5 papers
Can Coding Agents Reproduce Findings in Computational Materials Science?
Ziyang Huang, Yi Cao, Ali K. Shargh +15
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is uncle…
Uncertainty-aware phase fraction prediction and active-learning-guided out-of-domain discovery of refractory multi-principal element alloys
A. K. Shargh, C. D. Stiles, J. A. El-Awady
Refractory multi-principal element alloys (RMPEAs) represent a novel class of alloys characterized by an extensive compositional design space and the potential for exceptional mech…
From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extraction
Koushik Rameshbabu, Jing Luo, Ali Shargh +2
Scientific data are widely dispersed across research articles and are often reported inconsistently across text, tables, and figures, making manual data extraction and aggregation…
Effect of ice nucleating proteins on the structure-property relationships of ice: A molecular dynamics study
A. K. Shargh, C. D. Stiles, J. A. El-Awady
Ice-nucleating proteins (INPs) are a unique class of biological macromolecules that catalyze the freezing of supercooled water far more efficiently than homogeneous nucleation. The…
Temperature-dependent discovery of BCC refractory multi-principal element alloys: Integrating deep learning and CALPHAD calculations
A. K. Shargh, C. D. Stiles, J. A. El-Awady
Single-phase body-centered cubic (BCC) refractory multi-principal element alloys (RMPEAs) offer potential for developing alloys with exceptional strength. However, the compositiona…