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RESEARCH PAPER · Science Advances · 2016

Materials design by evolutionary optimization of functional groups in metal-organic frameworks

Sean P. Collins, Thomas D. Daff, Sarah Piotrkowski, Tom K. Woo

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Materials design by evolutionary optimization of functional groups in metal-organic frameworksOriginalExceptionally stable Bakelite-type polymers for efficient pre-combustion CO 2 capture and H 2 purification14Metal–organic frameworks meet scalable and sustainable synthesis520Partially Interpenetrated NbO Topology Metal–Organic Framework Exhibiting Selective Gas Adsorption32A mechanistic approach towards the photocatalytic organic transformations over functionalised metal organic frameworks: a review138Computational evaluation of metal pentazolate frameworks: inorganic analogues of azolate metal–organic frameworks47Microporous mixed-metal mixed-ligand metal organic framework for selective CO 2 capture10Preferential Adsorption of CO2 in an Ultramicroporous MOF with Cavities Lined by Basic Groups and Open-Metal Sites67CO2 adsorption performance of functionalized metal-organic frameworks of varying topologies by molecular simulations31In Silico Screening of MOFs with open copper sites for C2H2/CO2 separation36Role of Pore Chemistry and Topology in the CO 2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning239Ligands-Coordinated Zr-Based MOF for Wastewater Treatment60Materials genomics methods for high-throughput construction of COFs and targeted synthesis282Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO 2 /CH 4 separation86The inverse design of structural color using machine learning69Universal Scaling Law for Methane Capture Quantity in Metal–Organic Frameworks2Materials‐Informatics‐Assisted High‐Yield Synthesis of 2D Nanomaterials through Exfoliation32Computational screening of metal−organic frameworks for CO2 separation22The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation0Performance of biologically inspired algorithms tuned on TiO2 nanoparticle benchmark system1Theoretical Prediction and Experimental Evaluation of Topological Landscape and Thermodynamic Stability of a Fluorinated Zeolitic Imidazolate Framework43Strategies for Overcoming Defects of HKUST‐1 and Its Relevant Applications54The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation123Water‐stable Adenine‐based MOFs with Polar Pores for Selective CO2 Capture43Experiment‐Oriented Materials Informatics for Efficient Exploration of Design Strategy and New Compounds for High‐Performance Organic Anode25Copper(II), Zinc(II), and Cadmium(II) Formylbenzoate Complexes: Reactivity and Emission Properties19Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks78Modeling of multi-scale transport phenomena in shale gas production — A critical review293High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design5Sulfonated Sub-1-nm Metal–Organic Framework Channels with Ultrahigh Proton Selectivity82Ab Initio Prediction of Metal-Organic Framework Structures28Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Applications of machine learning in metal-organic frameworks225High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials127Isotherm parameter library and evaluation software for CO2 capture adsorbents10Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization40Evolutionary design of optimal surface topographies for biomaterials9Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery94Bayesian optimization of nanoporous materials112Computational Screening of MOFs for CO2 Capture3Engineering metal–organic frameworks for adsorption-based gas separations: from process to atomic scale64Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review306Inverse design of nanoporous crystalline reticular materials with deep generative models400Machine Learning-Driven Discovery of Metal–Organic Frameworks for Efficient CO2 Capture in Humid Condition72A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage211Machine Learning-based approach for Tailor-Made design of ionic Liquids: Application to CO2 capture41Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption12Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25A graph theoretic representation and analysis of zeolite frameworks20Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Angstrom-scale ion channels towards single-ion selectivity294Neighborhoods and functionality in metals0PoreMatMod.jl : Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models17Rapid Cs + Capture via Multiple Supramolecular Interactions in Anionic Metal–Organic Framework Isomers15A Step in Carbon Capture from Wet Gases: Understanding the Effect of Water on CO2 Adsorption and Diffusion in UiO-6630Application of metal organic framework in wastewater treatment272Machine Learning for the Discovery, Design, and Engineering of Materials66Effect of nickel ion concentration on structural, optical and electrical properties towards Ni–H3BTC-MOF formation for nonlinear saturable absorption phenomenon11Computer-aided tuning of silica/poly(dimethylsiloxane) composites for 3D printing process: A computational and experimental study3Machine-learning-guided reaction kinetics prediction towards solvent identification for chemical absorption of carbonyl sulfide29Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives90Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning38DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition34Computation-informed optimization of Ni(PyC)2 functionalization for noble gas separations12Synthesis of a bifunctional metal-organic framework membrane for the removal of anionic and cationic heavy metals from wastewater1Energy landscapes in inorganic chemistry13Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46A Self-Evolutionary Methodology for Reverse Design of Novel MOFs9Automated and intelligent chemical synthesis5Modelling carbon capture on metal-organic frameworks with quantum computing33A review of recent advances and applications of machine learning in tribology109Artificial intelligence in multiscale scaffolds for cancer organoids testbed1Influence of anions on the synthesis of Cu( i )-based coordination polymers along with a series of derived materials for selective photocatalytic properties3Machine learning of isomerization in porous molecular frameworks: exploring functional group pair distance distributions6ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning150Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications4Is GPT-3 all you need for low-data discovery in chemistry?26Investigations on the Factors Influencing the Mercaptan Removal through Adsorption with Metal‐Organic Frameworks1Data Driven Discovery of MOFs for Hydrogen Gas Adsorption35Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios3Interfacial Engineering of Heterogeneous Reactions for MOF‐on‐MOF Heterostructures105Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence48CO2 adsorption using functionalized metal–organic frameworks under low pressure: Contribution of functional groups, excluding amines, to adsorption80Deep learning-based recommendation system for metal–organic frameworks (MOFs)15Leveraging large language models for predictive chemistry367Unraveling the separation mechanism of gas mixtures in MOFs by combining the breakthrough curve with machine learning and high-throughput calculation8Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications79Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions272Application of machine learning in CCUS0AI4Materials: Transforming the landscape of materials science and enigneering25Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes9Generative AI for design of nanoporous materials: review and future prospects41Machine Learning Descriptors for CO2 Capture Materials21Harnessing stimuli-responsive moieties for next-gen smart metal-organic frameworks (MOFs): Designing strategies, key advancements and future perspectives5Recent advances in computational screening of metal-organic frameworks for carbon capture6Inverse Design of Metal‐Organic Frameworks for CH 4 /N 2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms7Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective22Bioengineering hybrid artificial life0AI-driven Carbon Capture and Storage (CCS): an analysis of emerging technologies for sustainable climate solutions8SOCIAL: Social network optimization algorithm via centrality and influence-aware learning0Multifunctional origami-structured triboelectric nanogenerators based on zinc coordination polymers for self-powered photoinduced oxidation systems toward green energy harvesting6

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  10. Role of Pore Chemistry and Topology in the CO 2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

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  21. Strategies for Overcoming Defects of HKUST‐1 and Its Relevant Applications

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