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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

scientific article published in Physical Review Letters

Attributes	Values
rdf:type	Item
description	artículu científicu (ast) wetenschappelijk artikel (nl) наукова стаття, опублікована у квітні 2010 (uk) im April 2010 veröffentlichter wissenschaftlicher Artikel (de) article publié dans la revue scientifique Physical Review Letters (fr) scientific article published in Physical Review Letters (en)
publication date	wds:Q60451216-EC7D5870-94AC-4BB2-B86F-4810168281CC
publication date	2010-04-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q60451216-A98441B3-47B8-4DB7-82FF-75031103A657
language of work or name	English
author name string	wds:Q60451216-14C7D953-AB56-4764-BC65-BC721518008A wds:Q60451216-595AC9FF-5004-42E9-A82B-F3EC8988556B wds:Q60451216-C12B4950-5E83-4890-BC5E-E47E196C1F6A wds:Q60451216-FB213FDC-40A0-45E8-88F9-4ACB72E57E5F
author name string	Gábor Csányi Mike C Payne Albert P Bartók Risi Kondor
rdfs:label	Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (en) Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (nl)
skos:prefLabel	Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (en) Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (nl)
name	Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (en) Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (nl)
title	wds:Q60451216-F86A1B36-C8E9-4C7E-95DD-3FA0DB4680CD
title	Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons (en)
page(s)	wds:Q60451216-7832FAC0-717A-40CC-8705-BE1E4254C44E
page(s)	136403
instance of	wds:Q60451216-119F19BB-27D0-4AC9-8D6B-B9EE695EA9D1
instance of	scholarly article
main subject	wds:Q60451216-7A384FD1-073D-4B8A-9E32-32C0990F01A2
main subject	quantum mechanics
PubMed ID	wds:Q60451216-66B7D761-6291-41A6-A837-CA28CEB39182
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/20481899
PubMed ID	20481899
published in	wds:Q60451216-8E845F50-3768-4A40-BEF9-59A52C6CE96E
published in	Physical Review Letters
issue	wds:Q60451216-CB20261F-C7AE-476C-A06F-837E7D70A3B4
volume	wds:Q60451216-C24A3BE5-8226-4537-A208-70F6C1352A0D
issue	13
volume	104
DOI	wds:Q60451216-5607A754-6149-4B59-B47A-A2F86EE64F01
DOI	http://dx.doi.org/10.1103/PHYSREVLETT.104.136403
DOI	10.1103/PHYSREVLETT.104.136403
arXiv ID	wds:Q60451216-05E0D5E9-8CF4-4A7D-AF02-56F46A29E173
arXiv ID	0910.1019
ADS bibcode	wds:Q60451216-D70102B2-C1BC-40FC-98D7-087172716D52
ADS bibcode	2010PhRvL.104m6403B
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q60451216
is cites work of	Nucleation mechanism for the direct graphite-to-diamond phase transition Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Machine learning estimates of natural product conformational energies Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster. Quantum-chemical insights from deep tensor neural networks Machine learning of accurate energy-conserving molecular force fields. Biomolecular Force Field Parameterization via Atoms-in-Molecule Electron Density Partitioning Machine learning unifies the modeling of materials and molecules. A general-purpose machine learning framework for predicting properties of inorganic materials Bypassing the Kohn-Sham equations with machine learning Towards exact molecular dynamics simulations with machine-learned force fields Physically informed artificial neural networks for atomistic modeling of materials Machine learning for molecular and materials science The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Machine learning enables long time scale molecular photodynamics simulations The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules Can we really do computer-aided drug design? Transferable Machine-Learning Model of the Electron Density wd:Q108527464 Geometry Optimization with Machine Trained Topological Atoms Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry Unfavorable regions in the ramachandran plot: Is it really steric hindrance? The interacting quantum atoms perspective. Neural networks-based variationally enhanced sampling Reliable and explainable machine-learning methods for accelerated material discovery Multipolar electrostatics based on the Kriging machine learning method: an application to serine Machine-learned approximations to Density Functional Theory Hamiltonians Energy-free machine learning force field for aluminum. Towards an atomistic understanding of disordered carbon electrode materials. Machine-learned multi-system surrogate models for materials prediction Machine learning reveals orbital interaction in materials. Recent advances and applications of machine learning in solid-state materials science Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Machine learning for chemical discovery Molecular Dynamics Simulations of Large Systems in Electronic Excited States Learning grain boundary segregation energy spectra in polycrystals Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning. Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon A general-purpose machine-learning force field for bulk and nanostructured phosphorus Extracting Crystal Chemistry from Amorphous Carbon Structures De novo exploration and self-guided learning of potential-energy surfaces Chemiscope: interactive structure-property explorer for materials and molecules A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes' reactivity. Force Field for Water Based on Neural Network A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data. Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application.

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 194 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software