Conference Tools for 2027 TMS Annual Meeting & Exhibition

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About this Abstract

Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Integrating Physical Models, Specialized Databases, AI/ML Tools, and Self-Driving Labs to Understand the Synthesis of Inorganic Materials

Author(s)

Bin Ouyang

On-Site Speaker (Planned)

Bin Ouyang

Abstract Scope

Closing the materials design loop with AI/ML tools and self-driving laboratories is widely regarded as the next paradigm in materials design. Despite this promise, several bottlenecks remain in specific domains of materials science. In this presentation, we will share our perspective on how big data and specialized AI/ML tools can be leveraged to deepen our understanding of solid-state and aqueous synthesis reactions. We will present several closed-loop workflows that demonstrate how different components can work together to enable more controllable and predictable solid-state and aqueous solution syntheses of diverse ceramic materials for energy and sustainability applications.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Sustainability

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