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AI3SD Project: Deep-Learning-Enhanced Quantum Chemistry: Pushing the Limits of Materials Discovery

AI3SD Project: Deep-Learning-Enhanced Quantum Chemistry: Pushing the Limits of Materials Discovery
AI3SD Project: Deep-Learning-Enhanced Quantum Chemistry: Pushing the Limits of Materials Discovery
Discovery of new functional materials is central to achieving radical advances in societally important challenges (efficient energy materials, organic solar cells, etc). The vast space of possible materials combinations and compositions gives hope that useful materials exist, but finding ways to navigate this vast space remains a fundamental challenge to materials research. Quantum theoretical computational materials research based on density functional theory has revolutionised the search for new materials over the last twenty years with its ability to predict materials properties based on atomic structure. However, the computational cost of solving quantum mechanical equations at high-performance computing centres remains a severe bottle-neck to its commonplace use in research. In this project, we use machine learning to develop an accurate quantum mechanical simulation method of structural, optical, and electronic properties of hybrid organic-metallic materials used in modern solar cells that is efficient enough to run on standard desktop computers for systems that include many thousands of atoms.
AI3SD, Funded Project, Quantum Chemistry, Deep Learning Enhanced
2
University of Southampton
McSloy, Adam
77500573-bb00-4879-9e03-1e662abff4bd
Maurer, Reinhard J
cad1a0c1-cfa8-4a5d-8b52-5bee7e761f3f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
McSloy, Adam
77500573-bb00-4879-9e03-1e662abff4bd
Maurer, Reinhard J
cad1a0c1-cfa8-4a5d-8b52-5bee7e761f3f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

McSloy, Adam and Maurer, Reinhard J , Kanza, Samantha, Frey, Jeremy G. and Hooper, Victoria (eds.) (2020) AI3SD Project: Deep-Learning-Enhanced Quantum Chemistry: Pushing the Limits of Materials Discovery (AI3SD-Project-Series, 2) University of Southampton 17pp. (doi:10.5258/SOTON/P0041).

Record type: Monograph (Project Report)

Abstract

Discovery of new functional materials is central to achieving radical advances in societally important challenges (efficient energy materials, organic solar cells, etc). The vast space of possible materials combinations and compositions gives hope that useful materials exist, but finding ways to navigate this vast space remains a fundamental challenge to materials research. Quantum theoretical computational materials research based on density functional theory has revolutionised the search for new materials over the last twenty years with its ability to predict materials properties based on atomic structure. However, the computational cost of solving quantum mechanical equations at high-performance computing centres remains a severe bottle-neck to its commonplace use in research. In this project, we use machine learning to develop an accurate quantum mechanical simulation method of structural, optical, and electronic properties of hybrid organic-metallic materials used in modern solar cells that is efficient enough to run on standard desktop computers for systems that include many thousands of atoms.

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More information

Published date: 4 February 2020
Keywords: AI3SD, Funded Project, Quantum Chemistry, Deep Learning Enhanced

Identifiers

Local EPrints ID: 446273
URI: http://eprints.soton.ac.uk/id/eprint/446273
PURE UUID: 9e761d1a-77d7-47ec-a7bb-e9e34b184e45
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 03 Feb 2021 17:33
Last modified: 22 Oct 2021 01:55

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Contributors

Author: Adam McSloy
Author: Reinhard J Maurer
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Victoria Hooper

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