Advanced Machine Learning Methods to Accelerate Materials Discovery


Event details

Date 21.03.2023
Hour 15:3016:30
Speaker Santiago Miret is an AI Researcher at Intel Labs where he focuses on applying AI for scientific problems with an emphasis on materials discovery and materials understanding. Through this effort, Santiago manages a wide range of academic collaborations focused on applying AI for scientific application. Among these collaboration, there have been notable engagements with the Matter Lab led by Alán Aspuru-Guzik at the University of Toronto and various AI laboratories at MILA in Montreal that have led to cross-institutional publications at various machine learning venues. Santiago was a primary organizer of the 1st AI for Accelerated Materials Discovery (AI4Mat) workshop at NeurIPS 2022, which brought together domain experts from various fields of materials science and AI to exchange research work and ideas in an interdisciplinary forum. Prior to working at Intel Labs, Santiago obtained his PhD in Materials Science and Engineering from the University of California, Berkeley.
Category Conferences - Seminars
Event Language English

The ability to apply advanced machine learning (ML) to process large amounts of heterogenous data can greatly aid in the understanding, discovery and design of new materials for advanced application like clean energy, sustainable semiconductor manufacturing and drug discovery. The heterogenous nature of data encompasses materials structural data, material property data and qualitative descriptions across various types of materials, including solid-state (e.g., semiconductors, batteries) and molecules. This multitude of multi-modal data types necessitates the application of a diverse of advanced ML techniques across different fields. Additionally, computational materials design is only the first step in the materials creation process, which involves a large range of experimental synthesis and analysis techniques creating additional technical challenges and uncertainties. Overall, applying ML to materials discovery provides a notable platform for interdisciplinary ML research with real-world impactful applications. 
In this talk, I will present an overview of Intel Labs’ research efforts focused on applying ML to materials discovery through a closed-loop discovery paradigm spanning automated materials design, automated material synthesis and automated materials characterization. Based on the closed-loop discovery framework, I will illustrate some example research efforts detailing materials property modeling using geometric deep learning, materials discovery using new generative algorithms and materials understanding through natural language processing.

Practical information

  • General public
  • Free


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