ArticleJournal of chemical theory and computation2021
Learning Electron Densities in the Condensed Phase.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Electrochemical Electron Transfer: Key Concepts, Theories, and Parameterization via Atomistic Simulations.Chemical reviews · 2026Review
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.Journal of chemical theory and computation · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Image super-resolution inspired electron density prediction.Nature communications · 2025Article
- LAGNet: better electron density prediction for LCAO-based data and drug-like substances.Journal of cheminformatics · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Classification of substances by health hazard using deep neural networks and molecular electron densities.Journal of cheminformatics · 2024Article
- Article
- Physics-Inspired Equivariant Descriptors of Nonbonded Interactions.The journal of physical chemistry letters · 2023Article
- Machine learning electronic structure methods based on the one-electron reduced density matrix.Nature communications · 2023Article
- Structural and Electromagnetic Signatures of Anatase and Rutile NTs and Sheets in Three Different Water Models under Different Temperature Conditions.International journal of molecular sciences · 2023Article
- Electrostatic Embedding of Machine Learning Potentials.Journal of chemical theory and computation · 2023Article
- Extending machine learning beyond interatomic potentials for predicting molecular properties.Nature reviews. Chemistry · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable for learning isolated and periodic systems alike. We show that, using this formulation, the electron densities of metals, semiconductors, and molecular crystals can all be accurately predicted using symmetry-adapted Gaussian process regression models, properly adjusted for the nonorthogonal nature of the basis. These predicted densities enable the efficient calculation of electronic properties, which present errors on the order of tens of meV/atom when compared to
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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.