Related references
Note: Only part of the references are listed.Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
Valentin Vassilev-Galindo et al.
JOURNAL OF CHEMICAL PHYSICS (2021)
An On-the-Fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems
Zheng Cheng et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2020)
A machine learning based intramolecular potential for a flexible organic molecule
Daniel J. Cole et al.
FARADAY DISCUSSIONS (2020)
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Huziel E. Sauceda et al.
JOURNAL OF CHEMICAL PHYSICS (2019)
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Justin S. Smith et al.
NATURE COMMUNICATIONS (2019)
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
Yaolong Zhang et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang et al.
COMPUTER PHYSICS COMMUNICATIONS (2018)
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao et al.
CHEMICAL SCIENCE (2018)
Machine learning of correlated dihedral potentials for atomistic molecular force fields
Pascal Friederich et al.
SCIENTIFIC REPORTS (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
ISA-Pol: distributed polarizabilities and dispersion models from a basis-space implementation of the iterated stockholder atoms procedure
Alston J. Misquitta et al.
THEORETICAL CHEMISTRY ACCOUNTS (2018)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
Next-Generation Force Fields from Symmetry-Adapted Perturbation Theory
Jesse G. McDaniel et al.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 67 (2016)
Beyond Born-Mayer: Improved Models for Short-Range Repulsion in ab Initio Force Fields
Mary J. Van Vleet et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Transferable Next-Generation Force Fields from Simple Liquids to Complex Materials
J. R. Schmidt et al.
ACCOUNTS OF CHEMICAL RESEARCH (2015)
CHARMM additive and polarizable force fields for biophysics and computer-aided drug design
K. Vanommeslaeghe et al.
BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS (2015)
Constructing high-dimensional neural network potentials: A tutorial review
Joerg Behler
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Improved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field
Michael J. Robertson et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)
Polarizable Atomic Multipole-Based AMOEBA Force Field for Proteins
Yue Shi et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Assessment of the Performance of MP2 and MP2 Variants for the Treatment of Noncovalent Interactions
Kevin E. Riley et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2012)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Intermolecular potentials based on symmetry-adapted perturbation theory with dispersion energies from time-dependent density-functional calculations
AJ Misquitta et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Symmetry-adapted perturbation-theory calculations of intermolecular forces employing density-functional description of monomers
AJ Misquitta et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Density-functional theory-symmetry-adapted intermolecular perturbation theory with density fitting: A new efficient method to study intermolecular interaction energies
A Hesselmann et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Share Paper
