Abstract

New tools enable new ways of working, and materials science is no exception. In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows.

Details

Title
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Author
Pyzer-Knapp, Edward O 1   VIAFID ORCID Logo  ; Pitera, Jed W 2 ; Staar Peter W J 3 ; Takeda Seiji 4 ; Laino Teodoro 3 ; Sanders, Daniel P 5 ; Sexton, James 6 ; Smith, John R 6 ; Curioni Alessandro 3 

1  IBM Research Europe - Daresbury, Daresbury, UK 
2  IBM Almaden Research Centre, San Jose, USA 
3  IBM Research Europe Zurich, Rüschlikon, Switzerland (GRID:grid.410387.9) 
4  IBM Research Tokyo, Tokyo, Japan (GRID:grid.420126.3) 
5  IBM Almaden Research Centre, San Jose, USA (GRID:grid.410387.9) 
6  IBM Thomas J. Watson Research Centre, Yorktown Heights, USA (GRID:grid.410387.9) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20573960
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41524-022-00765-z
ProQuest document ID
2655335564
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.