/Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery

Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery Awesome

A curated list of papers and resources about our survey paper:

Contents

Datasets

  • Catalysis-Hub. org, an open electronic structure database for surface reactions (Scientific data, 2019) [paper]
  • The Open catalyst 2020 (OC20) dataset and community challenges (Acs Catalysis, 2021) [paper]
  • The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts (ACS Catalysis, 2023) [paper]

Existing Surveys in Catalyst Discovery

  • Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists (Chemical Reviews, 2019) [paper]
  • High-throughput experimentation meets artificial intelligence - a new pathway to catalyst discovery (Physical Chemistry Chemical Physics, 2020) [paper]
  • Automated in Silico Design of Homogeneous Catalysts (ACS Catalysis, 2020) [paper]
  • Data-Driven Design of Electrocatalysts - Principle, Progress, and Perspective (Journal of Materials Chemistry A, 2023) [paper]
  • Bridging the complexity gap in computational heterogeneous catalysis with machine learning (Nature Catalysis, 2023) [paper]

A Brief Summary of Related Surveys

Table 1 in our survey paper.

Open Platforms

  • Open Catalyst Project: Provide datasets, baseline models, and a public leaderboard
  • ASE: An open-source package for setting up, manipulating, running, visualizing, and analyzing atomistic simulations
  • LCMD: A platform that aggregates tools and databases for the digital optimization and discovery of catalysts
  • RDKit: Offer a comprehensive suite of tools for cheminformatics, supporting both 2D and 3D molecular operations

Existing Works in both Homogeneous and Heterogeneous Catalyst Discovery (with either Direct or Inverse designs)

Classical Methods in Catalysts

  • An adaptive machine learning strategy for accelerating discovery of perovskite electrocatalysts (ACS Catalysis, 2020) [paper]
  • Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts (Nature communications, 2020) [paper]
  • Exploring chemical and conformational spaces by batch mode deep active learning (Digital Discovery, 2022) [paper]
  • Accelerating atomic catalyst discovery by theoretical calculations-machine learning strategy (Advanced Energy Materials, 2020) [paper]
  • Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides (Nature Communications, 2022) [paper]
  • Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (Nature Communications, 2023) [paper]
  • Automated and Intelligent Synthesis of Oxygen-Producing Catalysts from Martian Meteorites by Robotic AI-Chemist [paper]
  • Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning (Nature Communications, 2023) [paper]
  • Machine-learning-accelerated design of high-performance platinum intermetallic nanoparticle fuel cell catalysts(Nature Communications, 2024) [paper]

Generative and Reinforcement Learning in Catalysts

  • Navigating through the maze of homogeneous catalyst design with machine learning (Trends in Chemistry, 2021) [paper]
  • Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics (Scientific Reports, 2022) [paper]
  • AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design. (NeurIPS, 2023) [paper]
  • Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions (Digital discovery, 2023) [paper]

Graph Neural Networks in Catalysts

  • Gemnet: Universal directional graph neural networks for molecules (NeurIPS, 2021) [paper]
  • Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations (ICLR, 2022) [paper]
  • Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion (Catalysis Science & Technology, 2022) [paper]
  • Dr-label: Improving gnn models for catalysis systems by label deconstruction and reconstruction (arXiv, 2023) [paper]
  • Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO2 Reduction (ACS Catalysis, 2023) [paper]
  • CURATOR: Autonomous Batch Active-Learning Workflow for Catalysts (NeurIPS, 2023) [paper]
  • Boosting heterogeneous catalyst discovery by structurally constrained deep learning models (Materials Today Chemistry, 2023) [paper]
  • Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks (Nature communications, 2023) [paper]
  • Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language (Nature Communications, 2023) [paper]
  • Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach (NeurIPS, 2024) [paper]
  • PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design (JMLR, 2024) [paper]

Large Language Models in Catalysts

  • Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design (EMNLP, 2023) [paper]
  • Artificial Intelligence (AI) Workflow for Catalyst Design and Optimization (Industrial & Engineering Chemistry Research, 2023)[paper]
  • Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models (ACS Catalysis, 2023) [paper]
  • Augmenting large language models with chemistry tools (Nature Machine Intelligence, 2024) [paper]
  • CO2 Reduction beyond Copper-Based Catalysts: A Natural Language Processing Review from the Scientific Literature (ACS Sustainable Chemistry & Engineering, 2024) [paper]

Other Related Works

  • Constrained Graph Variational Autoencoders for Molecule Design (NeurIPS, 2018) [paper]
  • GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (SIGKDD, 2020) [paper]
  • MoFlow: An Invertible Flow Model for Generating Molecular Graphs (ICLR, 2020) [paper]
  • Multi-objective molecule generation using interpretable substructures (ICML, 2020) [paper]
  • E(n) Equivariant Normalizing Flows (NeurIPS, 2021) [paper]
  • A 3D Generative Model for Structure-Based Drug Design (NeurIPS, 2021) [paper]
  • Learning Neural Generative Dynamics for Molecular Conformation Generation (ICLR, 2021) [paper]
  • Equivariant Diffusion for Molecule Generation in 3D (ICML, 2022) [paper]
  • Generating 3D Molecules for Target Protein Binding (ICML, 2022) [paper]
  • GeoLDM: Geometric Latent Diffusion Models for 3D Molecule Generation (ICML, 2023) [paper]
  • Optimization of base-catalyzed ethanolysis of sunflower oil by regression and artificial neural network models (Fuel processing technology, 2013) [paper]
  • Optimization of ultrasound-assisted base-catalyzed methanolysis of sunflower oil using response surface and artifical neural network methodologies (Chemical Engineering Journal, 2013) [paper]
  • Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter (Renewable Energy, 2015) [paper]
  • Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst (Renewable Energy, 2022) [paper]

Contribution

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Citations

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