The ability to perform accurate calculations efficiently is crucial for computational materials design. In this talk, we will discuss our stream-lined approach to force field development using first principles density functional theory training data and machine learning algorithms. Our objective has been to develop new, first-principles based, more accurate and more robust inter-atomic potentials for accurate simulations of dynamical processes at reactive interfaces and low dimensional systems such as clusters and molecules. The procedure involves several steps including (a) generation and manipulation of extensive fitting data sets through electronic structure calculations, (b) defining functional forms, (c) formulating novel highly optimized fitting procedures, (d) dual-Hamiltonian optimization to leverage classical FFs with more accurate approaches, and (d) subsequently coding and implementing these algorithms on high performance computers (HPCs). We will also discuss the validation of this approach on several diverse material systems ranging precious metal nanocatalysts to newly discovered two dimensional materials such as stanene and silicene. Machine learning algorithms to perform structural optimization and phase diagram predictions using the developed inter-atomic potentials will also be discussed.

Pushing the frontiers of materials modeling and discovery with machine learning

256th National Meeting (2018)

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