Accurate global machine learning force fields for molecules with hundreds of atoms

Colossal Enhancement of Atomic Force Response in van der Waals Materials Arising from Many-Body Electronic Correlations

(2022) Paul Hauseux et al.   PHYSICAL REVIEW LETTERS

Self-consistent determination of long-range electrostatics in neural network potentials

(2022) Ang Gao et al.   Nature Communications

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

(2022) Simon Batzner et al.   Nature Communications

BIGDML—Towards accurate quantum machine learning force fields for materials

(2022) Huziel E. Sauceda et al.   Nature Communications

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

(2022) Niklas Frederik Schmitz et al.   Journal of Physical Chemistry Letters

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

Understanding deep learning (still) requires rethinking generalization

(2021) Chiyuan Zhang et al.   COMMUNICATIONS OF THE ACM

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

(2021) Johannes Hoja et al.   Scientific Data

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

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

Learning intermolecular forces at liquid–vapor interfaces

(2021) Samuel P. Niblett et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

FCHL revisited: Faster and more accurate quantum machine learning

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

From quantum to continuum mechanics in the delamination of atomically-thin layers from substrates

(2020) Paul Hauseux et al.   Nature Communications

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

(2020) M. Gastegger 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

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

(2020) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

(2020) Bing Huang et al.   Nature Chemistry

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

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

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

Incorporating long-range physics in atomic-scale machine learning

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

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

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

(2019) Martin Stöhr et al.   Science Advances

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

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

(2018) Kun Yao et al.   Chemical Science

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

i-PI 2.0: A universal force engine for advanced molecular simulations

(2018) Venkat Kapil et al.   COMPUTER PHYSICS COMMUNICATIONS

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

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

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

(2016) Alberto Ambrosetti et al.   SCIENCE

Role of Dispersion Interactions in the Polymorphism and Entropic Stabilization of the Aspirin Crystal

(2014) Anthony M. Reilly et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2012) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS

Relative-Error $CUR$ Matrix Decompositions

(2008) Petros Drineas et al.   SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS