J. Harry Moore, Daniel J. Cole, and Gábor Csányi (2025)
Highlighted by Jan Jensen
Local CCSD(T) has made it possible to reach near chemical accuracy for many real life applications. However, most real life applications happen in solution, where the only realistic option is still continuum solvation methods, which, in general, do offer chemical accuracy (especially for charged systems).
In principle, this can be fixed explicit solvation at the CCSD(T) level but of course the need for sampling makes this practically impossible at present. However, this paper by Moore and co-workers is a step in that direction.
The idea is that ML potentials now approach chemical accuracy and are fast enough for proper sampling. The authors developed a MLP that is compatible with alchemical transformation (needed for sampling efficiency) and showed that experimental logP values (the difference in solvation energy between water and octanol) of drug-like compounds can be predicted within 0.65 kcal/mol (0.45 log units), i.e chemical accuracy.
Furthermore, the calculations took "only" about 4-7 days per molecule (octanol simulations converge slower than water) on a single node, containing either 8 NVIDIA A100, or 8 NVIDIA L40S, GPUs. While this is too slow for routine applications it is fast enough to create benchmark sets. This is great news since experimental solvation energies only are measured for relatively small molecules.
However, there are some caveats. The main one is that only neutral molecules where tested, since the MLP only was trained on neutral compounds, and it is not clear whether the same accuracy can be obtained for charged systems.
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