Machine Learning Force Fields: Recent Advances and Remaining Challenges

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

(2021) Huziel E. Sauceda et al.   Nature Communications

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

(2021) Tsz Wai Ko et al.   Nature Communications

Machine Learning Force Fields

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

Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory

(2021) Apurba Nandi et al.   JOURNAL OF CHEMICAL PHYSICS

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

(2021) Valentin Vassilev-Galindo et al.   JOURNAL OF CHEMICAL PHYSICS

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

(2021) Gregory Fonseca et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

(2020) Yongqiang Kang et al.   Journal of Energy Chemistry

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks

(2020) Martin Stöhr et al.   Journal of Physical Chemistry Letters

Deep-neural-network solution of the electronic Schrödinger equation

(2020) Jan Hermann et al.   Nature Chemistry

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

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

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst

(2019) Eric Walker et al.   Journal of Chemical Information and Modeling

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

(2019) Frank Noé et al.   SCIENCE

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

Grandmaster level in StarCraft II using multi-agent reinforcement learning

(2019) Oriol Vinyals et al.   NATURE

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

(2019) Wenbo Sun et al.   Science Advances

Quantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions

(2019) Martin Stöhr et al.   Science Advances

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

(2018) Jake Graser et al.   CHEMISTRY OF MATERIALS

Predicting the thermodynamic stability of perovskite oxides using machine learning models

(2018) Wei Li et al.   COMPUTATIONAL MATERIALS SCIENCE

Metadynamics for training neural network model chemistries: A competitive assessment

(2018) John E. Herr et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal Structure Prediction via Deep Learning

(2018) Kevin Ryan et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

(2018) Prasanna V. Balachandran et al.   Nature Communications

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

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

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Deep Audio-Visual Speech Recognition

(2018) Triantafyllos Afouras et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

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

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

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

Software update: the ORCA program system, version 4.0

(2017) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Electrostatic phenomena in organic semiconductors: fundamentals and implications for photovoltaics

(2016) Gabriele D’Avino et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Wavelike charge density fluctuations and van der Waals interactions at the nanoscale

(2016) Alberto Ambrosetti et al.   SCIENCE

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Adaptive machine learning framework to accelerateab initiomolecular dynamics

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

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

(2013) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

Accurate and Efficient Method for Many-Body van der Waals Interactions

(2012) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

How Fast-Folding Proteins Fold

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

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

(2010) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Long range interactions in nanoscale science

(2010) Roger H. French et al.   REVIEWS OF MODERN PHYSICS

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data

(2009) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS