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Neural network models and concurrent learning schemes for multi-scale molecular modelling

Duke Math
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Tuesday, February 25, 2020
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3:15 pm - 4:15 pm
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Linfeng Zhang (Princeton University)
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Applied Math And Analysis Seminar

We will discuss two issues in the context of applying deep learning methods to multi-scale molecular modelling: 1) how to construct symmetry-preserving neural network models for scalar and tensorial quantities; 2) how to efficiently explore the relevant configuration space and generate a minimal set of training data. We show that by properly addressing these two issues, one can systematically develop deep learning-based models for electronic properties and interatomic and coarse-grained potentials, which greatly boost the ability of ab-initio molecular dynamics; one can also develop enhanced sampling techniques that are capable of using tens or even hundreds of collective variables to drive phase transition and accelerate structure search.