| Abstract Scope |
Predicting materials behavior at extreme temperatures is critical for designing refractory ceramics, molten salts, high-entropy alloys, and other advanced materials. This talk presents an integrated computational framework combining density functional theory, molecular dynamics, and deep learning to predict melting, diffusion, entropy, free energy, thermal expansion, and high-temperature phase stability. Two complementary platforms will be highlighted: SLUSCHI, which automates first-principles and molecular-dynamics workflows through VASP and LAMMPS interfaces, and MAPP, which enables rapid property prediction directly from chemical formulas using machine-learning models, web tools, and high-throughput APIs. Together, these tools support both accurate thermodynamic modeling and scalable materials screening. Case studies include melting-point prediction for thousands of minerals, design of ultra-high-temperature Hf–C–N and Zr–C–N systems, thermodynamic assessment of molten salts, and entropy calculations for ordered, disordered, and partially molten states. This framework provides an open route to high-temperature materials discovery and design. |