Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

mad-GP: automatic differentiation of Gaussian processes for molecules and materials

(2022) Daniel Huang et al.   JOURNAL OF MATHEMATICAL CHEMISTRY

Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

(2021) Kim A. Nicoli et al.   PHYSICAL REVIEW LETTERS

Machine Learning Force Fields

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

Designing self-assembling kinetics with differentiable statistical physics models

(2021) Carl P. Goodrich et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine learning–accelerated computational fluid dynamics

(2021) Dmitrii Kochkov et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

(2021) John A. Keith et al.   CHEMICAL REVIEWS

Gauge Equivariant Neural Networks for Quantum Lattice Gauge Theories

(2021) Di Luo et al.   PHYSICAL REVIEW LETTERS

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

(2021) Oliver T. Unke et al.   Nature Communications

Machine Learning for Molecular Simulation

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

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Exploring chemical compound space with quantum-based machine learning

(2020) O. Anatole von Lilienfeld et al.   Nature Reviews Chemistry

Equivariant Flow-Based Sampling for Lattice Gauge Theory

(2020) Gurtej Kanwar et al.   PHYSICAL REVIEW LETTERS

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen 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

Physically informed artificial neural networks for atomistic modeling of materials

(2019) G. P. Purja Pun et al.   Nature Communications

Machine Learning for Fluid Mechanics

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

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi 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

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Kun Yao et al.   Chemical Science

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Machine learning of linear differential equations using Gaussian processes

(2017) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Variational estimation of the drift for stochastic differential equations from the empirical density

(2016) Philipp Batz et al.   JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Searching for exotic particles in high-energy physics with deep learning

(2014) P. Baldi et al.   Nature Communications

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Matérn Cross-Covariance Functions for Multivariate Random Fields

(2010) Tilmann Gneiting et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

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

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