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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Multi-information Source Batch Bayesian Optimization of Alloys
Author(s) Raymundo Arroyave
On-Site Speaker (Planned) Raymundo Arroyave
Abstract Scope ICME methods and combinatorial materials synthesis/characterization constitute the dominant paradigms for materials development. Unfortunately, they suffer from significant limitations: ICME methods tend to be sequential in nature and are limited by the computational costs of models used to build process-structure-property (PSP) relationships. Combinatorial methods, on the other hand, are "open loop" and are incapable of providing recommendations on the next action to take once information has been acquired. Here, we present a new framework that aims to incorporate the advantages of both paradigms while addressing all their weaknesses. We demonstrate a Multi-Information Source Batch Bayesian Optimization (BO) framework capable of integrating multiple models and information sources at once in order to optimally explore and exploit a materials design space. More importantly, our approach is capable of carrying out this Bayesian-optimal exploration/exploitation in batch mode. This overcomes the major limitation of sequential BO, enabling considerable order-of-magnitude speedups in materials design.


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