Accelerating the discovery of materials for clean energy in the era of smart automation

Tabor, Daniel P.; Roch, Loic M.; Saikin, Semion K.; Kreisbeck, Christoph; Sheberla, Dennis; Montoya, Joseph H.; Dwaraknath, Shyam; Aykol, Muratahan; Ortiz, Carlos; Tribukait, Hermann; Amador-Bedolla, Carlos; Brabec, Christoph J.; Maruyama, Benji; Persson, Kristin A.; Aspuru-Guzik, Alan

doi:10.1038/s41578-018-0005-z

Title: Accelerating the discovery of materials for clean energy in the era of smart automation

Journal Article · Thu Apr 26 00:00:00 EDT 2018 · Nature Reviews. Materials

DOI:https://doi.org/10.1038/s41578-018-0005-z· OSTI ID:1461992

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^[1]; Kreisbeck, Christoph ^[1];

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^[3]; Ortiz, Carlos ^[4]; Tribukait, Hermann ^[5];

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^[7]; Maruyama, Benji ^[8];

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Harvard Univ., Cambridge, MA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Toyota Research Institute, Los Altos, CA (United States)
Secretaria de Energia, Mexico City (Mexico)
Fondo de Sustentabilidad Energetica, Mexico City (Mexico)
Univ. Nacional Autonoma de Mexico, Mexico City (Mexico)
Friedrich-Alexander-Univ. Erlangen-Nurnberg, Erlangen (Germany); ZAE Bayern, Erlangen (Germany)
Wright-Patterson Air Force Base, Dayton, OH (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Furthermore, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1461992

Journal Information:: Nature Reviews. Materials, Vol. 3, Issue 5; Related Information: © 2018 Macmillan Publishers Ltd., part of Springer Nature.; ISSN 2058-8437

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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