Study Questions Reliance on Scaling for Accurate Chemical Modelling
Researchers Matthias Rupp, Felix A. Faber, and O. Anatole von Lilienfeld have published a paper questioning the reliance on scaling up neural networks and datasets for accurate chemical modeling. They focused on predicting the bond dissociation energy of the hydrogen molecule.
The study found that even with increased model capacity and dataset size, including non-ground-state structures, the improvement in predicting the H2 bond dissociation energy was modest. Surprisingly, models trained solely on stable molecular structures struggled to predict this fundamental energy, failing to capture its basic shape.
The authors evaluated models trained on varying amounts of stable molecular structures, testing their ability to predict energy changes as bonds stretch and break. Even the largest models, trained on extensive datasets, struggled to reproduce the expected repulsive energy curve from the interaction of two protons. This suggests that simply increasing scale does not guarantee reliable chemical modeling.
Neural networks, particularly convolutional and graph neural networks, are central to these efforts, proving effective for analyzing spatial and structural data in molecular systems. Transfer learning and foundation models, like Uma and Mattersim, are also gaining prominence, allowing researchers to apply knowledge gained from one dataset to another and create broadly applicable models.
The inability of large foundation models to capture the basic physics of even the simplest diatomic molecule indicates that scaling alone is insufficient for building reliable quantum chemical models. The pursuit of increasingly accurate molecular simulations drives the machine learning community to build ever-larger foundation models, but this study suggests that other approaches may be necessary for capturing fundamental chemical properties.
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