1 research outputs found
Machine Learning and Statistical Analysis for Materials Science: Stability and Transferability of Fingerprint Descriptors and Chemical Insights
In
the paradigm of virtual high-throughput screening for materials,
we have developed a semiautomated workflow or “recipe”
that can help a material scientist to start from a raw data set of
materials with their properties and descriptors, build predictive
models, and draw insights into the governing mechanism. We demonstrate
our recipe, which employs machine learning tools and statistical analysis,
through application to a case study leading to identification of descriptors
relevant to catalysts for CO<sub>2</sub> electroreduction, starting
from a published database of 298 catalyst alloys. At the heart of
our methodology lies the Bootstrapped Projected Gradient Descent (BoPGD)
algorithm, which has significant advantages over commonly used machine
learning (ML) and statistical analysis (SA) tools such as the regression
coefficient shrinkage-based method (LASSO) or artificial neural networks:
(a) it selects descriptors with greater stability and transferability,
with a goal to understand the chemical mechanism rather than fitting
data, and (b) while being effective for smaller data sets such as
in the test case, it employs clustering of descriptors to scale far
more efficiently to large size of descriptor sets in terms of computational
speed. In addition to identifying the descriptors that parametrize
the <i>d</i>-band model of catalysts for CO<sub>2</sub> reduction,
we predict work function to be an essential and relevant descriptor.
Based on this result, we propose a modification of the <i>d</i>-band model that includes the chemical effect of work function, and
show that the resulting predictive model gives the binding energy
of CO to catalyst fairly accurately. Since our scheme is general and
particularly efficient in reducing a set of large number of descriptors
to a minimal one, we expect it to be a versatile tool in obtaining
chemical insights into complex phenomena and development of predictive
models for design of materials