Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

SchNet - A deep learning architecture for molecules and materials

Authors
Schuett, K. T.Sauceda, H. E.Kindermans, P. -J.Tkatchenko, A.Mueller, K. -R.
Issue Date
28-6월-2018
Publisher
AMER INST PHYSICS
Citation
JOURNAL OF CHEMICAL PHYSICS, v.148, no.24
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF CHEMICAL PHYSICS
Volume
148
Number
24
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/74886
DOI
10.1063/1.5019779
ISSN
0021-9606
Abstract
Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C-20-fullerene that would have been infeasible with regular ab initio molecular dynamics. Published by AIP Publishing.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE