ArticleNature communications2021
Pure non-local machine-learned density functional theory for electron correlation.
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 12 papers.
What it found
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The trial behind it
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Who cites it
12 citing papers in PubMed.
- Multistate reactivity of weak-field clusters at ambient temperatures.Nature communications · 2026Review
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications.ACS materials Au · 2026Review
- Basic concepts of grain-boundary structure and phase behavior: From theory and experiments to material properties.MRS bulletin · 2026Review
- Real-space machine learning of correlation density functionals.Nature communications · 2025Article
- Machine Learning Accurate Exchange-Correlation Potentials for Reducing Delocalization Error in Density Functional Theory.JACS Au · 2025Article
- Adapting hybrid density functionals with machine learning.Science advances · 2025Article
- SPAJournal of chemical theory and computation · 2024Article
- Machine Learning Density Functionals from the Random-Phase Approximation.Journal of chemical theory and computation · 2023Article
- Electronic structure of strongly correlated systems: recent developments in multiconfiguration pair-density functional theory and multiconfiguration nonclassical-energy functional theory.Chemical science · 2022Review
- Cerium Oxides withoutThe journal of physical chemistry letters · 2021Article
- A review on machine learning algorithms for the ionic liquid chemical space.Chemical science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron self-interaction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the mean-field computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation.
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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.