【学术视频】机器学习-计算化学Workshop | Bastiaan J. Braams

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图 | Bastiaan J. Braams

题   目：Machine learning of equivariant functions inspired by atomistic modelling and three-dimensional image processing

报告人：Bastiaan J. Braams

单   位：Centrum Wiskunde & Informatica, Dalian Institute of Chemical Physics

时   间：2019-09-06

地   点：厦门大学化学化工学院

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报告摘要

In my talk I noted several works that use (finally) linear regression: the Gaussian Approximation Potential (Gábor Csányi et al.), Solid Harmonic Wavelet Scattering (Stéphane Mallat et al.) and the Atomic Cluster Expansion (Ralf Drautz). I also described recent work employing deep neural networks and there I emphasized two key aspects of the some of the recent work: the networks are structured and parameterized.

Structured: Relative to a standard feed-forward neural network, in these networks each "neuron" holds a structured value and the layer-to-layer connections are compatible with that structure. The concept is familiar from machine vision where the structured value is a two-dimensional array of real values (a pixel image in the input layer) and the compatible operation is a convolution with compact kernel. In our applications the structured value can be a feature vector (in the SchNet approach from Berlin) or also an object that has welldefined transformation properties under rotations of 3D space (in the equivariant neural networks from the Amsterdam group, the Tensor Field networks from the Google people and the more recent DeepPot work from Princeton).

个人简介

Bastiaan braams is an affiliated researcher in the Multiscale Dynamics group in DICP (December 2016 - present). Previously employed at International Atomic Energy Agency (IAEA), Vienna, Austria (2009-2016), Emory University, Atlanta, GA (2003-2009), New York University, New York City, NY (1989-2003), Princeton Plasma Physics Laboratory, Princeton, NJ (1986-1989) and on PhD research at FOM Institute for Plasma Physics (now DIFFER), Nieuwegein, Netherlands. The PhD work was done at Max Planck Institute for Plasma Physics, Garching, Germany and at UKAEA Culham Laboratory. Basic university education in Utrecht (theoretical physics) and Eindhoven (mathematics and computing science). Interested in scientific computing and data analysis with applications in plasma physics, molecular modelling, and atomic physics. His has a broad view of research interest, including fusion energy research, quantum chemistry and molecular modelling. 

会议简介

2019年9月3日-6日，由固体表面物理化学国家重点实验室（厦门大学）、福建省理论与计算化学重点实验室和厦门大学化学化工学院主办的“机器学习-计算化学Workshop”在厦门大学化学化工学院举办。本次Workshop邀请了相关领域的研究者报告领域前沿进展，并设置Hands-on tutorials环节帮助学员们熟悉代码的使用。此次Workshop的举办增进了不同领域研究者的交流，促进了开源共享的观念传递，希望推动大数据技术在计算化学和材料模拟等领域的应用。

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