# Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials

arXiv: https://arxiv.org/abs/2207.12858 · HTML: https://mlsanity.com/paper/2207.12858/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://mlsanity.com/paper/2102.03150/) (2021)
- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Machine learning for electronically excited states of molecules](https://mlsanity.com/paper/2007.05320/) (2020)
- [NeuralNEB -- Neural Networks can find Reaction Paths Fast](https://mlsanity.com/paper/2207.09971/) (2022)

## Built on by (most cited first, 30 of 37)

- [From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction](https://mlsanity.com/paper/2310.16802/) (2023)
- [Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model](https://mlsanity.com/paper/2304.06174/) (2023)
- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [HIP: Hessian Interatomic Potentials without derivatives](https://mlsanity.com/paper/2509.21624/) (2025)
- [Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces](https://mlsanity.com/paper/2305.16325/) (2023)
- [Accurate and scalable exchange-correlation with deep learning](https://mlsanity.com/paper/2506.14665/) (2025)
- [Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents](https://mlsanity.com/paper/2408.08006/) (2024)
- [On the design space between molecular mechanics and machine learning force fields](https://mlsanity.com/paper/2409.01931/) (2024)
- [React-OT: Optimal Transport for Generating Transition State in Chemical Reactions](https://mlsanity.com/paper/2404.13430/) (2024)
- [Implicit Neural Representations for Chemical Reaction Paths](https://mlsanity.com/paper/2502.15843/) (2025)
- [Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields](https://mlsanity.com/paper/2503.08674/) (2025)
- [Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches](https://mlsanity.com/paper/2510.06030/) (2025)
- [Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs](https://mlsanity.com/paper/2605.03964/) (2026)
- [nabla2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials](https://mlsanity.com/paper/2406.14347/) (2024)
- [Transferable Learning of Reaction Pathways from Geometric Priors](https://mlsanity.com/paper/2504.15370/) (2025)
- [Flow matching for reaction pathway generation](https://mlsanity.com/paper/2507.10530/) (2025)
- [FragmentFlow: Scalable Transition State Generation for Large Molecules](https://mlsanity.com/paper/2602.02310/) (2026)
- [Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs](https://mlsanity.com/paper/2605.13788/) (2026)
- [Generative Model for Constructing Reaction Path from Initial to Final States](https://mlsanity.com/paper/2401.10721/) (2024)
- [Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review](https://mlsanity.com/paper/2503.18975/) (2025)
- [Towards Faster and More Compact Foundation Models for Molecular Property Prediction](https://mlsanity.com/paper/2504.19538/) (2025)
- [Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data](https://mlsanity.com/paper/2506.21788/) (2025)
- [Coherent energy and force uncertainty in deep learning force fields](https://mlsanity.com/paper/2312.04174/) (2023)
- [OpenQDC: Open Quantum Data Commons](https://mlsanity.com/paper/2411.19629/) (2024)
- [On the Importance of Pretraining Data Alignment for Atomic Property Prediction](https://mlsanity.com/paper/2502.11085/) (2025)
- [LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models](https://mlsanity.com/paper/2505.22208/) (2025)
- [A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches](https://mlsanity.com/paper/2603.10992/) (2026)
- [Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding](https://mlsanity.com/paper/2603.11924/) (2026)
- [Hessian-informed machine learning interatomic potential towards bridging theory and experiments](https://mlsanity.com/paper/2603.25373/) (2026)
- [Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data](https://mlsanity.com/paper/2604.15380/) (2026)

## Abstract (arXiv)

Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due to the scarcity of training data in relevant transition state regions of chemical space. Currently, available datasets for training ML models on small molecular systems almost exclusively contain configurations at or near equilibrium. In this work, we present the dataset Transition1x containing 9.6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the wB97x/6-31G(d) level of theory. The data was generated by running Nudged Elastic Band (NEB) calculations with DFT on 10k reactions while saving intermediate calculations. We train state-of-the-art equivariant graph message-passing neural network models on Transition1x and cross-validate on the popular ANI1x and QM9 datasets. We show that ML models cannot learn features in transition-state regions solely by training on hitherto popular benchmark datasets. Transition1x is a new challenging benchmark that will provide an important step towards developing next-generation ML force fields that also work far away from equilibrium configurations and reactive systems.