ArticleNature communications2021
Artificial intelligence-enhanced quantum chemical method with broad applicability.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- The Newton-X platform for mixed quantum-classical dynamics.Physical chemistry chemical physics : PCCP · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- What will be the future of computational biology for macromolecules in the era of AI?PLoS computational biology · 2026Article
- Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks.ACS central science · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- Photochemical N-dealkylation of Tertiary Amines Coupled With Photocharging of Poly(Heptazine Imides).Angewandte Chemie (International ed. in English) · 2026Article
- AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.Angewandte Chemie (International ed. in English) · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism Controversy.Journal of chemical theory and computation · 2026Article
- Real-space machine learning of correlation density functionals.Nature communications · 2025Article
- Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data-Driven Approaches with Traditional Computational Chemistry Algorithms.ChemistryOpen · 2025Article
- Thermodynamic insights into the interplay between calcium and iron(ii) hydroxycarboxylates: impacts on solubility, speciation, and bioavailability.RSC advances · 2025Article
- Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations.Journal of chemical theory and computation · 2025Article
- Ultrafast Correlation Energy Estimator.The journal of physical chemistry. A · 2025Article
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence-quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C
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Registered trials
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