4 papers
Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Tamás Lajos Tompa, Eszter Varga-Umbrich, Ilyes Batatia +3
Adapting machine-learned interatomic potential (MLIP) foundation models to specialised tasks through fine-tuning is an increasingly important practice, yet systematic guidance on w…
Eigenstate condensation in quantum systems with finite-dimensional Hilbert spaces
Christopher David White, Michael Winer, Noam Bernstein
Random quantum states drawn from the Haar ensemble with a constraint on the energy expectation value display \textit{eigenstate conden…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
Mark E. Turiansky, John L. Lyons, Noam Bernstein
The optical properties of defects in solids produce rich physics, from gemstone coloration to single-photon emission for quantum networks. Essential to describing optical transitio…