journal of Chemical Theory and Computation

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Deep Learning for Nonadiabatic Excited-State Dynamics

(2018) Wen-Kai Chen et al.   Journal of Physical Chemistry Letters

Neural network potentials for dynamics and thermodynamics of gold nanoparticles

(2017) Siva Chiriki et al.   JOURNAL OF CHEMICAL PHYSICS

Interpolation of intermolecular potentials using Gaussian processes

(2017) Elena Uteva et al.   JOURNAL OF CHEMICAL PHYSICS

Representations in neural network based empirical potentials

(2017) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics

(2017) Raimondas Galvelis et al.   Journal of Chemical Theory and Computation

Direct Quantum Dynamics Using Grid-Based Wave Function Propagation and Machine-Learned Potential Energy Surfaces

(2017) Gareth W. Richings et al.   Journal of Chemical Theory and Computation

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

(2017) Kun Yao et al.   Journal of Physical Chemistry Letters

High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)

(2017) Brian Kolb et al.   Journal of Physical Chemistry Letters

Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)

(2017) Khosrow Shakouri et al.   Journal of Physical Chemistry Letters

Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

(2017) Vanessa Quaranta et al.   Journal of Physical Chemistry Letters

Neural network predictions of oxygen interactions on a dynamic Pd surface

(2017) Jacob R. Boes et al.   MOLECULAR SIMULATION

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

(2017) Eliodoro Chiavazzo et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

(2017) Florian Häse et al.   Chemical Science

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

(2017) Justin S. Smith et al.   Scientific Data

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling

(2016) Seong Jin An et al.   CARBON

Perspective: Quantum mechanical methods in biochemistry and biophysics

(2016) Qiang Cui   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

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Toward amino acid typing for proteins in FFLUX

(2016) Timothy L. Fletcher et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Mode specific dynamics in the H2 + SH → H + H2S reaction

(2016) Dandan Lu et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Machine learning exciton dynamics

(2016) Florian Häse et al.   Chemical Science

The ReaxFF reactive force-field: development, applications and future directions

(2016) Thomas P Senftle et al.   npj Computational Materials

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   ADVANCED FUNCTIONAL MATERIALS

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Communication: An accurate full 15 dimensional permutationally invariant potential energy surface for the OH + CH4 → H2O + CH3 reaction

(2015) Jun Li et al.   JOURNAL OF CHEMICAL PHYSICS

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm

(2015) Michael Gastegger et al.   Journal of Chemical Theory and Computation

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

Deep learning

(2015) Yann LeCun et al.   NATURE

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning

(2014) Matthew J. L. Mills et al.   Journal of Chemical Theory and Computation

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

Proposed definition of crystal substructure and substructural similarity

(2014) Lusann Yang et al.   PHYSICAL REVIEW B

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Neural networks for local structure detection in polymorphic systems

(2013) Philipp Geiger et al.   JOURNAL OF CHEMICAL PHYSICS

Communication: Non-radiative recombination via conical intersection at a semiconductor defect

(2013) Yinan Shu et al.   JOURNAL OF CHEMICAL PHYSICS

Optimizing Protein–Protein van der Waals Interactions for the AMBER ff9x/ff12 Force Field

(2013) Dail E. Chapman et al.   Journal of Chemical Theory and Computation

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

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

On representing chemical environments

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

Optimizing transition states via kernel-based machine learning

(2012) Zachary D. Pozun et al.   JOURNAL OF CHEMICAL PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Denaturant-dependent folding of GFP

(2012) G. Reddy et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?

(2011) Stefano Piana et al.   BIOPHYSICAL JOURNAL

DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)

(2011) Michael Gaus et al.   Journal of Chemical Theory and Computation

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

How Fast-Folding Proteins Fold

(2011) K. Lindorff-Larsen et al.   SCIENCE

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

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

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

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