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AdvancedComputational Materials Science

AI & Machine Learning for Materials Discovery

Generative models, high-throughput screening, and data-driven materials design

DRA Admin · CEMDI, INRS15 micro-lessons156 min totalAdvanced

About This Course

Discover how artificial intelligence and machine learning are transforming computational materials science. This course features four world-class researchers presenting cutting-edge methods — from generative models that design novel crystals, to high-throughput screening of porous materials, to data-driven discovery of high-entropy alloys and beyond. Designed for PhD students, postdocs, and researchers in materials science, chemistry, physics, and related fields. --- Featuring talks from the CEMDI Symposium series: • Prof. Alex Hernandez-Garcia (Mila / Université de Montréal) — 2nd CEMDI Symposium, 2024 • Prof. Tom Woo (University of Ottawa) — 3rd CEMDI-PAIMS Symposium, 2025 • Prof. Chandra Singh (University of Toronto) — 3rd CEMDI-PAIMS Symposium, 2025 • Dr. Adrian Xiao Bin Yong (Kyushu University) — 3rd CEMDI-PAIMS Symposium, 2025 All videos are courtesy of the CEMDI YouTube channel (youtube.com/@cemdi6439).

Skills You'll Gain

Machine LearningMaterials DiscoveryGFlowNetsMOFsCO2 CaptureHigh-Entropy AlloysGenerative ModelsDFTBenchmarking

Course Content

Module 1: Generative Flow Networks for Crystal Design

  • The Inverse Design Problem12 min
  • Introduction to GFlowNets16 min
  • Crystal-GFN Architecture14 min
  • Results and Applications13 min
  • Q&A: Connections to MCMC and Reinforcement Learning5 min

Module 2: ML-Assisted Design of Porous Materials for CO2 Capture

  • Metal-Organic Frameworks and the CO2 Problem10 min
  • High-Throughput Screening with Machine Learning10 min
  • Process-Scale Integration and Results13 min

Module 3: AI-Enabled Discovery of High-Entropy Materials

  • High-Entropy Alloys and Catalysis12 min
  • Breaking Scaling Relations in CO2 Reduction10 min
  • Hydrogen Evolution and Platinum Reduction6 min
  • High-Entropy MXenes for Batteries6 min

Module 4: Benchmarking Generative Models for Materials

  • The Evaluation Problem in Generative Materials Science10 min
  • The Dismai-Bench Framework10 min
  • Model Comparison and Insights9 min

Ready to Learn?

  • 100% free — no hidden costs
  • Earn a digital credential
  • Learn at your own pace
  • 15 micro-lessons, 156 min total

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