Machine Learning Struggles to Predict Molecular Bond Energy
Recent research in machine learning and chemistry has explored the challenges and advancements in predicting molecular behaviour, particularly bond dissociation energy. Studies have shown that models trained solely on stable molecular structures perform poorly, even failing to capture the basic shape of the energy curve.
In a bid to capture long-range dependencies within molecular structures, state-space models like Mamba have been explored. However, even with extensive training data and large models, predictions for the bond dissociation curve of H2 were not accurate, indicating a fundamental limitation in learning essential physics.
Researchers, including K. T. Schütt, P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, and K.-R. Müller, tested the hypothesis that increasing the scalability of neural networks and training data improves the ability to model quantum chemistry systems. Their findings suggest that while increasing capacity and data size helps, even the largest models struggle to reproduce the fundamental repulsive energy curve expected from the interaction of two protons.
Transfer learning and foundation models, such as Uma and Mattersim, are also gaining prominence. These models allow researchers to apply knowledge gained from one dataset to another, further aiding in the understanding and prediction of molecular behaviour.
The pursuit of accurate molecular simulations continues to drive the machine learning community. Despite challenges, advancements like state-space models and transfer learning offer promising tools for understanding and predicting molecular behaviour. However, fundamental limitations in learning essential physics remain an area of active research.
