Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning

Origins of structural and electronic transitions in disordered silicon

(2021) Volker L. Deringer et al.   NATURE

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

(2021) Ziteng Liu et al.   Journal of Chemical Information and Modeling

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

(2021) Manyi Yang et al.   CATALYSIS TODAY

Phase Diagram of a Deep Potential Water Model

(2021) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

Accurate and Efficient Prediction of NMR Parameters of Condensed-Phase Systems with the Generalized Energy-Based Fragmentation Method

(2020) Dongbo Zhao et al.   Journal of Chemical Theory and Computation

An On-the-fly Approach to Construct Generalized Energy‒Based Fragmentation Machine Learning Force Fields of Complex Systems

(2020) Zheng Cheng et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

(2020) Zhilong Wang et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

(2020) Julia Westermayr et al.   Journal of Physical Chemistry Letters

Ab initio phase diagram and nucleation of gallium

(2020) Haiyang Niu et al.   Nature Communications

Assessing conformer energies using electronic structure and machine learning methods

(2020) Dakota Folmsbee et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

(2020) Xiang Gao et al.   Journal of Chemical Information and Modeling

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration

(2020) Pei-Lin Kang et al.   ACCOUNTS OF CHEMICAL RESEARCH

Structures and Spectroscopic Properties of Large Molecules and Condensed-Phase Systems Predicted by Generalized Energy-Based Fragmentation Approach

(2020) Wei Li et al.   ACCOUNTS OF CHEMICAL RESEARCH

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

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

Integrating Machine Learning with the Multilayer Energy-Based Fragment Method for Excited States of Large Systems

(2019) Wen-Kai Chen et al.   Journal of Physical Chemistry Letters

New developments in force fields for biomolecular simulations

(2018) Paul S Nerenberg et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Force Field for Water Based on Neural Network

(2018) Hao Wang et al.   Journal of Physical Chemistry Letters

Accurate prediction of the structure and vibrational spectra of ionic liquid clusters with the generalized energy-based fragmentation approach: critical role of ion-pair-based fragmentation

(2018) Yunzhi Li et al.   PHYSICAL CHEMISTRY 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

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

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

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network

(2018) Hao Wang et al.   Journal of Chemical Theory and Computation

Accurate Prediction of NMR Chemical Shifts in Macromolecular and Condensed-Phase Systems with the Generalized Energy-Based Fragmentation Method

(2017) Dongbo Zhao et al.   Journal of Chemical Theory and Computation

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes

(2016) Michael Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

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

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

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

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

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

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

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

The Combined Fragmentation and Systematic Molecular Fragmentation Methods

(2014) Michael A. Collins et al.   ACCOUNTS OF CHEMICAL RESEARCH

LSQC: Low scaling quantum chemistry program

(2014) Wei Li et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Folding Simulations for Proteins with Diverse Topologies Are Accessible in Days with a Physics-Based Force Field and Implicit Solvent

(2014) Hai Nguyen et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Polarizable Atomic Multipole-Based AMOEBA Force Field for Proteins

(2013) Yue Shi et al.   Journal of Chemical Theory and Computation

Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps Method for Full Quantum Mechanical Calculation of Protein Energy

(2013) Xianwei Wang et al.   JOURNAL OF PHYSICAL CHEMISTRY A

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

A generalized many-body expansion and a unified view of fragment-based methods in electronic structure theory

(2012) Ryan M. Richard et al.   JOURNAL OF CHEMICAL PHYSICS

Many-Overlapping-Body (MOB) Expansion: A Generalized Many Body Expansion for Nondisjoint Monomers in Molecular Fragmentation Calculations of Covalent Molecules

(2012) Nicholas J. Mayhall et al.   Journal of Chemical Theory and Computation

Comment on “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”

(2012) Jonathan E. Moussa   PHYSICAL REVIEW LETTERS

Systematic Validation of Protein Force Fields against Experimental Data

(2012) Kresten Lindorff-Larsen et al.   PLoS One

Fragmentation Methods: A Route to Accurate Calculations on Large Systems

(2011) Mark S. Gordon et al.   CHEMICAL REVIEWS

GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories

(2011) Andreas P. Eichenberger et al.   Journal of Chemical Theory and Computation

Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models

(2010) Pär Bjelkmar et al.   Journal of Chemical Theory and Computation

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

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

Are Current Molecular Dynamics Force Fields too Helical?

(2008) Robert B. Best et al.   BIOPHYSICAL JOURNAL