About

Kieron Burke is a distinguished professor in both the chemistry and physics departments at UC Irvine, with a research focus on developing a theory of quantum mechanics known as density functional theory (DFT). His work involves developing all aspects of DFT, including formalism, extensions to new areas, new approximations, and simplifications. DFT is a widely used tool in science, especially in chemistry and materials science, for solving the equations of quantum mechanics for electrons in various substances. Burke's research has practical applications in materials science, chemistry, matter under extreme conditions, magnetic materials, and molecular electronics. He has pioneered new applications of machine learning to electronic structure problems, collaborating with Google Accelerated Science and Google DeepMind. Burke is also known for his educational and outreach activities, and his graduate course in machine learning for scientists is among the most popular in the school of physical sciences.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Physics
• Statistical physics
• Data science
• Management science
• Chemistry
• Cognitive science
• Psychology
• Bioinformatics
• Epistemology
• Computational chemistry
• Quantum mechanics
• Mathematical analysis
• Algorithm
• Biology
• Engineering

Selected publications

• Density-Corrected Density Functional Theory for Solids

  ChemRxiv · 2026-04-24

  articleOpen access

  Density-corrected density functional theory (DC-DFT) considers whether self-consistent densities yield optimal energetics in Kohn-Sham calculations. With considerable success in molecular calculations, we here apply DC-DFT to solid-state calculations with Hartree-Fock (HF) densities. We resolve a known anomaly: that dispersion corrections can worsen results as one climbs Jacob’s ladder. This is illustrated for simple covalent solids, such as Si. The relative energetics of phases of ice are also much improved with HF densities, as is CO absorption on NaCl, while graphite interlayer binding is insensitive to its density.

  PublisherOA PDFDOI

• Enhancing molecular dynamics with equivariant machine-learned densities

  arXiv (Cornell University) · 2026-04-27

  preprintOpen access

  Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $Δ$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.

  PublisherDOI

• Enhancing molecular dynamics with equivariant machine-learned densities

  ArXiv.org · 2026-04-27

  articleOpen access

  Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $Δ$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.

  PublisherOA PDF

• Conditional probability density functional theory for solids

  ArXiv.org · 2026-05-13

  articleOpen access

  A recently developed approach, conditional probability density functional theory (CP-DFT), yields direct access to the exchange-correlation hole of a system, an important correlation function that is not available from any standard DFT calculation. We present the first results for extended materials with periodic boundary conditions. We demonstrate that CP-DFT works on weakly correlated materials (Na, Si). When applied to the prototypical Kagome material $CsV_3Sb_5$, we find $d$-orbital correlations that are not captured by standard DFT. Such distribution leads to a positive finding probability between two separated electrons and an enhanced charge density wave signal, suggesting a useful approach for strongly correlated systems.

  PublisherOA PDF

• Conditional probability density functional theory for solids

  arXiv (Cornell University) · 2026-05-13

  preprintOpen access

  A recently developed approach, conditional probability density functional theory (CP-DFT), yields direct access to the exchange-correlation hole of a system, an important correlation function that is not available from any standard DFT calculation. We present the first results for extended materials with periodic boundary conditions. We demonstrate that CP-DFT works on weakly correlated materials (Na, Si). When applied to the prototypical Kagome material $CsV_3Sb_5$, we find $d$-orbital correlations that are not captured by standard DFT. Such distribution leads to a positive finding probability between two separated electrons and an enhanced charge density wave signal, suggesting a useful approach for strongly correlated systems.

  PublisherDOI

• Approximate normalizations for approximate density functionals

  ArXiv.org · 2025-04-04

  preprintOpen accessSenior author

  It seems self-evident that a density functional calculation should be normalized to the number of electrons in the system. We present multiple examples where the accuracy of the approximate energy is improved (sometimes greatly) by violating this basic principle. In one dimension, we explicitly derive the appropriate correction to the normalization. Beyond one dimension, Weyl asymptotics for energy levels yield these corrections for any cavity. We include examples with Coulomb potentials and the exchange energy of atoms to illustrate relevance to realistic calculations.

  PublisherOA PDFDOI

• Extending Density-Corrected Density Functional Theory to Large Molecular Systems

  The Journal of Physical Chemistry Letters · 2025-01-21 · 5 citations

  article

  Practical density-corrected density functional theory (DC-DFT) calculations rely on Hartree–Fock (HF) densities, which can be computationally expensive for systems with over a hundred atoms. We extend the applicability of HF-DFT using the dual-basis method, where the density matrix from a smaller basis set is used to estimate the HF solution on a larger basis set. Benchmarks on many systems, including the GMTKN55 database for main-group chemistry, and the L7 and S6L data sets for large molecular systems demonstrate the efficacy of our approach. We apply the dual-basis method to both DNA and HIV systems and compare with the literature. The details of a recent reparameterization of HF-r2SCAN-DC4 are explained, showing no loss of performance.

  PublisherDOI

• Can machines learn density functionals? Past, present, and future of ML in DFT

  ArXiv.org · 2025-03-03 · 1 citations

  preprintOpen accessSenior author

  Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future.

  PublisherOA PDFDOI

• Ensemble Time-Dependent Density Functional Theory

  ArXiv.org · 2025-07-25

  preprintOpen accessSenior author

  Time-dependent density functional theory (TDDFT) is a standard approach for calculating optical excitations of molecules and solids, while ensemble DFT (EDFT) is a promising alternative under development. We introduce ensemble TDDFT (ETDDFT), a practical theory that combines the two, generalizing both; we ensemble-generalize the Gross-Kohn equation and the exchange-correlation kernel of TDDFT, and generalize EDFT to time-dependent problems. We relate coordinate scaling to the adiabatic connection. The new theory provides multiple avenues for constructing and using approximations. We illustrate these on the 2-site Hubbard model. We connect our results to the more general case of non-perturbative time-dependence.

  PublisherOA PDFDOI

• Analyzing density-driven errors: Principles and pitfalls

  ChemRxiv · 2025-09-25

  article

  The theory of density-corrected density functional theory (DC-DFT) separates the error in any approximate DFT calculation into a functional-driven contribution and a density-driven error. Practical DC-DFT calculations often use the Hartree-Fock density instead of a self-consistent DFT density--a method known as HF-DFT--and reduce energetic errors in several classes of chemical problems. Using principles of DC-DFT, we illustrate several pitfalls when analyzing HF-DFT errors, including an interpolator for density-driven errors that is chronically inaccurate, using proxies instead of accurate densities, and conflating common measures of density errors with those of DC-DFT. We report ideal density-driven errors for one- and two-electron systems, where we can calculate most properties exactly, illustrating these problems. A simple analysis of benchmarking data shows that proxy densities proposed in recent literature are too inaccurate to be useful in DC-DFT. Despite recent claims to the contrary, we show that the success of DC-DFT for barrier heights does not rely on a cancellation of errors, and it is to be expected that HF-DFT errors can be smaller than functional errors, but we fall short of explaining why the improvement is so quantitatively systematic.

  PublisherDOI

Recent grants

• EAGER: Density functionals from Machine Learning

  NSF · $300k · 2012–2015

• Electron-molecule Collisions From Time-dependent Density Functional Theory

  NSF · $345k · 2004–2007

• Aiming for Chemical Accuracy in Ground-state Density Functional Theory

  NSF · $510k · 2022–2025

• Systematic approach to Density Functional Theory

  NSF · $542k · 2015–2019

• Electron-molecule Collisions From Time-dependent Density Functional Theory

  NSF · $42k · 2007–2008

Frequent coauthors

• John P. Perdew

  Temple University

  71 shared

• Adam Wasserman

  Purdue University West Lafayette

  49 shared

• Eunji Sim

  Yonsei University

  42 shared

• Peter Elliott

  Max-Born-Institute for Nonlinear Optics and Short Pulse Spectroscopy

  41 shared

• Morrel H. Cohen

  Princeton University

  39 shared

• Neepa T. Maitra

  31 shared

• Suhwan Song

  Yonsei University

  28 shared

• Aurora Pribram‐Jones

  University of California, Merced

  28 shared

Awards & honors

• Fellow of the American Physical Society
• Fellow of the Royal Society of Chemistry
• Fellow of the American Association for the Advancement of Sc…
• Member of the International Academy of Quantum Molecular Sci…