CrossRef Open Access 2019 76 sitasi

Analyzing machine learning models to accelerate generation of fundamental materials insights

Mitsutaro Umehara Helge S. Stein Dan Guevarra Paul F. Newhouse David A. Boyd +1 lainnya

Abstrak

AbstractMachine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

Penulis (6)

M

Mitsutaro Umehara

H

Helge S. Stein

D

Dan Guevarra

P

Paul F. Newhouse

D

David A. Boyd

J

John M. Gregoire

Format Sitasi

Umehara, M., Stein, H.S., Guevarra, D., Newhouse, P.F., Boyd, D.A., Gregoire, J.M. (2019). Analyzing machine learning models to accelerate generation of fundamental materials insights. https://doi.org/10.1038/s41524-019-0172-5

Akses Cepat

Lihat di Sumber doi.org/10.1038/s41524-019-0172-5
Informasi Jurnal
Tahun Terbit
2019
Bahasa
en
Total Sitasi
76×
Sumber Database
CrossRef
DOI
10.1038/s41524-019-0172-5
Akses
Open Access ✓