Conveners
Plenary Session 4: AI and Data-Driven Approaches for Materials and Molecular Simulations
- Fei Peng (Clemson University)
Many recent efforts have focused on developing accurate interatomic potentials via data driven approaches in the pursuit of accelerating scientific studies of atomistic mechanisms as well as discovery of new materials. However, the accuracy of the interatomic potentials is limited by the data on which these models are trained, often obtained from DFT calculations. Thus, improving the quality...
Nonequilibrium phenomena in quantum materials represent an exciting research frontier, in which theoretical insights are critical for both understanding cutting-edge experiments and guiding the exploration and realization of transient quantum states. I will briefly review the main challenges for practical simulations of realistic condensed matter systems based on the propagation of many-body...
Large language models (LLMs) for biology such as protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein–nucleic acid binding sites, critical for characterizing the interactions between proteins and...
