$Δ$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions

• URL: http://arxiv.org/abs/2511.17753v1
• Date: Fri, 21 Nov 2025 20:10:29 GMT
• Title: $Δ$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions
• Abstract summary: We propose a framework based on an ensemble of $$-ML models trained on features extracted from a pre-trained atom-pairwise neural network.<n>Our results show that the proposed framework achieves very small mean-absolute-errors below 0.1 kcal/mol regardless of the given method.
• Score: 2.0837295518447934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ab initio quantum chemical methods for accurately computing interactions between molecules have a wide range of applications but are often computationally expensive. Hence, selecting an appropriate method based on accuracy and computational cost remains a significant challenge due to varying performance of methods. In this work, we propose a framework based on an ensemble of $Δ$-ML models trained on features extracted from a pre-trained atom-pairwise neural network to predict the error of each method relative to all other methods including the ``gold standard'' coupled cluster with single, double, and perturbative triple excitations at the estimated complete basis set limit [CCSD(T)/CBS]. Our proposed approach provides error estimates across various levels of theories and identifies the computationally efficient approach for a given error range utilizing only a subset of the dataset. Further, this approach allows comparison between various theories. We demonstrate the effectiveness of our approach using an extended BioFragment dataset, which includes the interaction energies for common biomolecular fragments and small organic dimers. Our results show that the proposed framework achieves very small mean-absolute-errors below 0.1 kcal/mol regardless of the given method. Furthermore, by analyzing all-to-all $Δ$-ML models for present levels of theory, we identify method groupings that align with theoretical hypotheses, providing evidence that $Δ$-ML models can easily learn corrections from any level of theory to any other level of theory.

Related papers

• Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
  Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
  arXiv  Detail & Related papers  (2026-08-25T15:07:11Z)
• Sharper Analysis of Single-Loop Methods for Bilevel Optimization [81.36947969465848]
  We establish sharper convergence results for single-loop approximate implicit differentiation (AID) and iterative differentiation (ITD) methods.<n>We prove that the error is $mathcalO(2)$, exactly matching the known lower bound and improving upon the previous $mathcalO(3)$ guarantee.
  arXiv  Detail & Related papers  (2026-07-11T11:41:42Z)
• 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)
• Self-Refining Training for Amortized Density Functional Theory [5.5541132320126945]
  We propose a novel method that reduces the dependency of amortized DFT solvers on large pre-collected datasets by introducing a self-refining training strategy.<n>We derive our method as a minimization of the variational upper bound on the KL-divergence measuring the discrepancy between the generated samples and the target Boltzmann distribution defined by the ground state energy.
  arXiv  Detail & Related papers  (2025-06-02T00:32:32Z)
• Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts [64.34482582690927]
  We provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models.<n>We propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality.
  arXiv  Detail & Related papers  (2025-03-04T17:46:51Z)
• A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility [5.763661159910719]
  Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors.
  arXiv  Detail & Related papers  (2024-09-06T14:20:38Z)
• Benchmark Computations of Nearly Degenerate Singlet and Triplet states of N-heterocyclic Chromophores : II. Density-based Methods [0.0]
  A set of functionals with the least mean absolute error (MAE) is proposed for both the approaches, LR-TDDFT and $Delta$SCF. We have based our findings on extensive studies of three cyclazine-based molecular templates. The role of exact-exchange, spin-contamination and spin-polarization in the context of DFT comes to the forefront in our studies.
  arXiv  Detail & Related papers  (2024-08-09T07:47:38Z)
• E$^2$M: Double Bounded $α$-Divergence Optimization for Tensor-based Discrete Density Estimation [3.9633191508712398]
  We present a generalization of the expectation-maximization (EM) algorithm, called E$2M algorithm.<n>It circumvents this issue by first relaxing the optimization into minimization of a surrogate objective based on the Kullback-Leibler (KL) divergence.<n>Our approach offers flexible modeling for not only a variety of low-rank structures, including the CP, Tucker, and Train formats.
  arXiv  Detail & Related papers  (2024-05-28T14:28:28Z)
• Sampling with Mollified Interaction Energy Descent [57.00583139477843]
  We present a new optimization-based method for sampling called mollified interaction energy descent (MIED) MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs) We show experimentally that for unconstrained sampling problems our algorithm performs on par with existing particle-based algorithms like SVGD.
  arXiv  Detail & Related papers  (2022-10-24T16:54:18Z)
• Pseudo-Spherical Contrastive Divergence [119.28384561517292]
  We propose pseudo-spherical contrastive divergence (PS-CD) to generalize maximum learning likelihood of energy-based models. PS-CD avoids the intractable partition function and provides a generalized family of learning objectives.
  arXiv  Detail & Related papers  (2021-11-01T09:17:15Z)
• Learning Mean-Field Equations from Particle Data Using WSINDy [0.0]
  We develop a weak-form sparse identification method for interacting particle systems (IPS) We use concepts from mean-field theory of IPS in combination with the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy) Our examples include a canonical problem from homogenization theory, the dynamics of an attractive-repulsive swarm, and the IPS description of the parabolic-elliptic Keller-Segel model for chemotaxis.
  arXiv  Detail & Related papers  (2021-10-14T22:40:01Z)
• Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
  We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations. Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
  arXiv  Detail & Related papers  (2021-09-07T20:01:22Z)
• Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions [79.35722941720734]
  Generalised linear models for multi-class classification problems are one of the fundamental building blocks of modern machine learning tasks. We prove exacts characterising the estimator in high-dimensions via empirical risk minimisation. We discuss how our theory can be applied beyond the scope of synthetic data.
  arXiv  Detail & Related papers  (2021-06-07T16:53:56Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

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.