Machine learning of accurate energy-conserving molecular force fields.
about
Bypassing the Kohn-Sham equations with machine learning.Machine learning molecular dynamics for the simulation of infrared spectra.Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules.An atomistic fingerprint algorithm for learning ab initio molecular force fields.Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.Learning a Local-Variable Model of Aromatic and Conjugated Systems.Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials.The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.Towards exact molecular dynamics simulations with machine-learned force fields
P2860
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P2860
Machine learning of accurate energy-conserving molecular force fields.
description
2017 nî lūn-bûn
@nan
2017 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2017 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2017年の論文
@ja
2017年論文
@yue
2017年論文
@zh-hant
2017年論文
@zh-hk
2017年論文
@zh-mo
2017年論文
@zh-tw
2017年论文
@wuu
name
Machine learning of accurate energy-conserving molecular force fields.
@ast
Machine learning of accurate energy-conserving molecular force fields.
@en
type
label
Machine learning of accurate energy-conserving molecular force fields.
@ast
Machine learning of accurate energy-conserving molecular force fields.
@en
prefLabel
Machine learning of accurate energy-conserving molecular force fields.
@ast
Machine learning of accurate energy-conserving molecular force fields.
@en
P2093
P2860
P50
P356
P1433
P1476
Machine learning of accurate energy-conserving molecular force fields
@en
P2093
Igor Poltavsky
Klaus-Robert Müller
Stefan Chmiela
P2860
P304
P356
10.1126/SCIADV.1603015
P577
2017-05-05T00:00:00Z
P698
P818
1611.04678