| Scope |
The past two decades have witnessed transformative advances in computational materials science, driven by the convergence of first-principles calculations, thermodynamic modeling, high-throughput computation, and more recently, machine learning and artificial intelligence. Together, these approaches have enabled unprecedented predictive capability in identifying stable and metastable compounds, resolving complex phase equilibria, and accelerating the discovery of novel materials.
Prof. Chris Wolverton has been a pioneering force in this transformation. His contributions to high-throughput density functional theory, data-driven materials design, and the integration of machine learning with thermodynamics and electronic-structure theory have profoundly expanded our ability to predict novel materials and elucidate structure–property relationships, cementing his role as one of the defining figures in modern computational materials science.
This Hume-Rothery Symposium honors Prof. Wolverton's contributions by convening leading experts in theory, computation, data science, and experiments to assess the state of the art in computational and data-driven materials discovery, and to chart emerging directions enabled by machine learning and AI.
Topics will include, but are not limited to:
• First-principles prediction of phase stability and phase diagrams
• High-throughput computational materials discovery
• Machine learning and artificial intelligence for materials design
• Data-driven approaches to phase stability and thermodynamics
• Integration of CALPHAD, first-principles calculations, and ML methods
• Prediction and discovery of functional materials
• Computational design of alloys and complex material systems
• Thermodynamic modeling and metastability
• Materials databases and informatics infrastructure for accelerated discovery
• Experimental validation of computationally predicted materials
• Autonomous and closed-loop materials discovery frameworks
Note: This symposium only accepts invited abstracts. |