Dataset for: A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer Grafted Nanoparticles

Zhang, Melody Y.; Lee, Shih Kuang (Alex); Glotzer, Sharon C.; Lindsey, Rebecca K.

Work Description

Title: Dataset for: A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer Grafted Nanoparticles Embargo Deposited

Embargo release date

07/31/2025

Attribute	Value
Methodology	The dataset contains potential of mean force (PMF) data, calculated via the forward-reversed method, of polymer-grafted nanoparticles (PGNs). The PMF data were calculated using the HOOMD-blue simulation engine with molecular dynamics. The PMF data were generated in the xyzf format, an extended xyz particle data format, which is used by the ChIMES-LSQ package. This dataset can be used to perform traditional ChIMES model fitting and extended ChIMES fitting to produce one-site coarse-grained PGN models. Please refer to our publication and the README.md for detailed usage of the dataset.
Description	The PMF dataset was generated for our work “A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer Grafted Nanoparticles.” The dataset contains PMF data in xyzf file format for 6 PGNs designed with varying capping polymer lengths, represented as the constituent numbers of Martini beads, ranging from 3 to 8. The length unit is in Angstroms, and the energy unit is in kJ/mol.
Creator	Zhang, Melody Y. Lee, Shih Kuang (Alex) Glotzer, Sharon C. Lindsey, Rebecca K.
Creator ORCID iD	https://orcid.org/0009-0002-4677-336X
Depositor	[email protected]
Depositor creator	true
Contact information	[email protected]; [email protected]; [email protected]
Discipline	Engineering
Funding agency	National Science Foundation (NSF) Other Funding Agency
Other Funding agency	Advanced Research Computing at the University of Michigan, Ann Arbor
ORSP grant number	DMR 2302470, DMR 140129, CHM 240010, CHM 240082
Keyword	Polymer-grafted nanoparticles Potential of mean force Interaction potential Machine learning Inverse design Coarse-graining
Date coverage	2023-09-01 to 2025-06-10
Citations to related material	Joshua A. Anderson, Jens Glaser, Sharon C. Glotzer, HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations, Computational Materials Science, Volume 173, 2020, 109363, https://doi.org/10.1016/j.commatsci.2019.109363. Zhang MY, Lee S-K (Alex), Glotzer S, Lindsey R. A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer Grafted Nanoparticles. ChemRxiv. 2025; doi:10.26434/chemrxiv-2025-n4d3s This content is a preprint and has not been peer-reviewed. https://github.com/LindseyLab-umich/chimes_lsq-LLfork/tree/develop/contrib/xchimes https://github.com/LindseyLab-umich/chimes_lsq-LLfork/tree/develop/test_suite-lsq/OS-CG_PGNs https://github.com/LindseyLab-umich/chimes_calculator-LLfork/tree/add_lmp_tests/etc/lmp/tests/CG_ChIMES_lmp_tests https://github.com/glotzerlab/xchimes
Resource type	Dataset
Last modified	06/11/2025
Published	06/11/2025
Language	English
DOI	https://doi.org/10.7302/s3rm-vd81
License	http://creativecommons.org/licenses/by-nc/4.0/

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