Heat capacity obtained using MACE-MP-0 and AD Hessians.
This repository contains the scripts and data files to reproduce the results from the publication:
Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation.[1]
The .cif files used in this study, provided by Moosavi et al. [2], are not included in this repository. These files can be downloaded from Materials Cloud.
[1] Gönnheimer, Nils, Karsten Reuter, and Johannes T. Margraf.
Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation.
chemrxiv.org, (2025).
https://doi.org/10.26434/chemrxiv-2025-p97c2
[2] Moosavi, S.M., Novotny, B.Á., Ongari, D. et al.
A data-science approach to predict the heat capacity of nanoporous materials.
Nat. Mater., 21, 1419–1425 (2022).
https://doi.org/10.1038/s41563-022-01374-3