Agentic Discovery of Exchange-Correlation Density Functionals

• URL: http://arxiv.org/abs/2605.05460v1
• Date: Wed, 06 May 2026 21:36:43 GMT
• Title: Agentic Discovery of Exchange-Correlation Density Functionals
• Abstract summary: Development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT)<n>Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop.<n>This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history.
• Score: 6.373702912974026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human researchers combining physical insight, exact constraints, and empirical fitting. Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop. This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history. The system attempts to improve functional performance through an iterative plan-execute-summarize loop, where improvements are measurable by optimizing functional parameters against a standard thermochemistry dataset, then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a (Seed Agentic Functional Search 2026), improves upon the gold-standard ωB97M-V baseline by ~9%. These results also surface a cautionary lesson for AI-assisted science: models powerful enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts to game the benchmark; domain expertise translated into explicitly enforced constraints remains essential to keeping results scientifically grounded.

Related papers

• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• FunctionalAgent: Towards end-to-end on-top functional design [7.9012744471981184]
  FunctionalAgent orchestrates a team of specialized sub-agents to decompose the development process into dataset construction, active-space generation, MCSCF calculation and descriptor generation, loss-function construction, and functional fitting, optimization, and evaluation.<n>We develop MC26, a hybrid meta-GGA on-top functional that achieves overall accuracy on the training set compared with other methods evaluated on the same benchmark dataset.<n>We introduce COF26, a new functional form that, owing to the optimized training process, achieves the best performance on both the training and test sets.
  arXiv  Detail & Related papers  (2026-05-07T13:16:16Z)
• From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models [73.72877445629383]
  Interpretability-Guided Data Selection (IGDS) is a framework that first identifies causal task features through frequency recall and interventional filtering.<n>We validate IGDS on mathematical reasoning, summarization, and translation tasks within Gemma-2, LLaMA-3.1, and Qwen3 models.
  arXiv  Detail & Related papers  (2026-04-28T03:16:24Z)
• A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes [0.7646713951724009]
  We introduce FOCaL (Functional Outcome Causal Learning), a doubly robust meta-learner specifically engineered to estimate a functional heterogeneous treatment effect (F-CATE)<n> FOCaL integrates advanced functional regression techniques for both outcome modeling and functional pseudo-outcome reconstruction, thereby enabling the direct and robust estimation of F-CATE.<n> FOCaL advances the capabilities of machine intelligence to infer nuanced, individualized causal effects from complex data.
  arXiv  Detail & Related papers  (2026-02-11T18:31:59Z)
• Automatic Design of Optimization Test Problems with Large Language Models [0.0]
  We introduce Evolution of Test Functions (EoTF), a framework that automatically generates continuous optimization test functions whose landscapes match a target ELA feature vector.<n>EoTF produces non-trivial functions with closely matching ELA characteristics and preserves performance rankings under fixed evaluation budgets.<n>Overall, EoTF offers a practical route to scalable, portable, and interpretable benchmark generation properties.
  arXiv  Detail & Related papers  (2026-02-02T19:42:14Z)
• Accelerating Scientific Discovery with Autonomous Goal-evolving Agents [51.6546298853762]
  We introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to amend this challenge.<n>SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions.<n>We demonstrate the framework through a broad spectrum of applications, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design.
  arXiv  Detail & Related papers  (2025-12-25T20:54:41Z)
• FuncGenFoil: Airfoil Generation and Editing Model in Function Space [63.274584650021744]
  We introduce FuncGenFoil, a novel function-space generative model that directly reconstructs airfoil geometries as function curves.<n> Empirical evaluations demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation.
  arXiv  Detail & Related papers  (2025-02-15T07:56:58Z)
• On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimization [55.88862563823878]
  This study focuses on evaluating the efficiency of DM21 functional in predicting molecular geometries.<n>We implement geometry optimization in PySCF for the DM21 functional in geometry optimization problem.<n>Our findings reveal both the potential and the current challenges of using neural network functionals for geometry optimization in DFT.
  arXiv  Detail & Related papers  (2025-01-21T14:01:06Z)
• Adaptive LASSO estimation for functional hidden dynamic geostatistical model [69.10717733870575]
  We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hiddenstatistical models (f-HD) The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (GMSOLAS) penalty function, wherein the weights are obtained by the unpenalised f-HD maximum-likelihood estimators.
  arXiv  Detail & Related papers  (2022-08-10T19:17:45Z)
• Evolving symbolic density functionals [5.846106754490333]
  We propose a new framework, Symbolic Functional Evolutionary Search (SyFES), that automatically constructs accurate functionals in the symbolic form. Our framework opens a new direction in leveraging computing power for the systematic development of symbolic density functionals.
  arXiv  Detail & Related papers  (2022-03-03T07:02:26Z)
• eQE 2.0: Subsystem DFT Beyond GGA Functionals [58.720142291102135]
  subsystem-DFT (sDFT) can dramatically reduce the computational cost of large-scale electronic structure calculations. The key ingredients of sDFT are the nonadditive kinetic energy and exchange-correlation functionals which dominate it's accuracy. eQE 2.0 delivers excellent interaction energies compared to conventional Kohn-Sham DFT and CCSD(T)
  arXiv  Detail & Related papers  (2021-03-12T22:26:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.