ReviewNational science review2023
Accurate fundamental invariant-neural network representation of
Review in National science review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Solving the vibrational Schrödinger equation with artificial neural networks.Nature communications · 2026Article
- Unveiling a flip-over retention mechanism in the gas-phase ClNature communications · 2026Article
- Long-Range Fit: A Software Package for the Representation and Study of Long-Range Molecular Interactions.Journal of chemical theory and computation · 2026Article
- Methods in PES-Learn: Direct-Fit Machine Learning of Born-Oppenheimer Potential Energy Surfaces.Molecules (Basel, Switzerland) · 2025Article
- "Gold-Standard" Δ-Machine Learned Transferable Potential for Linear Alkanes.The journal of physical chemistry letters · 2025Article
- Artificial Neural Networks Fitting of Potential Energy Curves and Surfaces: The 1/R Conundrum.Journal of computational chemistry · 2025Article
- Reactivity of syn-CHNature chemistry · 2025Article
- Targeted Transferable Machine-Learned Potential for Linear Alkanes Trained on CJournal of chemical theory and computation · 2025Article
- Exclusive roaming mechanism for the Cl + CNature communications · 2025Article
- How Microsolvation Affects the Balance of Atomic Level Mechanism in Substitution and Elimination Reactions: Insights into the Role of Solvent Molecules in Inducing Mechanistic Transitions.Molecules (Basel, Switzerland) · 2025Article
- Ab Initio Neural Network Potential Energy Surface and Quantum Dynamics Calculations on Na(Molecules (Basel, Switzerland) · 2024Article
- A Globally Accurate Neural Network Potential Energy Surface and Quantum Dynamics Studies on BeMolecules (Basel, Switzerland) · 2024Article
- Preface: paving the road for AI in molecular sciences.National science review · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly accurate potential energy surfaces are critically important for chemical reaction dynamics. The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces (PESs) for polyatomic reactions. Recently, our group has made substantial progress in this direction by developing the fundamental invariant-neural network (FI-NN) approach. Here, we review these advances, demonstrating that the FI-NN approach can represent highly accurate, global, full-dimensional PESs for reactive systems with even more than 10 atoms. These multi-channel reactions typically involve many intermediates, transition states, and products. The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation. These PESs exhibit a high level of complexity, molecular size, and accuracy of fit. Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms, providing deep insights into the intricate dynamics involved in combustion, atmospheric, and organic chemistry.
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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.