TY - JOUR
T1 - Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
AU - Lookman, Turab
AU - Balachandran, Prasanna V.
AU - Xue, Dezhen
AU - Yuan, Ruihao
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.
AB - One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.
UR - http://www.scopus.com/inward/record.url?scp=85061732788&partnerID=8YFLogxK
U2 - 10.1038/s41524-019-0153-8
DO - 10.1038/s41524-019-0153-8
M3 - 文献综述
AN - SCOPUS:85061732788
SN - 2057-3960
VL - 5
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 21
ER -