ArticleChemical science2025
AIQM2: organic reaction simulations beyond DFT.
Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Application of the String Method to Determine the Ambimodal Nature of Transition States in Bifurcation Reactions.Journal of computational chemistry · 2026Article
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 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
- Ultrafast Correlation Energy Estimator.The journal of physical chemistry. A · 2025Article
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Authors and funding
2 authors.
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Abstract
Density functional theory (DFT) is the workhorse of reaction simulations, but it often suffers from either prohibitive cost or insufficient accuracy. In this work, we report AIQM2-the universal AI-enhanced QM method 2-as the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the high speed of AIQM2, which is orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction.
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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.