When do short-range atomistic machine-learning models fall short?

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

(2020) Kirill Shmilovich et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

(2020) Hythem Sidky et al.   MOLECULAR PHYSICS

An accurate and transferable machine learning potential for carbon

(2020) Patrick Rowe et al.   JOURNAL OF CHEMICAL PHYSICS

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water

(2020) Thomas E. Gartner et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Ab initio thermodynamics of liquid and solid water

(2019) Bingqing Cheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine learning coarse grained models for water

(2019) Henry Chan et al.   Nature Communications

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Teaching an Old Dog New Tricks: Machine Learning an Improved TIP3P Potential Model for Liquid-Vapor Phase Phenomena

(2019) Troy David Loeffler et al.   Journal of Physical Chemistry C

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

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

An electrostatic spectral neighbor analysis potential for lithium nitride

(2019) Zhi Deng et al.   npj Computational Materials

Machine Learning for Vibrational Spectroscopic Maps

(2019) Alexei A. Kananenka et al.   Journal of Chemical Theory and Computation

Isotope effects in liquid water via deep potential molecular dynamics

(2019) Hsin-Yu Ko et al.   MOLECULAR PHYSICS

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

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

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

Focus Article: Oscillatory and long-range monotonic exponential decays of electrostatic interactions in ionic liquids and other electrolytes: The significance of dielectric permittivity and renormalized charges

(2018) Roland Kjellander   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

(2018) So Fujikake et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

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

(2018) Kun Yao et al.   Chemical Science

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

A universal strategy for the creation of machine learning-based atomistic force fields

(2017) Tran Doan Huan et al.   npj Computational Materials

Hydrogen-Bonding Polarizable Intermolecular Potential Model for Water

(2016) Hao Jiang et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments

(2016) Bingqing Cheng et al.   Journal of Physical Chemistry Letters

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

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

Structure, Dynamics, and Spectral Diffusion of Water from First-Principles Molecular Dynamics

(2014) Arindam Bankura et al.   Journal of Physical Chemistry C

Polarizable model of water with field-dependent polarization

(2011) András Baranyai et al.   JOURNAL OF CHEMICAL PHYSICS

Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

(2011) Roman M. Balabin et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Water Simulation Model with Explicit Three-Molecule Interactions

(2008) R. Kumar et al.   JOURNAL OF PHYSICAL CHEMISTRY B