Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

• URL: http://arxiv.org/abs/2603.25381v1
• Date: Thu, 26 Mar 2026 12:31:30 GMT
• Title: Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
• Abstract summary: A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES)<n>Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians.<n>This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character.
• Score: 7.308984085522867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schrödinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sparsely, with local approximations constructed by estimating VMC energies and forces at sampled geometries and aggregating the resulting noisy data using Gaussian process regression. Our method enables accurate and efficient exploration of complex PES landscapes, including structure relaxation, transition-state searches, and minimum-energy pathways, for both ground and excited states. This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character.

Related papers

• Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems [1.476367559174889]
  We present a graph-theoretic molecular fragmentation framework integrated with machine learning to model post-Hartree-Fock nuclear forces.<n>Our framework successfully reproduced complex dynamical signatures and key structural characteristics.<n>Ultimately, this scalable, systematically improvable framework bridges the gap between high-level correlated wavefunction theories and long-timescale reactive sampling.
  arXiv  Detail & Related papers  (2026-07-23T19:49:22Z)
• Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data [41.531262858699925]
  We develop a scalable hypothesis-refinement paradigm that tightly integrates spectral evidence with large-scale molecular priors.<n>We introduce textbfSpectroMol, a spectrum-to-structure model that proposes chemically valid molecular hypotheses conditioned on multimodal spectral inputs.<n>We also develop textbfMS-Mol2Mol, a high-resolution mass-constrained molecular generator that integrates molecular formula, exact mass, and degree of unsaturation.
  arXiv  Detail & Related papers  (2026-07-22T06:50:53Z)
• Advancing Practical Quantum Embedding Simulations via Operator Commutativity Based State Preparation for Complex Chemical Systems [0.0]
  Hybrid quantum-classical algorithms implemented on quantum computers offer a promising route toward addressing this problem.<n>We propose a dynamic ansatz construction strategy based on operator commutativity and energy driven screening within density matrix embedding theory.<n>We demonstrate improved accuracy and significantly reduced quantum gate requirements compared with conventional ansatze.
  arXiv  Detail & Related papers  (2026-04-21T13:51:55Z)
• Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure [6.554055153293176]
  We introduce a quantum-adapted extension to the Landau-Zener-Surface-Hopping NAMD.<n>We also develop a sub-microhartree-accurate PES calculation protocol.<n>This work paves the way for practical application of quantum computing in NAMD.
  arXiv  Detail & Related papers  (2025-12-02T03:34:00Z)
• MolSight: Optical Chemical Structure Recognition with SMILES Pretraining, Multi-Granularity Learning and Reinforcement Learning [47.029225594084345]
  MolSight is a comprehensive learning framework that employs a three-stage training paradigm.<n>We show that MolSight achieves state-of-the-art performance in (stereo)chemical optical structure recognition.
  arXiv  Detail & Related papers  (2025-11-21T15:11:47Z)
• Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo [4.437335677401287]
  We introduce a novel method for the geometrically transferable optimization of neural network wave functions.<n>Our method enables the efficient prediction of ground and excited-state PESs and their intersections at the highest accuracy.<n>We validate our approach on three challenging excited-state PESs, including ethylene, the carbon dimer, and the methylenimmonium cation.
  arXiv  Detail & Related papers  (2025-03-25T17:12:29Z)
• Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation [41.94295877935867]
  We introduce a variational approach for fermionic time-dependent wave functions, surpassing mean-field approximations. We use time-dependent Jastrow factors and backflow transformations, which are enhanced through neural networks parameterizations. The results showcase the ability of our variational approach to accurately capture the time evolution, providing insight into the quantum dynamics of interacting electronic systems.
  arXiv  Detail & Related papers  (2024-03-12T09:37:22Z)
• Force-free identification of minimum-energy pathways and transition states for stochastic electronic structure theories [0.18641315013048293]
  We present strategies that utilize the surrogate Hessian line-search method to efficiently identify MEP and TS structures. We show that it is possible to identify MEPs and TSs using a force-free Quantum Monte Carlo approach.
  arXiv  Detail & Related papers  (2024-02-20T17:58:43Z)
• Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond [33.54862439531144]
  Development of reliable and molecular mechanics (MM) force fields is indispensable for biomolecular simulation and computer-aided drug design. We introduce a generalized and machine-learned MM force field, ttexttespaloma-0.3, and an end-to-end differentiable framework using graph neural networks. The force field reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids.
  arXiv  Detail & Related papers  (2023-07-13T23:00:22Z)
• Enhanced Sampling of Configuration and Path Space in a Generalized Ensemble by Shooting Point Exchange [71.49868712710743]
  We propose a new approach to simulate rare events caused by transitions between long-lived states. The scheme substantially enhances the efficiency of the transition path sampling simulations. It yields information on thermodynamics, kinetics and reaction coordinates of molecular processes without distorting their dynamics.
  arXiv  Detail & Related papers  (2023-02-17T08:41:31Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Local manifold learning and its link to domain-based physics knowledge [53.15471241298841]
  In many reacting flow systems, the thermo-chemical state-space is assumed to evolve close to a low-dimensional manifold (LDM) We show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space.
  arXiv  Detail & Related papers  (2022-07-01T09:06:25Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Entropic trust region for densest crystallographic symmetry group packings [0.8399688944263843]
  Molecular crystal structure prediction seeks the most stable periodic structure given the chemical composition of a molecule and pressure-temperature conditions. Modern CSP solvers use global optimization methods to search for structures with minimal free energy within a complex energy landscape induced by intermolecular potentials. We propose a class of periodic packings restricted to crystallographic symmetry groups (CSG) and design a search method for the densest CSG packings in an information-geometric framework.
  arXiv  Detail & Related papers  (2022-02-24T08:39:07Z)

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.