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cond-mat.mtrl-sci2026
Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials
Yizhi Xu, Jordan Dorrell, Katarina Lisac +4
Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we employ CSP to perform high-t…
cond-mat.mtrl-sci2026
Revealing 3D Strain and Carbide Architectures in Additively Manufactured Ni Superalloys
James A. D. Ball, David M. Collins, Yuanbo T. Tang +4
Fast directional solidification during Laser Additive Manufacturing (LAM) produces a complex microstructure in nickel-based superalloys, comprising columnar grains with cellular su…
cond-mat.mtrl-sci2025
A Foundational Potential Energy Surface Dataset for Materials
Aaron D. Kaplan, Runze Liu, Ji Qi +6
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs) offer…