Neural Constrained Charge Equilibration for Extracting Charge Transport Parameters from Machine Learned Interatomic Potentials

Overview

Authors: Carson Farmer¹, Hector Medina¹

School of Engineering Liberty University Lynchburg, Virginia 24515, United States

Journal: ACS omega, volume 11, issue 42, pages 64687-64693

Dates: received 28 April 2026; accepted 5 October 2026; published online 13 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI · PMCID PMC13717655

Status: code on request (how the code is found and verified)

Citations: 39 references in the paper

Abstract

Machine-learned interatomic potentials (MLIPs) continue to gain popularity as efficient tools for predicting energies and forces for atomic structures at an accuracy comparable to quantum chemistry. However, there are limited trained MLIPs for certain important applications such as in charge transport. Accelerating and automating the determination of parameters for such applications can be of critical importance in the rapid discovery of materials such as organic electronics (e.g., OLEDs). This work develops and introduces neural constrained charge equilibriation (NcQEq) and applies the technique to AimNet2 and OrbMol-V2. The new method enables extracting information about the diabatic states and thereby charge transport parameters. The approach is demonstrated on model systems against constrained density functional theory charge transport and then tested on intramolecular hole transport properties for polyethylene. This work demonstrates that the NcQeq approach is compatible with the charge equilibration schemes for AimNet2 and the latent charge predictions of OrbMol-V2. After calibration to reference data, the method enables estimation of the electronic coupling, reorganization energy, and the free energy difference.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

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Data Availability Statement

The data and code will be made available upon a reasonable request to the authors.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 37 references.

Cite

This paper

Farmer, C., & Medina, H. (2026). Neural Constrained Charge Equilibration for Extracting Charge Transport Parameters from Machine Learned Interatomic Potentials. ACS omega, 11(42), 64687-64693.

BibTeX

@article{farmer2026neural, author = {Farmer, Carson and Medina, Hector}, title = {{Neural Constrained Charge Equilibration for Extracting Charge Transport Parameters from Machine Learned Interatomic Potentials}}, journal = {ACS omega}, year = {2026}, month = oct, volume = {11}, number = {42}, pages = {64687--64693}, publisher = {American Chemical Society}, pmcid = {PMC13717655} }

RIS

TY - JOUR AU - Farmer, Carson AU - Medina, Hector TI - Neural Constrained Charge Equilibration for Extracting Charge Transport Parameters from Machine Learned Interatomic Potentials T2 - ACS omega PY - 2026 DA - 2026/10/30 VL - 11 IS - 42 SP - 64687 EP - 64693 PB - American Chemical Society LA - en ER -

CSL-JSON

{ "id": "pmcid:PMC13717655", "type": "article-journal", "title": "Neural Constrained Charge Equilibration for Extracting Charge Transport Parameters from Machine Learned Interatomic Potentials", "container-title": "ACS omega", "author": [ { "family": "Farmer", "given": "Carson" }, { "family": "Medina", "given": "Hector" } ], "volume": "11", "issue": "42", "page": "64687-64693", "PMCID": "PMC13717655", "publisher": "American Chemical Society", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 30 ] ] } }

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