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Found 3 papers, 2 papers with code
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology.
Chemical Physics
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
