About the Workshop
Traditionally, lab-to-market in the case of Drug or Materials development typically occurs over 15 to 25 years from research to development. Also, the exploration of search space for synthesis and characterization requires millions of samples. Hence, there is a need to accelerate scientific discovery from lab-to-market. Recently, Generative Artificial Intelligence (Gen-AI) approaches have progressed as valuable AI tools to accelerate the process by overcoming the traditional ways to synthesize and characterize materials in a laborious fashion.
Workshop Highlights
- Feature representation for predicting physical, chemical, and mechanical properties
- AI-driven scientific discovery algorithms: generative models, reinforcement learning, evolutionary optimization, and Bayesian optimization can help navigate unexplored spaces more resource-efficiently
- Applications to thin film materials, composite materials, inorganic crystals, and molecular dynamics
Program
| S/N | Name | Affiliation | Title | Time |
|---|---|---|---|---|
| 1 | J Senthilnath | Institute for Infocomm Research, A*STAR, Singapore | Overview of Generative AI from Algorithm to Scientific Discovery | 9.00-10.00 |
| 2 | Mahindra Rautela | Los Alamos National Laboratory, USA | Deep generative modeling approach for composite materials: An accelerated solution of prediction, discovery & design problems | 10.30-11.30 |
| 3 | Andre KY Low | Nanyang Technological University, Singapore | Evolutionary Guided Bayesian Optimization to drive automated research in Materials Science | 11.30-12.00 |
| 4 | Jichao Li | Institute of High Performance Computing, A*STAR, Singapore | Generative AI for Turbine Blade Design Exploration and Optimization | 12.00-12.30 |
| 5 | Ruiming Zhu | Nanyang Technological University, Singapore | Generative Design of Inorganic Materials | 13.30-14.10 |
| 6 | Manna Dai | Institute of High Performance Computing, A*STAR, Singapore | Metasurface Design via Generative Adversarial Networks | 15.00-15.30 |
| 7 | Rajdeep Dutta | Institute for Infocomm Research, A*STAR, Singapore | Neuro-Symbolic Interpretable Reinforcement Learning for molecular property optimization | 15.30-16.15 |
| 8 | Nagarajan Raghavan | Singapore University of Technology & Design, Singapore | Perspective of role of generative AI for Battery technology development | 16.15-17.00 |
Venue
Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
Organizers
J. Senthilnath
Senior Scientist, Institute for Infocomm Research (I2R), A*STAR, Singapore
Email: J_Senthilnath@i2r.a-star.edu.sg
Senior Scientist, Institute for Infocomm Research (I2R), A*STAR, Singapore
Email: J_Senthilnath@i2r.a-star.edu.sg
Nagarajan Raghavan
Associate Professor, Singapore University of Technology & Design (SUTD), Singapore
Email: nagarajan@sutd.edu.sg
Associate Professor, Singapore University of Technology & Design (SUTD), Singapore
Email: nagarajan@sutd.edu.sg