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RESEARCH PAPER · Chemical Reviews · 2020

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit

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Big-Data Science in Porous Materials: Materials Genomics and Machine LearningOriginalStandard Practices of Reticular Chemistry240Understanding the diversity of the metal-organic framework ecosystem615Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes74Digital Reticular Chemistry171Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution133The Role of Machine Learning in the Understanding and Design of Materials433The Reticular Chemist47Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow41Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database352Accelerated discovery of stable spinels in energy systems via machine learning56Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow95Bayesian optimization of nanoporous materials112Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules113Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning57Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review306Post-synthetically modified metal–organic frameworks for sensing and capture of water pollutants43Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment48Digital Transformation in Materials Science: A Paradigm Change in Material's Development72Anharmonic Correction to Adsorption Free Energy from DFT-Based MD Using Thermodynamic Integration50Machine learning for advanced energy materials208Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals54Delayed Linker Addition (DLA) Synthesis for Hybrid SOD ZIFs with Unsubstituted Imidazolate Linkers for Propylene/Propane and n‐Butane/i‐Butane Separations40Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties49Toward 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 discovery538Deep learning for ultra-fast and high precision screening of energy materials55Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks105Bias free multiobjective active learning for materials design and discovery198Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks69Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Machine learning and data-driven characterization framework for porous materials: Permeability prediction and channeling defect detection54Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data42Programmable Logic in Metal–Organic Frameworks for Catalysis316Encoding the atomic structure for machine learning in materials science85Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks92Harnessing artificial intelligence to holistic design and identification for solid electrolytes51Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning105In Silico Design of Covalent Organic Framework-Based Electrocatalysts73A Machine Learning Shortcut for Screening the Spinel Structures of Mg/Zn Ion Battery Cathodes with a High Conductivity and Rapid Ion Kinetics66Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy71Machine learning and in-silico screening of metal–organic frameworks for O2/N2 dynamic adsorption and separation81Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines64Ab Initio Machine Learning in Chemical Compound Space195Biomaterials by design: Harnessing data for future development58A multi-data-driven procedure towards a comprehensive understanding of the activated carbon electrodes performance (using for supercapacitor) employing ANN technique62Applied Machine Learning for Developing Next‐Generation Functional Materials72Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Realizing the data-driven, computational discovery of metal-organic framework catalysts61Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry74Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening141Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Materials informatics-guided superior electrocatalyst: A case of pyrolysis-free single-atom coordinated with N-graphene nanomesh80Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption50Designing the ultrasonic treatment of nanoparticle-dispersions via machine learning44Emerging porous organic polymers for biomedical applications234High-throughput computational screening of nanoporous materials in targeted applications65MOF/COF hybrids as next generation materials for energy and biomedical applications80Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Design high-entropy carbide ceramics from machine learning149MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra72Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water42Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling43Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Making the collective knowledge of chemistry open and machine actionable85Advances in the adsorption/enrichment of proteins/peptides by metal–organic frameworks-affinity adsorbents47Graph neural network predictions of metal organic framework CO 2 adsorption properties93Data‐Driven Materials Innovation and Applications187Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation89High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration190Quo vadis multiscale modeling in reaction engineering? – A perspective63Synthesis strategies and design principles for nanosized and hierarchical zeolites131Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective126MOF‐Based Chemiresistive Gas Sensors: Toward New Functionalities410Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery61Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials45Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Machine learning for battery research110SELFIES and the future of molecular string representations261Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency46A data-science approach to predict the heat capacity of nanoporous materials117Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Challenges and Opportunities of Chemiresistors Based on Microelectromechanical Systems for Chemical Olfaction52Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Using Machine Learning to make nanomaterials sustainable57Halloysite nanotube/black phosphorene nanohybrid modified screen-printed carbon electrode as an ultra-portable electrochemical sensing platform for smartphone-capable detection of maleic hydrazide with machine learning assistance5414 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations41Electron Microscopy Studies of Soft Nanomaterials107ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning150Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials55Machine learning in gas separation membrane developing: Ready for prime time72An Ecosystem for Digital Reticular Chemistry52Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Functional Carbon from Nature: Biomass‐Derived Carbon Materials and the Recent Progress of Their Applications432A database of ultrastable MOFs reassembled from stable fragments with machine learning models89Feature Selection in Machine Learning for Perovskite Materials Design and Discovery59The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction62Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications51From prediction to design: Recent advances in machine learning for the study of 2D materials90Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling95Natural Products Derived Porous Carbons for CO 2 Capture116Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs168Revolutionizing the structural design and determination of covalent–organic frameworks: principles, methods, and techniques155Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence48Recent advances in metal-organic framework (MOF)-based agricultural sensors for metal ions: a review103Leveraging machine learning in porous media89Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach81Efficient first-principles electronic transport approach to complex band structure materials: the case of n-type Mg3Sb241Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications203Quest for Multifunctionality: Current Progress in the Characterization of Heterometallic Metal–Organic Frameworks42Machine Learning-Assisted Discovery of Propane-Selective Metal–Organic Frameworks85Analysis of metal–organic framework-based photosynthetic CO2 reduction152Emerging Photoreforming Process to Hydrogen Production: A Future Energy106High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning78Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46High-throughput and data-driven machine learning techniques for discovering high-entropy alloys77Porous-DeepONet: Learning the Solution Operators of Parametric Reactive Transport Equations in Porous Media47Self-Driving Laboratories for Chemistry and Materials Science674The Reinforced Separation of Intractable Gas Mixtures by Using Porous Adsorbents61Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Challenges and solutions to the scale-up of porous materials81A review on recent applications of machine learning in mechanical properties of composites100Machine-learning synergy in high-entropy alloys: A review43Machine learning applications in nanomaterials: Recent advances and future perspectives94CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening106From text to insight: large language models for chemical data extraction142Artificial intelligence in smart drug delivery systems: a step toward personalized medicine56Polymeric membranes in carbon capture, utilization, and storage: current trends and future directions in decarbonization of industrial flue gas and climate change mitigation120Sustainable carbon materials in environmental and energy applications47Machine learning strategies for small sample size in materials science41Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors69Guiding the rational design of biocompatible metal-organic frameworks for drug delivery65Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Machine Learning Relationships Between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors52Identifying MOFs for electrochemical energy storage via density functional theory and machine learning51A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes41Applications of density functional theory and machine learning in nanomaterials: A review51Nucleation and Growth Mechanisms of Micro/Nano Structural Manganese‐Trimesic Acid Coordinations for Aqueous Zinc‐Ion Batteries43Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Review of machine learning approaches for predicting mechanical behavior of composite materials83

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