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RESEARCH PAPER · Catalysis Today · 2020

Machine learning in experimental materials chemistry

Balaranjan Selvaratnam, Ranjit T. Koodali

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Machine learning in experimental materials chemistryOriginalComparative Analysis of Traditional Machine Learning and Active Learning in Science Experiments0Predicting inorganic dimensionality in templated metal oxides8Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage23Machine Learning: New Ideas and Tools in Environmental Science and Engineering945Recent machine learning guided material research - A review12Gaussian process models of potential energy surfaces with boundary optimization9A machine learning colorimetric biosensor based on acetylcholinesterase and silver nanoparticles for the detection of dichlorvos pesticides32Machine learning for design principles for single atom catalysts towards electrochemical reactions116Neural Network Potentials: A Concise Overview of Methods306Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review109Deep learning modeling strategy for material science: from natural materials to metamaterials29Challenges and Opportunities for Renewable Ammonia Production via Plasmon‐Assisted Photocatalysis62Machine Learning–Assisted Thin-Film Transistor Characterization: A Case Study of Amorphous Indium Gallium Zinc Oxide (IGZO) Thin-Film Transistors6The case for data science in experimental chemistry: examples and recommendations83Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon42Sulfur oxidative coupling of methane process development and its modeling via machine learning3Lung cancer detection by using probabilistic majority voting and optimization techniques33Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning36Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength9Designing catalysts via evolutionary-based optimization techniques18Recent advances in machine learning for electronic excited state molecular dynamics simulations2Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods15Transformation of aluminosilicate materials into high value zeolites0Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR)40A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review39AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning78Deep‐Learning‐Enabled Intelligent Design of Thermal Metamaterials70Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes13Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_rev Dataset2Machine learning-based multi-objective parameter optimization for indium electrorefining8From prediction to design: Recent advances in machine learning for the study of 2D materials90Prediction of Material Surface Area using Multiple Linear Regression Algorithm with Independent Variables Time, T emperature and Quantity0Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set26Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films via precise automated characterization methods and machine learning assisted analysis14Insights into Preparation Methods and Functions of Carbon-Based Solid Acids18Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56Machine Learning Aided Design and Optimization of Thermal Metamaterials195Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model5A Three-Pronged Computational Approach for Evaluating Density Based Semi Empirical Equations of Supercritical Extraction Process and Data1Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques28A Three-Pronged Computational Approach for Evaluating Density Based Semi Empirical Equations of Supercritical Extraction Process and Data1A Three-Pronged Computational Approach for Evaluating Density Based Semi Empirical Equations of Supercritical Extraction Process and Data1Machine-Learned Models for Power Magnetic Material Characteristics7Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization4Machine learning-enhanced photocatalysis for environmental sustainability: Integration and applications53Artificial Intelligence Assisted Teaching and Learning and Research of Environmental Sciences1Erosion wear performance of titania filled ramie-epoxy composites: A data driven optimization study using supervised machine learning approach7Biomass valorization with metal-free catalysts: innovations in thermocatalytic, photocatalytic, and electrocatalytic approaches72Prediction of Surface Area of Material (MnO2) by Neuro-Fuzzy Techniques0A Machine Learning Model for the Prediction of Water Contact Angles on Solid Polymers24Shape-functional fusion metamaterials for vibration isolation by integrating forward and reverse deep leaning-driven design12A deep learning-assisted inverse design framework for nacre-like composites with excellent specific damping performance2Statistical and artificial intelligence approaches towards the optimization of thermoelectric materials synthesis: a review7A Conversational Large‐Language‐Model Tutor that Accelerates Machine‐Learning Method Development in Routine Bioanalytical Workflows1Engineering Hierarchical Zeolites: Mechanistic Insights, Predictive Framework, and Deployment Pathways3An overview of biofuels with artificial intelligence0An Explainable Stacked Machine Learning Approach for Toluene Capture in Metal‐Organic Frameworks: From Predictive Modeling to Interactive Web Platform7Computational approach to green chemistry0Topology Meets Reactivity: Rationalizing Electron Rearrangements in Cycloadditions Through Thom’s Polynomials and Bonding Evolution Theory3Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives5Machine learning-driven composition optimization for high strength and ductility in Al-Mg-Si-Zr-Er alloys4Data-driven transfer learning across MOF-derived zirconia polymorphs0Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction0

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