Name: ESRA-MLP-0003: MLIP of Cell 2026_HailiJia_MariaChan_ACS.Nano_5c16942
Creator: Haili Jia
Published: 2026-08-12
License: https://spdx.org/licenses/CC-BY-4.0

Researcher

Haili Jia

ORCID

0000-0003-2059-0768

PI

Maria Chan; Miaofang Chi

Project

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Cell / Sample ID

2026_HailiJia_MariaChan_ACS.Nano_5c16942 ← click to see all uploads from this cell

Publication DOI

10.1021/acsnano.5c16942 ↗

External Data URL

https://github.com/HailiJia/MultimodalEELS-XAS ↗

Upload Date (CDT)

2026-08-12 15:40:18

Upload ID

773cbc6d-c5dc-4f95-9cee-17f21f01f960

How to Cite

1 — Cite the dataset paper

↗ View paper

2 — Cite the ESRA platform

Galib, M. et al. (2026). ESRA: Energy Storage Research Assistant. Argonne National Laboratory. https://github.com/MusannaGalib/esra-platform

CC BY 4.0 · Data shared under Creative Commons Attribution 4.0

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Cell / Sample ID

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Notes

MLIP fields

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Target Properties *

Dft Training Level

Training Dataset Name

Training Dataset Size

Mae Energy Mev Per Atom

Mae Forces Mev Per A

Cell Id

Notes

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Location on Eagle / Perlmutter

Destination Path

/global/cfs/cdirs/m4845/gsharing/maria-chan-miaofang-chi/revealing-local-structures-through-machi/2026-hailijia-mariachan-acsnano-5c16942/Code/mlip/ESRA-MLP-0003/

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Linked Uploads

Type	Group	Date	Project	Link
XAS	Maria Chan; Miaofang Chi	2026-08-12 15:40	Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy	same cell
TEM	Maria Chan; Miaofang Chi	2026-08-12 15:40	Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy	same cell
DFT	Maria Chan; Miaofang Chi	2026-08-12 15:40	Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy	same cell

Experiment Metadata

Model Name	XGBoost
Model Type	Trained using simulated XAS and applied to experimental data
Visibility	public
Publication Doi	10.1021/acsnano.5c16942
Institution Code	ESRA
Target Properties	Li content (regression); local coordination environment (classification); oxygen vacancy detection; Ni/Li antisite detection
Dft Training Level	SCAN+U for DFT, PBE for XAS
Training Dataset Name	NMC_fdmnes.json
Training Dataset Size	851 structures, 35570 sites in total

MLIP fields

Location on Eagle / Perlmutter

Linked Uploads

Experiment Metadata

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