We are excited to share two significant papers recently accepted from our group, showcasing our work in solvation speciation and machine learning-driven alloy discovery:
1. Uncovering Solvation Subtleties in Halide Salts
The first paper, led by Farhan Shaikh and Emily Hiralal, represents a monumental computational effort to understand the molecular makeup of (A-site)-halide salts and solvents. By simulating and analyzing over 1.3 million frames of Ab Initio Molecular Dynamics (AIMD), the team has provided a definitive look at what is truly present in these solutions. This foundational work sets the stage for “Paper II,” which will explore how these dynamics shift with the addition of lead salts.
- Title: Uncovering the subtleties of solvation and speciation of organic halide salts in organic solvents
- Journal: The Journal of Physical Chemistry B (Accepted 4/14/26)
- Authors: E. Hiralal,* F. Shaikh,* P. Clancy, L.A. Estroff (co-corresponding authors)
2. Machine Learning-Driven Discovery of High-Strength Alloys
The second paper, led by Maitreyee Sharma Priyadarshini, details a critical breakthrough in closed-loop discovery for Multiple Principal Element Alloys (MPEAs). By optimizing for alloy strength, the team successfully identified rare, “out-of-distribution” solutions that traditional methods might overlook.
- Title: Machine Learning-Driven Closed-Loop Discovery of Hard Multiple Principal Element Alloys
- Journal: Materials Horizons (Accepted 4/15/26)
- Authors: M. Sharma Priyadarshini,* E. Gienger,* J. Ren, B. Piloseno, E. A. Pogue, P. K. Lambert and P. Clancy
- Pre-print: ChemRxiv