| Abstract Scope |
Over his career, Chris Wolverton has helped define modern computational materials science through pioneering contributions spanning first-principles calculations, phase stability, computational thermodynamics, multiscale modeling, and materials informatics. His vision of combining physics-based simulation with data-driven methods has fundamentally changed how new materials are discovered and designed. This talk will examine the evolution of that vision, beginning with high-throughput density functional theory and the Open Quantum Materials Database (OQMD), which demonstrated the power of large, curated computational datasets for predicting stable compounds and training early machine learning models. It will then explore how these concepts evolved into modern materials informatics, integrating machine learning with thermodynamics, uncertainty quantification, and active learning to accelerate industrial materials development. Chris's work also profoundly influenced the founding principles of Citrine Informatics, and his legacy continues to shape the integration of first-principles calculations, CALPHAD, and AI for accelerated materials discovery. |