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DS02.12.03 2022 MRS Fall Meeting

Machine Learning Based Strategies for Materials Discovery and Design

When and Where

Dec 2, 2022
3:00pm - 3:30pm

Hynes, Level 2, Room 210

Presenter

Co-Author(s)

Gowoon Cheon¹

Google Research¹

Abstract

Gowoon Cheon¹

Google Research¹

With the rapid increase in computational resources and the large-scale datasets of materials, data-driven methods for materials discovery and design are rising to prominence. I will talk about our work on combining high-throughput calculations, data mining and machine learning-based modeling to discover new materials.

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi

Mathieu Bauchy, University of California, Los Angeles

Ekin Dogus Cubuk, Google

Grace Gu, University of California, Berkeley

Symposium Support

Bronze
Patterns, Cell Press

Session Chairs

Mathieu Bauchy

N M Anoop Krishnan

In this Session

DS02.12.01
Multi-Fidelity Scale Bridging Using AI and Machine Learning

DS02.12.03
Machine Learning Based Strategies for Materials Discovery and Design

DS02.12.04
Accelerated Discovery of Aerospace Materials via Graph Neural Networks and Multiscale Modeling

DS02.12.05
Exploring Interface Structure Between Perovskite Oxides Using Evolutionary Structure Search and Automated Design of Deep Learning Potentials via Neural Architecture Search

DS02.12.06
Early Prediction of Ion Transport Properties in Molecular Dynamics Simulations of Solid Polymer Electrolytes Using Machine Learning

DS02.12.07
Combining Density Functional Theory and Machine Learning for Compositional Optimization of Alloyed Perovskite Electrocatalysts

DS02.12.08
Deep Learning-Based Prediction of Interfacial Surface Tension of Pendant Droplets

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