#PhysRevMaterials

Adaptive energy reference for machine-learning models of the electronic density of states Machine learning methods for predicting electronic density of states often assume that the model predictions and targets share the same absolute energy reference. However, this overlooks a subtle poin...

So read about this on #PhysRevMaterials journals.aps.org/prmaterials/..., or on the #arXiv if you don't have a subscription arxiv.org/html/2407.01.... Ah and there's a #cookbook recipe to atomistic-cookbook.org/examples/dos...!

Title and abstract of "Adaptive energy reference for machine-learning models of the electronic density of states" by Wei Bin How, Sanggyu Chong, Federico Grasselli, Kevin K. Huguenin-Dumittan, and Michele Ceriotti - published as Phys. Rev. Materials 9, 013802 (2025)

📢 Highlighted on #PhysRevMaterials (not yet on 🦋), a little, insightful gem by 🧑‍🚀 Wei Bin, Raymond, Fede & Kevin, relevant for all of you #machinelearning electronic structure properties. Band alignment matters, but it shouldn't, so here's how to deal with it with a self-aligning loss! A short 🧵...