# AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

arXiv: https://arxiv.org/abs/2211.16486 · HTML: https://mlsanity.com/paper/2211.16486/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://mlsanity.com/paper/2102.03150/) (2021)
- [Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules](https://mlsanity.com/paper/2011.14115/) (2020)
- [The Open Catalyst 2020 (OC20) Dataset and Community Challenges](https://mlsanity.com/paper/2010.09990/) (2020)
- [The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts](https://mlsanity.com/paper/2206.08917/) (2022)
- [GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets](https://mlsanity.com/paper/2204.02782/) (2022)
- [Rotation Invariant Graph Neural Networks using Spin Convolutions](https://mlsanity.com/paper/2106.09575/) (2021)
- [Spherical Channels for Modeling Atomic Interactions](https://mlsanity.com/paper/2206.14331/) (2022)
- [An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage](https://mlsanity.com/paper/2010.09435/) (2020)
- [Learned Force Fields Are Ready For Ground State Catalyst Discovery](https://mlsanity.com/paper/2209.12466/) (2022)

## Built on by (most cited first, 26 of 26)

- [UMA: A Family of Universal Models for Atoms](https://mlsanity.com/paper/2506.23971/) (2025)
- [Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction](https://mlsanity.com/paper/2502.12147/) (2025)
- [AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion](https://mlsanity.com/paper/2405.03962/) (2024)
- [Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent](https://mlsanity.com/paper/2410.16658/) (2024)
- [AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis](https://mlsanity.com/paper/2510.22938/) (2025)
- [Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances](https://mlsanity.com/paper/2407.10844/) (2024)
- [nabla2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials](https://mlsanity.com/paper/2406.14347/) (2024)
- [AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites](https://mlsanity.com/paper/2409.16204/) (2024)
- [Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin](https://mlsanity.com/paper/2606.05050/) (2026)
- [Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials](https://mlsanity.com/paper/2403.09811/) (2024)
- [Cross-functional transferability in universal machine learning interatomic potentials](https://mlsanity.com/paper/2504.05565/) (2025)
- [Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis](https://mlsanity.com/paper/2510.06632/) (2025)
- [AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces](https://mlsanity.com/paper/2606.19152/) (2026)
- [Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning](https://mlsanity.com/paper/2404.12445/) (2024)
- [Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity](https://mlsanity.com/paper/2502.07293/) (2025)
- [MoMa: A Modular Deep Learning Framework for Material Property Prediction](https://mlsanity.com/paper/2502.15483/) (2025)
- [Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation](https://mlsanity.com/paper/2504.02848/) (2025)
- [Universal crystal material property prediction via multi-view geometric fusion in graph transformers](https://mlsanity.com/paper/2507.15303/) (2025)
- [Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling](https://mlsanity.com/paper/2510.22123/) (2025)
- [Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures](https://mlsanity.com/paper/2512.15228/) (2025)
- [AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement](https://mlsanity.com/paper/2602.19289/) (2026)
- [Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning](https://mlsanity.com/paper/2605.04102/) (2026)
- [PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface](https://mlsanity.com/paper/2605.13297/) (2026)
- [Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials](https://mlsanity.com/paper/2609.00528/) (2026)
- [The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry](https://mlsanity.com/paper/2609.05643/) (2026)
- [Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials](https://mlsanity.com/paper/2610.19630/) (2026)

## Abstract (arXiv)

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a 2000x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 100,000 unique configurations.