data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation

DOI

<p>This entry provides the training and test datasets, final datasets, trained models, and simulation results associated with four machine learning interatomic potentials (MLIPs) of liquid water: DeePMD and MACE models trained from scratch via active learning, a fine-tuned MACE foundation model, and a DeePMD potential obtained by knowledge distillation. All input scripts for the active learning and AIMD simulations have been included. Source data for reproducing the figures in the manuscript are provided. Detailed explanations for each subdirectory are provided inside the folder.</p>

Identifier
DOI https://doi.org/10.24435/materialscloud:bk-23
Related Identifier https://doi.org/10.26434/chemrxiv.15002964/v1
Related Identifier https://archive.materialscloud.org/communities/mcarchive
Related Identifier https://doi.org/10.24435/materialscloud:7n-fs
Metadata Access https://archive.materialscloud.org/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:xwas5-xxp24
Provenance
Creator Lian, Xiliang; Pasquarello, Alfredo
Publisher Materials Cloud
Contributor Lian, Xiliang
Publication Year 2026
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type info:eu-repo/semantics/other
Format application/gzip; text/plain
Discipline Materials Science and Engineering