Emerging materials intelligence ecosystems propelled by machine learning
Abstract
We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materialsmore »
- Authors:
-
[1];
[2]
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Georgia Institute of Technology, Atlanta, GA (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program; US Department of the Navy, Office of Naval Research (ONR)
- OSTI Identifier:
- 1864296
- Grant/Contract Number:
- AC02-06CH11357; PRJ1007392
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Reviews. Materials
- Additional Journal Information:
- Journal Volume: 6; Journal Issue: 8; Journal ID: ISSN 2058-8437
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; machine learning; materials science; techniques; instrumentation; theory; computation
Citation Formats
Batra, Rohit, Song, Le, and Ramprasad, Rampi. Emerging materials intelligence ecosystems propelled by machine learning. United States: N. p., 2020.
Web. doi:10.1038/s41578-020-00255-y.
Batra, Rohit, Song, Le, & Ramprasad, Rampi. Emerging materials intelligence ecosystems propelled by machine learning. United States. https://doi.org/10.1038/s41578-020-00255-y
Batra, Rohit, Song, Le, and Ramprasad, Rampi. Mon .
"Emerging materials intelligence ecosystems propelled by machine learning". United States. https://doi.org/10.1038/s41578-020-00255-y. https://www.osti.gov/servlets/purl/1864296.
@article{osti_1864296,
title = {Emerging materials intelligence ecosystems propelled by machine learning},
author = {Batra, Rohit and Song, Le and Ramprasad, Rampi},
abstractNote = {We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.},
doi = {10.1038/s41578-020-00255-y},
journal = {Nature Reviews. Materials},
number = 8,
volume = 6,
place = {United States},
year = {Mon Nov 09 00:00:00 EST 2020},
month = {Mon Nov 09 00:00:00 EST 2020}
}
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