Progress in Li-ion Energy Storage and Alternative Technologies by Prof. Gerband Ceder
제 2회 최종현학술원 과학혁신 컨퍼런스 “Enabling Future of AI: Battery to Semiconductor”
Data-Driven Design for Energy Materials - IPAM at UCLA
Prof. Kristin Persson at the Department of Materials Science and Engineering, UC Berkeley
2023 Rosemary Schnell IIN Symposium "The Materials for Tomorrow, Today"
Prof. Alán Aspuru-Guzik at the Department of Chemistry at the University of Toronto
ACME Materials Research Seminar
Prof. Chris Wolverton at Department of Materials Science and Engineering at the Northwestern University
Fusing Machine Learning and Simulations for Materials Design
Prof. Rafael Gomez-Bombarelli at the Department of Materials Science and Engineering, MIT
Materials Design at SCALE through Automation & Machine Learning
Prof. Shyue Ping Ong at the NanoEngineering, UC San Diego
How AI is Revolutionizing the Discovery of Materials
Prof. Peter Schindler at Northeastern University
SIMPLE-NN 이론 및 실습 (이론)
한승우 교수, 서울대
SIMPLE-NN 이론 및 실습 (실습)
한승우 교수, 서울대
Materials Genome Initiative (MGI) promises to expedite materials discovery through high-through computation and high-throughput experiments. Application of Artificial Intelligence (AI) tools such as machine learning, deep learning, and various optimization techniques are critical to achieving such a goal.
Some of the key areas of applications in employing AI techniques to materials are: developing well-curated and diverse datasets, choosing effective representation for materials, inverse materials design, integrating autonomous experiments and theory, and choosing appropriate algorithm/workflow. The idea of including physics-based models in the AI framework is also fascinating. Lastly, uncertainty quantification in AI-based predictions for material properties and issues related to building infrastructure for disseminating AI knowledge is of immense importance for making AI-based investigations of materials successful. This workshop is intended to cover all the above-mentioned challenges.
GNN-Based Prediction of Material Properties and Chemical Reactions
박찬영 교수, KAIST 산업시스템공학과
Rethinking Materials Discovery with Generative Models
Dr. Tian Xie at Microsoft Research AI4Science
MatterGen: a Generative Model for Inorganic Materials Design
Dr. Tian Xie at Microsoft Research AI4Science
LLM Hackathon for Applications in Materials and Chemistry