ArticleNature communications2021
Teaching a neural network to attach and detach electrons from molecules.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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Who cites it
37 citing papers in PubMed.
- Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning.Nature communications · 2026Article
- Dissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learning.ACS omega · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.Angewandte Chemie (International ed. in English) · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Redefining Computational Enzymology with Multiscale Machine Learning/Molecular Mechanics: Catalytic Mechanism and Stereoselectivity in Diels-Alderases.Research square · 2025Article
- Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials.Journal of chemical theory and computation · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- IMPACT-4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections.Journal of computational chemistry · 2025Article
- The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.Scientific data · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes.Journal of chemical theory and computation · 2025Article
- Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules.Research (Washington, D.C.) · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- The seventh blind test of crystal structure prediction: structure ranking methods.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration.Journal of chemical theory and computation · 2024Article
- Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.Scientific data · 2024Article
- Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins.Chemical science · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2-3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.
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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.