AI3SD Winter Seminar Series #7: Materials Machine Learning (MML): Talk 1 - Dr Reinhard Maurer

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This video forms part of the AI3SD Winter Seminar Series 20/2021.

This video is the first talk in the seventh seminar of the series: Materials Machine Learning (MML).

Unifying Machine Learning and Quantum Chemistry: From Deep Learning of Wave Functions to ML/QM-hybrid methods – Dr Reinhard Maurer

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).

Bio: 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.

This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1
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