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Atomic Force Microscopy Imaging and Force Spectroscopy of Supported Lipid Bilayers

Atomic Force Microscopy Imaging and Force Spectroscopy of Supported Lipid Bilayers

Published on: July 22, 2015

A transferable artificial neural network model for atomic forces in nanoparticles.

Shweta Jindal¹, Satya S Bulusu¹

¹Discipline of Chemistry, Indian Institute of Technology Indore, Simrol, Indore 453552, India.

The Journal of Chemical Physics

|November 24, 2018

Summary

This summary is machine-generated.

We developed a single artificial neural network (SANN) method to efficiently compute molecular energies and forces for systems with multiple chemical species. This approach reduces computational cost and improves accuracy for simulations like geometry optimizations and molecular dynamics.

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Area of Science:

Computational chemistry
Materials science
Artificial intelligence in chemistry

Background:

Traditional methods for fitting potential energy surfaces in multicomponent systems using artificial neural networks (ANNs) require a separate network for each chemical species.
This increases computational expense and limits applicability to complex systems with numerous species.

Purpose of the Study:

To introduce a novel strategy employing a single artificial neural network (SANN) for computing both energy and atomic forces in molecular systems, regardless of the number of chemical species.
To address the computational challenges and limitations of existing ANN-based methods for multicomponent systems.

Main Methods:

Designed and implemented a SANN architecture capable of handling systems with any number of chemical species.
Modified the traditional approach for fitting atomic forces from energy expressions to improve accuracy.
Applied the SANN method to study geometry optimizations and dynamics in gold-silver nanoalloys and thiol-protected gold nanoclusters.

Main Results:

The SANN method successfully computes energy and forces for multicomponent molecular systems with reduced computational cost compared to traditional ANNs.
Accurate prediction of atomic forces was achieved, crucial for geometry optimizations and molecular dynamics simulations.
The force fitting approach enabled training on smaller systems and extrapolation of parameters for accurate predictions in larger systems.

Conclusions:

The developed SANN strategy offers a more efficient and versatile approach for modeling molecular systems with multiple chemical species.
This method simplifies the mapping and fitting of atomic forces, making it broadly applicable across various molecular systems.
The ability to train on smaller systems and extrapolate parameters enhances the scalability and predictive power for complex molecular simulations.