Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning
出版年份 2021 全文链接
标题
Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning
作者
关键词
-
出版物
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
Volume 24, Issue 3, Pages 1326-1337
出版商
Royal Society of Chemistry (RSC)
发表日期
2021-10-25
DOI
10.1039/d1cp03934b
参考文献
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- (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
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