In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …

The rapid growth of materials chemistry data, driven by advancements in large-scale
radiation facilities as well as laboratory instruments, has outpaced conventional data …

Molecular self-assembly is pervasive in the formation of living and synthetic materials.
Knowledge gained from research into the principles of molecular self-assembly drives …

We present a new open-source, machine learning (ML) enhanced computational method for
experimentalists to quickly analyze high-throughput small-angle scattering results from …

Small-angle neutron scattering (SANS) is a powerful technique that has been widely used to
study polymer materials. In particular, it can provide information on the size, shape, and …

Polymers, with the capacity to tunably alter properties and response based on manipulation
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …

In this paper, we present an open-source machine learning (ML)-accelerated computational
method to analyze small-angle scattering profiles [I (q) vs q] from concentrated …

Designing Pt-based nanoparticle (NP) catalysts is of great interest for the lowering of Pt
usage and the enhancement of catalytic activity on the proton-exchange membrane fuel …

In this article, we present a machine learning enhancement for our recently developed
“Computational Reverse Engineering Analysis for Scattering Experiments”(CREASE) …

Mesoporous Pt-containing nanocomposites with well-organized pores are desirable for fuel
cells as well as sensors, electronics, and various chemical reactions. However, it remains …