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Updated: Aug 9, 2025
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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
Published on: June 21, 2022
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Neural network potentials for chemistry: concepts, applications and prospects
Silvan Käser1, Luis Itza Vazquez-Salazar1, Markus Meuwly1
1Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland m.meuwly@unibas.ch kai.toepfer@unibas.ch.
Digital Discovery
|February 17, 2023
Summary
Neural networks (NN) are revolutionizing computational chemistry for potential energy surfaces (PES) and spectroscopy. This review covers NN foundations, methods, and applications, highlighting advancements and future challenges.
Area of Science:
- Computational Chemistry
- Machine Learning
- Quantum Chemistry
Background:
- Artificial Neural Networks (NN) are integral to computational chemistry tasks.
- Applications include potential energy surface (PES) representation and spectroscopic predictions.
Purpose of the Study:
- Provide an overview of neural network-based full-dimensional PES.
- Discuss architectures, concepts, representation, and applications in chemical systems.
Main Methods:
- Data generation and training procedures for PES construction.
- Error assessment and refinement using transfer learning.
- NN applications for direct prediction of physical results.
Main Results:
- Illustrate recent improvements in PES accuracy and system size limitations.
- Showcase NN enabling direct physical predictions without dynamics simulations.
Conclusions:
- Summarize state-of-the-art NN approaches in computational chemistry.
- Identify current challenges in enhancing NN reliability and large-scale applicability.
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