ReviewNature reviews. Chemistry2023
Ab initio quantum chemistry with neural-network wavefunctions.
Review in Nature reviews. Chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention.ACS physical chemistry Au · 2026Article
- An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking.Nature communications · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations.Journal of chemical theory and computation · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds.Journal of chemical theory and computation · 2025Article
- Physics-informed transformers for electronic quantum states.Nature communications · 2025Article
- Ultrafast Correlation Energy Estimator.The journal of physical chemistry. A · 2025Article
- Quantum-Assisted Variational Monte Carlo.Precision chemistry · 2025Article
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet.Nano letters · 2025Article
- Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians.Nature communications · 2025Article
- Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.Journal of chemical theory and computation · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Representing Born effective charges with equivariant graph convolutional neural networks.Scientific reports · 2025Article
- Double nanowire quantum dots and machine learning.Scientific reports · 2025Article
- Artificial-Intelligence Driven Precision Chemistry.Precision chemistry · 2025Article
- Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations.Communications physics · 2025Article
- Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation.Nature communications · 2024Article
- Analytical approach to structural chemistry origins of mechanical, acoustical and thermal properties.National science review · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations. Although still at their infancy, these methods can already generate virtually exact solutions of the electronic Schrödinger equation for small systems and rival advanced conventional QC methods for systems with up to a few dozen electrons.
Identifiers
37558761What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.