Hao and Yitian’s Short-for-long ML for nonadiabatic dynamics paper was accepted by JCTC!

Congrats to Hao and Yitian on the acceptance of short-for-long machine-learning for nonadiabatic dynamics by J. Chem. Theory Comput.! This paper introduces a practical way to estimate memory time in non-Markovian dynamics of reduced density matrix and also compares several ML methods and physics-based methods. 

• Hao Zeng, Yitian Kou, Xiang Sun*, How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics? (accepted by JCTC) Chemrxiv DOI: 10.26434/chemrxiv-2024-qns7q