An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
physics.chem-ph, cs.AI, stat.ML
Submitted: 2025-06-24
Updated: 2026-09-11
Journal ref: Nature Communications (2026)
DOI: 10.1038/s41467-026-76604-2
Code: https://github.com/microsoft/oneqmc
License: http://creativecommons.org/licenses/by/4.0/
The gist: Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireference character of the electronic structure in dissociating species.
Terminology
Abstract
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireference character of the electronic structure in dissociating species. Multireference methods in particular suffer from large computational cost, which under the normal paradigm has to be paid anew for each system at a full price, ignoring commonalities in electronic structure across molecules. Quantum Monte Carlo with deep neural networks uniquely offers to exploit such commonalities by pretraining transferable wavefunction models, but all such attempts were so far limited in scope. Here, we bring this paradigm to fruition with Orbformer, a transferable wavefunction model pretrained on 22,000 equilibrium and dissociating structures that can be fine-tuned on unseen molecules reaching an accuracy-cost ratio rivalling classical multireference methods. On established benchmarks as well as more challenging bond dissociations and Diels-Alder reactions, Orbformer is the only method that consistently converges to chemical accuracy (1 kcal/mol). This work turns the idea of amortizing the cost of solving the Schrödinger equation over many molecules into a practical approach in quantum chemistry.
Sources
- Better, Faster Fermionic Neural Networks
- A Self-Attention Ansatz for Ab-initio Quantum Chemistry
- Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
- Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
- Neural Pfaffians: Solving Many Many-Electron Schr\"odinger Equations
- Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
- Attention Is All You Need
- Learning Deep Transformer Models for Machine Translation
Related papers
- Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
- Accelerated "on-the-fly" coupled-cluster path-integral molecular dynamics: Impact of nuclear quantum effects on an asymmetric proton
- Variational Polaron Theory for Ground States of Strongly Coupled Light-Matter and Electron-Phonon Systems
- Pushing the accuracy of on-top functionals with agent-driven supervised learning
- Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
- Localized intrinsic bond orbitals decode correlated charge migration dynamics