Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-12584
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Main Title: Combining machine learning and computational chemistry for predictive insights into chemical systems
Author(s): Keith, John A.
Vassilev-Galindo, Valentin
Cheng, Bingqing
Chmiela, Stefan
Gastegger, Michael
Müller, Klaus-Robert
Tkatchenko, Alexandre
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/13808
http://dx.doi.org/10.14279/depositonce-12584
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
Subject(s): electrical energy
quantum mechanics
potential energy
materials
molecules
Issue Date: 7-Jul-2021
Date Available: 4-Nov-2021
Language Code: en
DDC Class: 540 Chemie und zugeordnete Wissenschaften
Sponsor/Funder: BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data
BMBF, 01GQ1115, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum
BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data
BMBF, 031L0207D, CompLS - Runde 2 - Verbundprojekt: Patho234 - Multidimensionale Bildanalyse von reaktiven und neoplastischen Lymphknoten durch maschinelles Lernen - Teilprojekt D
BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
DFG, 390685689, EXC 2046: MATH+: Berlin Mathematics Research Center
Journal Title: Chemical Reviews
Publisher: American Chemical Society (ACS)
Volume: 121
Issue: 16
Publisher DOI: 10.1021/acs.chemrev.1c00107
Page Start: 9816
Page End: 9872
EISSN: 1520-6890
ISSN: 0009-2665
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Maschinelles Lernen
Appears in Collections:Technische Universität Berlin » Publications

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