Detailed Information

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

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Authors
Hansen, KatjaBiegler, FranziskaRamakrishnan, RaghunathanPronobis, Wiktorvon Lilienfeld, O. AnatoleMueller, Klaus-RobertTkatchenko, Alexandre
Issue Date
18-6월-2015
Publisher
AMER CHEMICAL SOC
Keywords
atomization energies; chemical compound space; machine learning; many-body potentials; molecular properties
Citation
JOURNAL OF PHYSICAL CHEMISTRY LETTERS, v.6, no.12, pp.2326 - 2331
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume
6
Number
12
Start Page
2326
End Page
2331
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/93245
DOI
10.1021/acs.jpclett.5b00831
ISSN
1948-7185
Abstract
Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the "holy grail" of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.
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