Artikel ; Online: Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery.
Chemistry (Weinheim an der Bergstrasse, Germany)
2023 Band 29, Heft 60, Seite(n) e202301957
Abstract: Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials ... ...
Abstract | Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field. |
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Sprache | Englisch |
Erscheinungsdatum | 2023-09-14 |
Erscheinungsland | Germany |
Dokumenttyp | Journal Article |
ZDB-ID | 1478547-X |
ISSN | 1521-3765 ; 0947-6539 |
ISSN (online) | 1521-3765 |
ISSN | 0947-6539 |
DOI | 10.1002/chem.202301957 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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