mlsanityData to 7 Oct 2026

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

arXiv 2211.16486 ↗First posted 29 Nov 2022cond-mat.mtrl-sci cs.LGcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 11 of its 61 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

11 of 61 references.

References · 11

  1. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  2. 2020Directional Message Passing for Molecular Graphscited by 556
  3. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  4. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  5. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  6. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  7. 2022GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasetscited by 61
  8. 2021Rotation Invariant Graph Neural Networks using Spin Convolutionscited by 52
  9. 2022Spherical Channels for Modeling Atomic Interactionscited by 47
  10. 2020An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storagecited by 45
  11. 2022Learned Force Fields Are Ready For Ground State Catalyst Discoverycited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 11; 2026: 7.

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  1. 2025UMA: A Family of Universal Models for Atomscited by 99
  2. 2025Learning Smooth and Expressive Interatomic Potentials for Physical Property Predictioncited by 79
  3. 2024AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusioncited by 8
  4. 2024Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agentcited by 8
  5. 2025AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysiscited by 4
  6. 2024Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distancescited by 3
  7. 2024nabla2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentialscited by 2
  8. 2024AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sitescited by 2
  9. 2026Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twincited by 2
  10. 2024Adapting OC20-trained EquiformerV2 Models for High-Entropy Materialscited by 1
  11. 2025Cross-functional transferability in universal machine learning interatomic potentialscited by 1
  12. 2025Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysiscited by 1
13 more
  1. 2026AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfacescited by 1
  2. 2024Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learningcited by 0
  3. 2025Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearitycited by 0
  4. 2025MoMa: A Modular Deep Learning Framework for Material Property Predictioncited by 0
  5. 2025Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformationcited by 0
  6. 2025Universal crystal material property prediction via multi-view geometric fusion in graph transformerscited by 0
  7. 2025Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modelingcited by 0
  8. 2025Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structurescited by 0
  9. 2026AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placementcited by 0
  10. 2026Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learningcited by 0
  11. 2026PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interfacecited by 0
  12. 2026Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentialscited by 0
  13. 2026The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistrycited by 0