ArticleNature communications2024
Analytical ab initio hessian from a deep learning potential for transition state optimization.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD.Science advances · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Generative flow model on distance geometry for predicting transition states of chemical reactions.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Learning potential energy surfaces of hydrogen atom transfer reactions in peptides.Digital discovery · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
- A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.Scientific data · 2025Article
- Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.Chemical science · 2025Review
- NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions.Journal of chemical theory and computation · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Including Quantum Effects in Molecular Simulations Using the Feynman-Kleinert Linearized Path Integral Method.Entropy (Basel, Switzerland) · 2025Review
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
- Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Identifying transition states-saddle points on the potential energy surface connecting reactant and product minima-is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2-3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.
Identifiers
What 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.