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AI3SD Video: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery

AI3SD Video: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery
AI3SD Video: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery
Atomistic simulation based on quantum mechanics (QM) is currently being revolutionized by machine-learning (ML) methods. Many existing approaches use ML to predict molecular properties from quantum chemical calculations. This has enabled molecular property prediction within vast chemical compound spaces and the high-dimensional parametrization of energy landscapes for the efficient molecular simulation of measurable observables. However, as all properties derive from the QM wave function, an ML model that is able to predict the wave function also has the potential to predict all other molecular properties. In this talk, I will explore ML approaches that directly represent wave functions and QM Hamiltonians and their derivatives for developing methods that use ML and QM in synergy. [1] Using examples from molecular dynamics [1] and heterogeneous catalysis, [2,3] I will discuss the challenges associated with encoding physical symmetries and invariance properties into deep learning models. Upon overcoming these challenges, integrated ML-QM methods offer the combined benefits of big-data-driven parametrization and first-principles-based methods. I will discuss several opportunities associated with building ML-augmented quantum chemical methods, including Inverse Chemical Design based on ML-predicted wave functions and the development of efficient and accurate semi-empirical methods to study hybrid metal-organic materials. [4] [1] KT Schütt, M Gastegger, A Tkatchenko, K-R Müller & RJ Maurer, Nature Communications 10, 5024 (2019). [2] Y Zhang, RJ Maurer, and B Jiang, J. Phys. Chem. C 124, 186-195 (2020); [3] Y Zhang, RJ Maurer, H Guo, and B Jiang, Chem. Sci. 10, 1089-1097 (2019). [4] M Gastegger, A McSloy, M Luya, KT Schütt, RJ Maurer, J. Chem. Phys. 153, 044123 (2020).
AI, AI3SD Event, Artificial Intelligence, Chemical Discovery, Chemistry, Machine Intelligence, Machine Learning, Materials Discovery, ML, Molecules Discovery, Quantum Chemistry
Maurer, Reinhard J.
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Kanza, Samantha
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Frey, Jeremy G.
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Niranjan, Mahesan
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Hooper, Victoria
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Maurer, Reinhard J.
cad1a0c1-cfa8-4a5d-8b52-5bee7e761f3f
Kanza, Samantha
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Frey, Jeremy G.
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Niranjan, Mahesan
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Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Maurer, Reinhard J. (2021) AI3SD Video: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0075).

Record type: Conference or Workshop Item (Other)

Abstract

Atomistic simulation based on quantum mechanics (QM) is currently being revolutionized by machine-learning (ML) methods. Many existing approaches use ML to predict molecular properties from quantum chemical calculations. This has enabled molecular property prediction within vast chemical compound spaces and the high-dimensional parametrization of energy landscapes for the efficient molecular simulation of measurable observables. However, as all properties derive from the QM wave function, an ML model that is able to predict the wave function also has the potential to predict all other molecular properties. In this talk, I will explore ML approaches that directly represent wave functions and QM Hamiltonians and their derivatives for developing methods that use ML and QM in synergy. [1] Using examples from molecular dynamics [1] and heterogeneous catalysis, [2,3] I will discuss the challenges associated with encoding physical symmetries and invariance properties into deep learning models. Upon overcoming these challenges, integrated ML-QM methods offer the combined benefits of big-data-driven parametrization and first-principles-based methods. I will discuss several opportunities associated with building ML-augmented quantum chemical methods, including Inverse Chemical Design based on ML-predicted wave functions and the development of efficient and accurate semi-empirical methods to study hybrid metal-organic materials. [4] [1] KT Schütt, M Gastegger, A Tkatchenko, K-R Müller & RJ Maurer, Nature Communications 10, 5024 (2019). [2] Y Zhang, RJ Maurer, and B Jiang, J. Phys. Chem. C 124, 186-195 (2020); [3] Y Zhang, RJ Maurer, H Guo, and B Jiang, Chem. Sci. 10, 1089-1097 (2019). [4] M Gastegger, A McSloy, M Luya, KT Schütt, RJ Maurer, J. Chem. Phys. 153, 044123 (2020).

Video
AI3SD-Winter-Seminar-Series-MML-ReinhardMaurer - Version of Record
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More information

Published date: 24 February 2021
Additional Information: Reinhards research focuses on the theory and simulation of molecular reactions on surfaces and in materials. Reinhard studies the structure, composition, and reactivity of molecules interacting with solid surfaces. Reinhards goal is to find a detailed understanding of the explicit molecular-level dynamics of molecular reactions as they appear in catalysis, photochemistry, and nanotechnology. Members of Reinhards research group develop and use electronic structure theory, quantum chemistry, molecular dynamics, and machine learning methods to achieve this.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemical Discovery, Chemistry, Machine Intelligence, Machine Learning, Materials Discovery, ML, Molecules Discovery, Quantum Chemistry

Identifiers

Local EPrints ID: 448773
URI: http://eprints.soton.ac.uk/id/eprint/448773
PURE UUID: 08b1614f-3438-4eb6-b630-5c40223ef4ee
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: 05 May 2021 16:33
Last modified: 22 Oct 2021 01:55

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Contributors

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

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