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

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

• Category: Applied Math and Analysis
• Duration: 01:34:45
• Date:  February 25, 2020 at 3:10 PM
• Tags:   seminar, Applied Math And Analysis Seminar