Brandon M. Wood, Misko Dzamba1, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick (2025)
Highlighted by Jan Jensen
I use xTB extensively in my research and I am often asked why don't switch to machine learning potentials (MLPs) instead. My answer has always been that they have too many limitations: limited atom types, no charged molecules, can't handle reactions, efficiency on CPUs, solvent effects, etc. I know these can be overcome by making bespoke MLPs, then it is not really a simple replacement for xTB, but a whole new workflow.
However, the new UMA MLP from Meta seems to address all but one of my concerns (more on that below). UMA is trained to reproduce DFT energies and gradients calculated for nearly half a billion 3D structures, spanning molecules, surfaces, reactions, etc, containing atoms from virtually all of the periodic table. It is also possible to specify the charge and multiplicity and the cost seems to be comparable to xTB when running on CPUs, when interfaced with ORCA. So this is all very encouraging.
Two main questions remain. One is the accuracy, and by that I mean how faithfully it reproduces ωB97M-V/def2-TZVPD results (in the case of molecules) for molecules outside it's training set. AFAIK nothing is published yet, but encouraging results are being shared online.
The other main question is how to include implicit solvent effects. In cases where it is OK to optimise i the gas phase, one option is to compute the solvation energy with some other method and add it to the gas phase UMA results. Even if you do that at the DFT level, UMA has still saved you a lot if time. However, if the problem requires optimisation in solvent, then you have to use a faster method like xTB to compute the solvent effects on the gradient in order to get any time-savings. Depending on how well xTB does on the system of interest, this could "contaminate" the UMA results. Alternatively, a purely ML approach would basically amount to redoing UMA for molecules with continuum solvation included. Explicit solvation is fine in principle, but impractical for routine applications.
Anyway, before this is resolved there could be some fairly routine applications that still cannot be address satisfactorily with MLPs.
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