Universitat Autònoma de Barcelona

Whether hydrogen sulphide physisorbs or dissociates on nanoscale refractory oxide surfaces had been hard to settle because the configuration space is too large to enumerate. This work pairs unbiased automated adsorption sampling with an off-the-shelf atomistic foundation model: MACE's MPA screens candidate minima, each survivor is re-optimised with PBE-D2*(N) and HSE06 DFT, and DFT multiconfigurational infrared spectra are compared with in situ spectra at 150 K. The computed spectra reproduce the red-shift order of the H–S stretch across the three materials: silica at 2603 cm⁻¹, forsterite at 2588 cm⁻¹ and MgO at 2504 cm⁻¹.

Whether hydrogen sulphide physisorbs or dissociates on nanoscale refractory oxide surfaces had been hard to settle because the configuration space is too large to enumerate. This work pairs unbiased automated adsorption sampling with an off-the-shelf atomistic foundation model: MACE's MPA screens candidate minima, each survivor is re-optimised with PBE-D2*(N) and HSE06 DFT, and DFT multiconfigurational infrared spectra are compared with in situ spectra at 150 K. The computed spectra reproduce the red-shift order of the H–S stretch across the three materials: silica at 2603 cm⁻¹, forsterite at 2588 cm⁻¹ and MgO at 2504 cm⁻¹.

In replay-free continual learning, EWC penalizes changes to parameters deemed important using Fisher information estimated at the retained solution, but that local estimate does not capture sensitivity after the parameters move. FS-EWC uses Fisher information earlier, shaping the current task's solution by minimizing the worst-case Fisher trace over a neighborhood, and builds the retained anchor by averaging checkpoints along the optimization trajectory. Across vision and language benchmarks, FS-EWC generally improves on the corresponding EWC baseline under both full-parameter fine-tuning and LoRA.

In replay-free continual learning, EWC penalizes changes to parameters deemed important using Fisher information estimated at the retained solution, but that local estimate does not capture sensitivity after the parameters move. FS-EWC uses Fisher information earlier, shaping the current task's solution by minimizing the worst-case Fisher trace over a neighborhood, and builds the retained anchor by averaging checkpoints along the optimization trajectory. Across vision and language benchmarks, FS-EWC generally improves on the corresponding EWC baseline under both full-parameter fine-tuning and LoRA.