Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

(2020) Cheol Woo Park et al.   Physical Review Materials

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

(2019) Tian Xie et al.   Nature Communications

SchNet – A deep learning architecture for molecules and materials

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

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Review of force fields and intermolecular potentials used in atomistic computational materials research

(2018) Judith A. Harrison et al.   Applied Physics Reviews

Super-Ionic Conduction in Solid-State Li7P3S11-Type Sulfide Electrolytes

(2018) Donghee Chang et al.   CHEMISTRY OF MATERIALS

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Insights into the Performance Limits of the Li7P3S11 Superionic Conductor: A Combined First-Principles and Experimental Study

(2016) Iek-Heng Chu et al.   ACS Applied Materials & Interfaces

Neural network and ReaxFF comparison for Au properties

(2016) Jacob R. Boes et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Automated Discovery and Refinement of Reactive Molecular Dynamics Pathways

(2016) Lee-Ping Wang et al.   Journal of Chemical Theory and Computation

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

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

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

Discovering chemistry with an ab initio nanoreactor

(2014) Lee-Ping Wang et al.   Nature Chemistry

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys

(2012) B. Jelinek et al.   PHYSICAL REVIEW B

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Molecular dynamics simulation of O2 sticking on Pt(111) using the ab initio based ReaxFF reactive force field

(2010) Paolo Valentini et al.   JOURNAL OF CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

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