Materials Science Research Lecture

***Refreshments at 3:45pm in Noyes lobby

Abstract:

High throughput experimentation for materials discovery comprises a family of research techniques that have evolved in the past three decades and are presently being transformed by advancements in computation, both ab initio theory and artificial intelligence (AI). The High Throughput Experimentation (HTE) group at Caltech has implemented dozens of high throughput workflows, each with a specific strategy: Edisonian exploration, data-driven hypothesis generation, active learning for benchmarking and designing autonomous labs, accelerated evaluation of predictions from high throughput density functional theory, and generation of data to train physics-informed machine learning models. Underlying this portfolio of workflows is a suite of automation, orchestration, and data management capabilities, a toolbox for realizing the grand vision of worldwide interconnected laboratories. This vision could lead to a million-fold increase in knowledge generation by accelerating scientific learning cycles from the traditional year-long cadence set by publications and conferences to sub-1 minute AI learning cycles, which hinges upon the development of AI that comprehends and reasons about scientific data. While highlighting our most successful workflows and the associated (photo)electrocatalyst discoveries, the overarching theme of the presentation will be the strategic application of experiment automation and the grand challenges for further accelerating discovery in materials and chemical sciences.

More about the Speaker:

John Gregoire is a Research Professor of Applied Physics and Materials Science at Caltech, where he leads the High Throughput Experimentation group. He also holds leadership positions in DOE centers focused on solar fuels and reactive carbon capture. His research team explores, discovers, and understands energy-related materials via high throughput experimental methods and their integration with materials theory and artificial intelligence. He received his B.A. in Math, Physics, and Computer Science from Concordia College and PhD in Physics from Cornell University.