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

• URL: http://arxiv.org/abs/2211.16486v3
• Date: Fri, 15 Sep 2023 19:56:43 GMT
• Title: AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
• Authors: Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi
• Abstract summary: We show 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.
• Score: 8.636519538557001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent [5.812284760539713]
  Adsorb-Agent is a Large Language Model (LLM) agent designed to efficiently derive system-specific stable adsorbate-catalyst configurations. We demonstrate its performance using two example systems, NNH-CuPd3 (111) and NNH-Mo3Pd (111), for the Nitrogen Reduction Reaction (NRR), a sustainable alternative to the Haber-Bosch process.
  arXiv  Detail & Related papers  (2024-10-22T03:19:16Z)
• Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning [17.00084254889438]
  High-performance catalysts are crucial for sustainable energy conversion and human health. The discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimensional structure and composition spaces.
  arXiv  Detail & Related papers  (2024-04-18T18:11:06Z)
• Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials [0.0]
  We propose a rapid method for predicting HEA properties using data from monometallic systems. We developed high-precision models by employing both classical and quantum machine learning. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts.
  arXiv  Detail & Related papers  (2024-04-12T11:54:06Z)
• Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery [0.0]
  Open Catalyst Project aims to apply advances in graph neural networks (GNNs) to accelerate progress in catalyst discovery. By implementing robust design patterns like geometric and symmetric message passing, we were able to train a GNN model that reached a MAE of 0.0748 in predicting the per-atom forces of adsorbate-surface interactions.
  arXiv  Detail & Related papers  (2024-04-05T17:13:51Z)
• AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design [0.0]
  A central challenge of the clean energy transition is the development of catalysts for low-emissions technologies. Recent advances in Machine Learning for quantum chemistry drastically accelerate the computation of catalytic activity descriptors. Here we introduce AdsorbRL, a Deep Reinforcement Learning agent aiming to identify potential catalysts given a multi-objective binding energy target.
  arXiv  Detail & Related papers  (2023-12-04T19:44:04Z)
• PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design [102.9593507372373]
  Catalyst materials play a crucial role in the electrochemical reactions involved in industrial processes. Machine learning holds the potential to efficiently model materials properties from large amounts of data. We propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-11-22T05:24:30Z)
• Faster spectral density calculation using energy moments [77.34726150561087]
  We reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian. One of the main advantages of this framework is that it allows for an important reduction of the computational cost.
  arXiv  Detail & Related papers  (2022-11-01T23:57:58Z)
• Exploring the role of parameters in variational quantum algorithms [59.20947681019466]
  We introduce a quantum-control-inspired method for the characterization of variational quantum circuits using the rank of the dynamical Lie algebra. A promising connection is found between the Lie rank, the accuracy of calculated energies, and the requisite depth to attain target states via a given circuit architecture.
  arXiv  Detail & Related papers  (2022-09-28T20:24:53Z)
• Learned Force Fields Are Ready For Ground State Catalyst Discovery [60.41853574951094]
  We present evidence that learned density functional theory (DFT'') force fields are ready for ground state catalyst discovery. Key finding is that relaxation using forces from a learned potential yields structures with similar or lower energy to those relaxed using the RPBE functional in over 50% of evaluated systems. We show that a force field trained on a locally harmonic energy surface with the same minima as a target DFT energy is also able to find lower or similar energy structures in over 50% of cases.
  arXiv  Detail & Related papers  (2022-09-26T07:16:43Z)
• Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent [79.58680275615752]
  We propose an energy-efficient federated meta-learning framework. We assume each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model.
  arXiv  Detail & Related papers  (2021-05-31T08:15:44Z)
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference [56.24109486973292]
  We study the interplay between pruning and quantization during the training of neural networks for ultra low latency applications. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task.
  arXiv  Detail & Related papers  (2021-02-22T19:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.