ReviewDigital discovery2023
Neural network potentials for chemistry: concepts, applications and prospects.
Review in Digital discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
32 citing papers in PubMed.
- Tripeptide dynamics from empirical and machine-learned energy functions.Biophysical journal · 2026Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Compact Kernel/Neural Network Representation for Accurate, Fast, and Global Reactive Molecular Potential Energy Surfaces.Precision chemistry · 2026Article
- CRISP: Enhancing ASE Workflows With Advanced Molecular Simulation Post-Processing.Journal of computational chemistry · 2026Article
- Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks.ACS central science · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism Controversy.Journal of chemical theory and computation · 2026Article
- A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.Scientific data · 2025Article
- Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.The journal of physical chemistry letters · 2025Article
- Revisiting the ion dynamics in LiChemical science · 2025Article
- A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning.The journal of physical chemistry letters · 2025Article
- A dataset of chemical reaction pathways incorporating halogen chemistry.Scientific data · 2025Article
- Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations.Journal of chemical theory and computation · 2025Article
- Transferable Neural Network Potentials and Condensed Phase Properties.Journal of chemical information and modeling · 2025Article
- Artificial Neural Networks Fitting of Potential Energy Curves and Surfaces: The 1/R Conundrum.Journal of computational chemistry · 2025Article
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies.Journal of chemical theory and computation · 2025Article
- Enhancing Empirical Energy Functions Using Physics- and Machine Learning-Based Extensions: Structure, Dynamics and Spectroscopy of Modified Benzenes.Journal of computational chemistry · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.