TY - JOUR
T1 - Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry
AU - Askenazi, Evan M.
AU - Lazar, Emanuel A.
AU - Grinberg, Ilya
N1 - Publisher Copyright:
© 2023 American Chemical Society.
PY - 2023/12/11
Y1 - 2023/12/11
N2 - Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular, for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap, and lattice parameter as examples, we demonstrate that (1) construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable; (2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding; and (3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial for the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the data set consisting of several classes of materials.
AB - Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular, for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap, and lattice parameter as examples, we demonstrate that (1) construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable; (2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding; and (3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial for the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the data set consisting of several classes of materials.
UR - http://www.scopus.com/inward/record.url?scp=85179156496&partnerID=8YFLogxK
U2 - 10.1021/acs.jcim.3c01684
DO - 10.1021/acs.jcim.3c01684
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 37983482
AN - SCOPUS:85179156496
SN - 1549-9596
VL - 63
SP - 7350
EP - 7362
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 23
ER -