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RESEARCH ARTICLE · Nature Machine Intelligence · 2021

Inverse design of nanoporous crystalline reticular materials with deep generative models

Zhenpeng Yao, Benjamín Sánchez-Lengeling, N. Scott Bobbitt, Benjamin J. Bucior, Sai Govind Hari Kumar, Sean P. Collins, Thomas D. Burns, Tom K. Woo, Omar K. Farha, Randall Q. Snurr, Alán Aspuru‐Guzik

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Inverse design of nanoporous crystalline reticular materials with deep generative modelsOriginalPredicting Hydrogen Storage in Mofs via Machine Learning1Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry13Applications of machine learning in metal-organic frameworks225Understanding the diversity of the metal-organic framework ecosystem615The Role of Machine Learning in the Understanding and Design of Materials433Machine Learning for Electronically Excited States of Molecules398Bayesian optimization of nanoporous materials112Breaking the upper bound of siloxane uptake: metal–organic frameworks as an adsorbent platform25Engineering metal–organic frameworks for adsorption-based gas separations: from process to atomic scale64Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity40First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning7Generative deep learning as a tool for inverse design of high entropy refractory alloys50Generative models for inverse design of inorganic solid materials38Golem: an algorithm for robust experiment and process optimization38Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review306The application of machine learning for predicting the methane uptake and working capacity of MOFs31Data-Driven Strategies for Accelerated Materials Design473Gas Adsorption Enhancement in Partially Amorphized Metal–Organic Frameworks24Polymer-Coated NH2-UiO-66 for the Codelivery of DOX/pCRISPR122Data Science in Chemical Engineering: Applications to Molecular Science20A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Toward smart carbon capture with machine learning121Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning66Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks105Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240stk : An extendable Python framework for automated molecular and supramolecular structure assembly and discovery58JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design17Predicting hydrogen storage in MOFs via machine learning181Computational discovery of energy materials in the era of big data and machine learning: A critical review60Efficient water desalination with graphene nanopores obtained using artificial intelligence92Nanoparticle synthesis assisted by machine learning545Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Machine Learning: New Ideas and Tools in Environmental Science and Engineering945Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment22polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics79Deep learning framework for material design space exploration using active transfer learning and data augmentation178Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25Machine learning-based discovery of molecules, crystals, and composites: A perspective review16Inverse design of glass structure with deep graph neural networks55Applied Machine Learning for Developing Next‐Generation Functional Materials72A graph theoretic representation and analysis of zeolite frameworks20Machine learning for the design and discovery of zeolites and porous crystalline materials41Diversifying databases of metal organic frameworks for high-throughput computational screening0An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design3Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design19Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning103Machine learning for multiscale modeling in computational molecular design25Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization26Multiscale Graph Attention Neural Networks for Mapping Materials and Molecules beyond Short-Range Interatomic Correlations0Modeling Multicomponent Gas Adsorption in Nanoporous Materials with Two Versions of Nonlocal Classical Density Functional Theory7Building Unit Extractor for Metal–Organic Frameworks9Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures22Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Inverse design of two-dimensional materials with invertible neural networks69Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening141Artificial intelligence: machine learning for chemical sciences104Tunable Electrical Conductivity of Flexible Metal–Organic Frameworks22Algorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space0Application of transfer learning to predict diffusion properties in metal–organic frameworks18Metal-Organic Framework Targeting for Optimal Pressure Swing Adsorption Processes0Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN4Customizing Mems Designs via Conditional Generative Adversarial Networks11Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65Representations and strategies for transferable machine learning improve model performance in chemical discovery19Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Ensemble Machine‐Learning‐Based Analysis for In Situ Electron Diffraction12Inverse Design of Materials by Machine Learning137Autonomous high-throughput computations in catalysis29Machine Learning–Assisted Design of Material Properties80Machine Learning for the Discovery, Design, and Engineering of Materials66Accelerated discovery of porous materials for carbon capture by machine learning: A review19Predicting chemical ecotoxicity by learning latent space chemical representations19Graph neural network predictions of metal organic framework CO 2 adsorption properties93Data‐Driven Materials Innovation and Applications187Deep learning to catalyze inverse molecular design29Materials genome engineering: a promising approach for the development of high-performance metal–organic frameworks9Evaluation guidelines for machine learning tools in the chemical sciences180Integrated metal‐organic framework ( MOF ) and pressure/vacuum swing adsorption process design: MOF matching24Inverse design with deep generative models: next step in materials discovery44Human–Machine Interface1Learning interpretable representations of entanglement in quantum optics experiments using deep generative models22Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks75Data-driven multi-objective molecular design of ionic liquid with high generation efficiency on small dataset17Materials Discovery With Machine Learning and Knowledge Discovery40Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr18Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model84Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Metal–organic framework derived single-atom catalysts for CO2 conversion to methanol31Machine-learning-assisted exploration of anion-pillared metal organic frameworks for gas separation31Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Battery Materials Discovery and Smart Grid Management using Machine Learning8Reproducible sorbent materials foundry for carbon capture at scale4ParticleGrid: Enabling Deep Learning using 3D Representation of Materials1SELFIES and the future of molecular string representations261Into the Unknown: How Computation Can Help Explore Uncharted Material Space65De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models112Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges9Machine learning for a sustainable energy future517Assessing Deep Generative Models in Chemical Composition Space34MOFTransformer: A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks0A Self-Evolutionary Methodology for Reverse Design of Novel MOFs9Atomic structure generation from reconstructing structural fingerprints10Crystal twins: self-supervised learning for crystalline material property prediction72Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Graph neural networks for materials science and chemistry860High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO2RR81A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation24Reaching the Full Potential of Machine Learning in Mitigating Environmental Impacts of Functional Materials6Accelerated chemical space search using a quantum-inspired cluster expansion approach25Inverse design of metal–organic frameworks for C2H4/C2H6 separation29Computational Design of Metal–Organic Frameworks with Unprecedented High Hydrogen Working Capacity and High Synthesizability42Computational and data-driven modelling of solid polymer electrolytes21Deep generative design of porous organic cages via a variational autoencoder18Encapsulation of cobaltocenium ions in a zeolite-like metal–organic framework8Generative organic electronic molecular design informed by quantum chemistry21Metal–organic framework clustering through the lens of transfer learning11Optimal Design of Anion-pillared Metal-organic Frameworks for Gas Separation0Reticular framework materials for photocatalytic organic reactions262Reticular materials for wastewater treatment22The Illusion of Novelty in Generative Materials Models0Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics73ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning150Big Data Science in Nanoporous Materials0Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development5Overview of AI in the Understanding and Design of Nanoporous Materials2Prediction of methane adsorption isotherms in metal–organic frameworks by neural network synergistic with classical density functional theory27Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials55Is GPT-3 all you need for low-data discovery in chemistry?26Polymer Informatics at Scale with Multitask Graph Neural Networks123Advanced electrocatalytic technologies for conversion of carbon dioxide into methanol by electrochemical reduction: Recent progress and future perspectives87Inverse Hamiltonian design by automatic differentiation14Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks208Constructing Microstructural Evolution System for Cement Hydration From Observed Data Using Deep Learning16Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Representations of Materials for Machine Learning133CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks40Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning3PMTransformer: Universal Transfer Learning and Cross-material Few-shot Learning in Porous Materials1Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication52Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification91Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening39Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation85High-throughput and machine learning approaches for the discovery of metal organic frameworks18AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122

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