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Related Experiment Video

Updated: Jul 3, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Parametrization of analytic interatomic potential functions using neural networks.

M Malshe¹, R Narulkar, L M Raff

¹Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA.

The Journal of Chemical Physics

|August 7, 2008

Summary

This study presents a generalized method for fitting empirical potential parameters using neural networks (NNs) and the Levenberg-Marquardt algorithm. The approach automates fitting, avoids local minima, and accurately models complex systems like silicon clusters.

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Last Updated: Jul 3, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

Computational materials science
Machine learning in chemistry
Atomistic simulations

Background:

Accurate empirical potentials are crucial for large-scale molecular simulations.
Traditional fitting methods often require manual selection of functional forms, limiting accuracy and efficiency.
Neural networks offer a powerful tool for learning complex relationships in data.

Purpose of the Study:

To develop a generalized, automated method for fitting empirical potential parameters.
To enable parameters to be functions of system coordinates using neural networks.
To improve the accuracy and efficiency of potential energy surface fitting.

Main Methods:

A generalized neural network (NN) computes a subset of potential parameters as functions of internal coordinates.
The Levenberg-Marquardt algorithm optimizes NN weights/biases and constant parameters.
A modified Jacobian incorporates derivatives with respect to potential parameters.
Applied to fit ab initio energies of Si(5) clusters from molecular dynamics simulations.

Main Results:

The method successfully fitted 10,202 Si(5) cluster energies to a Tersoff potential with high accuracy.
Achieved an average fitting error of 0.0148 eV (1.43 kJ mol(-1)).
Demonstrated the NN's ability to automatically determine functional forms for parameters.

Conclusions:

The generalized method provides an automated and efficient approach for empirical potential fitting.
It overcomes limitations of pre-defined functional forms by using NNs.
The results show comparable accuracy to more general, but less automated, fitting techniques.