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

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [3];  [4];  [5]; ORCiD logo [6]; ORCiD logo [7];  [8]; ORCiD logo [9]; ORCiD logo [1]
  1. Harvard Univ., Cambridge, MA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Toyota Research Institute, Los Altos, CA (United States)
  4. Secretaria de Energia, Mexico City (Mexico)
  5. Fondo de Sustentabilidad Energetica, Mexico City (Mexico)
  6. Univ. Nacional Autonoma de Mexico, Mexico City (Mexico)
  7. Friedrich-Alexander-Univ. Erlangen-Nurnberg, Erlangen (Germany); ZAE Bayern, Erlangen (Germany)
  8. Wright-Patterson Air Force Base, Dayton, OH (United States)
  9. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1461992
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Nature Reviews. Materials
Additional Journal Information:
Journal Volume: 3; Journal Issue: 5; Related Information: © 2018 Macmillan Publishers Ltd., part of Springer Nature.; Journal ID: ISSN 2058-8437
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

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., and Aspuru-Guzik, Alan. Accelerating the discovery of materials for clean energy in the era of smart automation. United States: N. p., 2018. Web. doi:10.1038/s41578-018-0005-z.
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. Accelerating the discovery of materials for clean energy in the era of smart automation. United States. doi:10.1038/s41578-018-0005-z.
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., and Aspuru-Guzik, Alan. Thu . "Accelerating the discovery of materials for clean energy in the era of smart automation". United States. doi:10.1038/s41578-018-0005-z. https://www.osti.gov/servlets/purl/1461992.
@article{osti_1461992,
title = {Accelerating the discovery of materials for clean energy in the era of smart automation},
author = {Tabor, Daniel P. and Roch, Loic M. and Saikin, Semion K. and Kreisbeck, Christoph and Sheberla, Dennis and Montoya, Joseph H. and Dwaraknath, Shyam and Aykol, Muratahan and Ortiz, Carlos and Tribukait, Hermann and Amador-Bedolla, Carlos and Brabec, Christoph J. and Maruyama, Benji and Persson, Kristin A. and Aspuru-Guzik, Alan},
abstractNote = {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.},
doi = {10.1038/s41578-018-0005-z},
journal = {Nature Reviews. Materials},
number = 5,
volume = 3,
place = {United States},
year = {2018},
month = {4}
}

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