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RESEARCH PAPER · Current Opinion in Green and Sustainable Chemistry · 2020

Materials Acceleration Platforms: On the way to autonomous experimentation

Martha M. Flores‐Leonar, L.M. Mejía-Mendoza, Andrés Aguilar‐Granda, Benjamín Sánchez-Lengeling, Hermann Tribukait, Carlos Amador‐Bedolla, Alán Aspuru‐Guzik

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Materials Acceleration Platforms: On the way to autonomous experimentationOriginalSimplifying inverse material design problems for fixed lattices with alchemical chirality0Self-Driving Laboratories Coming of Age15Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery94Accelerating organic solar cell material's discovery: high-throughput screening and big data129Artificial Intelligence and Industry 4.0 Across the Continent: How AI and 4.0 are Addressed by Region2Golem: an algorithm for robust experiment and process optimization38Integrated “Generate, Make, and Test” for Formulated Products using Knowledge Graphs9Toward autonomous design and synthesis of novel inorganic materials159Data-Driven Strategies for Accelerated Materials Design473AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles102Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation1Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts21Simplifying inverse materials design problems for fixed lattices with alchemical chirality19Machine Learning for Chemical Reactions437Autonomous experimentation systems for materials development: A community perspective398Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space69Artificial Intelligence Applied to Battery Research: Hype or Reality?503Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization77Current Trends in Automotive Lightweighting Strategies and Materials552Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23A Conceptual Theory of Model-Science Interface: Where AI Outputs Become Experimental Inputs0Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids50Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models11Robotic cell assembly to accelerate battery research46Routescore: Punching the Ticket to More Efficient Materials Development20From Platform to Knowledge Graph: Evolution of Laboratory Automation108Active meta-learning for predicting and selecting perovskite crystallization experiments24Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations52The case for data science in experimental chemistry: examples and recommendations83Construction of Legal Reporting Information Platform Based on Natural Optimization Algorithm0Sidekick: A Low-Cost Open-Source 3D-printed liquid dispensing robot34Machine Learning in Tissue Engineering73Machine learning-facilitated multiscale imaging for energy materials27Brokering between tenants for an international materials acceleration platform5Linking scientific instruments and computation: Patterns, technologies, and experiences45Principles of the Battery Data Genome132Scanning probes as a materials automation platform with extremely miniaturized samples8Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis29A perspective on Bayesian methods applied to materials discovery and design47A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems6Physical computing for materials acceleration platforms7Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118What is a minimal working example for a self-driving laboratory?49Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery51Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces15NGenE 2022: Electrochemistry for Decarbonization19A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform36An open-source environmental chamber for materials-stability testing using an optical proxy13Autonomous experimentation in nanotechnology2By how much can closed-loop frameworks accelerate computational materials discovery?21Development of Key Technologies for Intelligent Research and Development of New Materials6Orchestrating nimble experiments across interconnected labs37Phase stability studies on transition metal phosphates aided by an automated synthesis7Robotically automated 3D printing and testing of thermoplastic material specimens16The materials experiment knowledge graph25What is missing in autonomous discovery: open challenges for the community44Emerging Trends in Machine Learning: A Polymer Perspective187Machine learning and experiments: A synergy for the development of functional materials19AutodiDAQt: Simple Scientific Data Acquisition Software with Analysis-in-the-Loop0Machine Learning Identification of Organic Compounds Using Visible Light11Self-driving laboratories: A paradigm shift in nanomedicine development84Combinatorial synthesis for AI-driven materials discovery114Autonomous experimental systems in materials science22Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis28Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science8The role of machine learning in perovskite solar cell research42Recommending Multiple Reaction Conditions UsingTwo-Stage Deep Neural Networks1Brokering between tenants for an international materials acceleration platform46Review of Low-cost Self-driving Laboratories: The "Frugal Twin" Concept3Closed-Loop Transfer Enables AI to Yield Chemical Knowledge6Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review40Accelerated Characterization of Electrode‐Electrolyte Equilibration9Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning12Event-driven data management with cloud computing for extensible materials acceleration platforms8Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug27Accelerated screening of carbon dioxide capture by liquid sorbents0Accelerated screening of gas diffusion electrodes for carbon dioxide reduction8ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry8GLAS: an open-source easily expandable Git-based scheduling architecture for integral lab automation3Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration via automated material compounding2PerQueue: managing complex and dynamic workflows18Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept93The future of self-driving laboratories: from human in the loop interactive AI to gamification102Toward Autonomous Materials Intelligence: A Human–AI–Physics Framework for Discovering, Optimizing, and Validating Next-Generation Materials1Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions33Sharing reproducible synthesis recipes6Designing workflows for materials characterization22The Future of Material Scientists in an Age of Artificial Intelligence76Machine intelligence-accelerated discovery of all-natural plastic substitutes113Accelerated Materials Design and Materials Data Management at MCL0Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs75Modern Automation in Organic Synthesis Laboratories5The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies79Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories42FullProfAPP : a graphical user interface for the streamlined automation of powder diffraction data analysis58Closed-loop transfer enables artificial intelligence to yield chemical knowledge73Deep learning revealed statistics of the MgO particles dissolution rate in a CaO–Al2O3–SiO2–MgO slag4Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows4Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization8Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides5Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs1Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science11ADEL: an automated drop-cast electrode setup for high-throughput screening of battery materials4An automated platform for “on-demand” high-speed catalyst synthesis by flame spray pyrolysis1Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation23Atlas: a brain for self-driving laboratories50Autonomy without Oversight: Governance Vacuums in Self-Driving Computational Materials Engineering Systems0Coordination of the European Research Community on Nuclear Materials for Energy Innovation: the research agenda of the CONNECT-NM co-funded European partnership0Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry51Leveraging feature gradient for efficient acquisition function maximization in material composition design3Autonomous Optimization of Air‐Processed Perovskite Solar Cell in a Multidimensional Parameter Space23Materials laboratories of the future for alloys, amorphous, and composite materials8Advancing perovskite photovoltaic technology through machine learning‐driven automation49Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review41SmartLab: Flexible and interoperable manufacturing laboratory system for remote education and research using a mobile manipulator4Digital Platform for Accelerated Materials Development: Status, Challenges, Prospects, Technologies1Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression6Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review9Autonomous ‘self-driving’ laboratories: a review of technology and policy implications70AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere19The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers6Flow-driven data intensification to accelerate autonomous inorganic materials discovery25Limitations of theory-informed machine learning algorithms for the prediction and exploration of molecular properties: Solvation free energy as a case study3Affordable Automated Modules for Lab‐Scale High‐Throughput Synthesis of Inorganic Materials4AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation1AI-guided high-throughput investigation of conjugated polymer doping reveals importance of local polymer order and dopant-polymer separation3Regenerative Engineering: Evolution and Its Modern Significance4FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction3Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics4Designing and Utilizing Material Acceleration Platforms: Need for Workforce Development0Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices15A scalable open-source software platform for managing and automating massively parallel battery cycling experiments0Accelerated materials discovery through random-library exploration by automated scanning transmission electron microscopy0Predicting Materials Using Variational Autoencoders: A Systematic Literature Review8AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries28Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology6Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges9Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows5The Interoperability Challenge in DFT Workflows Across Implementations3Prediction‐Guided Two‐Step Solid‐State Exploration of Unknown Pseudo‐Ternary Oxides0From High-Throughput Screening to Generative Design: Artificial Intelligence-Driven Dielectric Materials Discovery2Foundation models for multimodal image data fusion in materials science4Qualification of proxy experiments for accelerated electrochemical testing in self-driving labs1AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2Case Study of Bayesian Batch Optimization Strategies for Heterogeneous Catalysts in CO2 Hydrogenation0Toward automated discovery with generative models multimodal learning and closed loop workflows in inverse materials design0Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data0

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