(51c) Computational Materials Design with Machine Learning and Atomistic Simulations

Designing new materials is vital for addressing pressing societal challenges in health, energy, and sustainability. The computational techniques of atomistic simulation and machine learning (ML) offer an avenue to rapidly invent new materials and navigate this enormous space. By populating the continuum between physics-based simulations and machine learning, it is possible to enable rapid, computation-first design of materials that accelerate the materials discovery cycle.

Atomistic simulations, using techniques from quantum mechanics or statistical mechanics can predict the properties of hypothetical materials, and by engineering high-throughput simulation pipelines, millions of candidates can be evaluated to identify compositions or structures that optimize a given property. Simulations are, nevertheless, relatively costly, and may lack accuracy compared to experiment. This is where the synergy with ML enables a new paradigm: surrogate models bypass simulations by interpolating among pre-existing calculations at a fraction of the cost, while embedding physics-based priors in ML ensures robustness and transferability.

We will present our current progress in enabling end-to-end materials design for multiple materials classes and energy applications, from heterogeneous nanoporous catalysts, to crystalline and polymer solid electrolytes for batteries, to transition metal oxide for electrocatalysis.