DFT is so 2008?

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• Aaron Kaplan, Lawrence Berkeley National Laboratory (USA)
• Monday 09 March 2026, 14:00-14:30
• https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT09.

If you have a question about this talk, please contact Dr Fabian Berger.

Although DFT has long been the workhorse method of computational chemistry, materials science, and physics, all beasts of burden eventually need a break. Machine learning interatomic potentials (MLIPs) have recently begun to bridge the divide between chemistry-dependent classical interatomic potentials and DFT . Often covering nearly all the periodic table, the accuracy of an MLIP depends heavily on its training data. Extant MLIP -ready datasets, such as the Materials Project, tend to be noisy and favor basins on the potential energy surface. More recent efforts to purpose-build datasets for training MLI Ps have favored maximalist approaches, possibly leading to dataset duplication. In my talk, I’ll give a broad overview of the accuracy and computational considerations entering the construction of MLIP datasets, including relevant background on DFT . I’ll then discuss two recent dataset generation efforts I’ve contributed to, MatPES and MP-ALOE, which have sought to maximize dataset information density and accuracy. I’ll finish with an outlook to where these efforts lead.

This talk is part of the Lennard-Jones Centre series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre
• https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT09

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