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RESEARCH PAPER · Dalton Transactions · 2020

Navigating the design space of inorganic materials synthesis using statistical methods and machine learning

Erick J. Braham, Rachel D. Davidson, Mohammed Al‐Hashimi, Raymundo Arróyave, Sarbajit Banerjee

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42 direct citing works

Navigating the design space of inorganic materials synthesis using statistical methods and machine learningOriginalA power-triggered preparation strategy of nano-structured inorganics: sonosynthesis50Designing bioinspired green nanosilicas using statistical and machine learning approaches12Toward autonomous design and synthesis of novel inorganic materials159Using simulation to accelerate autonomous experimentation: A case study using mechanics67Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments28Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis3Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development19Bayesian optimization in continuous spaces via virtual process embeddings11Benchmarking active learning strategies for materials optimization and discovery48Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis11Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46A Materials Science Perspective of Midstream Challenges in the Utilization of Heavy Crude Oil37Lone but Not Alone: Precise Positioning of Lone Pairs for the Design of Photocatalytic Architectures38Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments19Statistical inverse estimation of source terms in the Helmholtz equation2Inverse Design of Materials by Machine Learning137The chemistry of quantum materials2Design of Experiments for Nanocrystal Syntheses: A How-To Guide for Proper Implementation62DoE-It-Yourself: A Case Study for Implementing Design of Experiments into Nanoparticle Synthesis2Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation21Artificial Scanning Electron Microscopy Images Created by Generative Adversarial Networks from Simulated Particle Assemblies19Machine Learning-Directed Predictive Models: Deciphering Complex Energy Transfer in Mn-Doped CsPb(Cl 1– y Br y ) 3 Perovskite Nanocrystals21Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier32A Data‐Driven Approach to Molten Salt Synthesis of N‐Rich Carbon Adsorbents for Selective CO2 Capture27Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis33Advanced morphological control over Cu nanowires through a design of experiments approach8Hydrothermal synthesis of metal nanoparticles@hydrogels and statistical evaluation of reaction conditions’ effects on nanoparticle morphologies17Accelerating the design of lattice structures using machine learning47Construction frontier molecular orbital prediction model with transfer learning for organic materials36GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale11High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis5Electrified Enhanced Recovery of Lithium from Unconventional Sources6Hydride synthesis of thermoelectric skutterudites (Ba,Sr)Fe 3± x Ni 1± x Sb 12 optimized via design of experiments1Materials laboratories of the future for alloys, amorphous, and composite materials8Autonomous Intuition for Advanced Manufacturing Systems: Benefits and Tradeoffs of Presampling for Autonomous Parameterization0Machine Learning Driven Energy Transfer Prediction in Mn-Doped 2D Halide Perovskites5Generating and leveraging explanations of AI/ML models in materials and manufacturing research13Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation13Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations2A Synthetic Perspective of Strategies for Accessing Metastable Polymorphs: Lessons Learned from Hafnium(IV) Oxide0Entropy-based materialistic and mechanistic insights for zinc ion battery applications0The challenge of synthesis dependence in quantum materials research1

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