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RESEARCH PAPER · Chemical Science · 2019

Progress and prospects for accelerating materials science with automated and autonomous workflows

Helge Sören Stein, John M. Gregoire

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Progress and prospects for accelerating materials science with automated and autonomous workflowsOriginalNamed Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature307Battery 2030+ Roadmap46Benchmarking the acceleration of materials discovery by sequential learning1496．機械学習を用いたX線吸収スペクトル解析の自動化0Exploring the landscape between synthetic and biosynthetic materials discovery: important considerations via systems connectivity, cooperation and scale-driven convergence in biomanufacturing9Role of Artificial Intelligence and Machine Learning in Nanosafety175Materials Acceleration Platforms: On the way to autonomous experimentation149Scanning electrochemical microscopy and its potential for studying biofilms and antimicrobial coatings37High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages29Designing and understanding light-harvesting devices with machine learning112Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles58On-the-fly closed-loop materials discovery via Bayesian active learning542Die Materialsynthesemaschine1Can we predict materials that can be synthesised?73Accelerating organic solar cell material's discovery: high-throughput screening and big data129Golem: an algorithm for robust experiment and process optimization38Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation18Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening16AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles102The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research29Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation1Olympus: a benchmarking framework for noisy optimization and experiment planning69Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra103Advancing the Pareto front for thin-film materials using a self-driving laboratory0Nobel Turing Challenge: creating the engine for scientific discovery140Deep learning for visualization and novelty detection in large X-ray diffraction datasets63G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge170Autonomous experimentation systems for materials development: A community perspective398Data-science driven autonomous process optimization226Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61The potential of scanning electrochemical probe microscopy and scanning droplet cells in battery research32AI - Based On The Fly Design of Experiments in Physics and Engineering4Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration5CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry0High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Accelerating Battery Characterization Using Neutron and Synchrotron Techniques: Toward a Multi‐Modal and Multi‐Scale Standardized Experimental Workflow62Flexible automation accelerates materials discovery98Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams98Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23Progress in Materials Data Availability and Application: A review1Bayesian optimization with known experimental and design constraints for chemistry applications89High-throughput exploration of activity and stability for identifying photoelectrochemical water splitting materials28Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study0Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration73Machine learning in nuclear materials research125CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry31Electrochemical Screening of Contact Layers for Metal Halide Perovskites11A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+235Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate24High-throughput workflows in the service of (photo)electrocatalysis research14Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings91A self-driving laboratory advances the Pareto front for material properties211Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data13Text Mining for Energy Materials3The case for data science in experimental chemistry: examples and recommendations83On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning55From materials discovery to system optimization by integrating combinatorial electrochemistry and data science37Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction55Advancing data-driven chemistry by beating benchmarks16Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters20Reproducible and Attributable Materials Science Workflows0Development of an automated millifluidic platform and data-analysis pipeline for rapid electrochemical corrosion measurements: A pH study on Zn-Ni19Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Brokering between tenants for an international materials acceleration platform5Scanning probes as a materials automation platform with extremely miniaturized samples8Solar fuel processing: Comparative mini-review on research, technology development, and scaling43Macaw: The Machine Learning Magnetometer Calibration Workflow7Machine-learning for designing nanoarchitectured materials by dealloying13Toward a function realization of multi‐scale modeling for lithium‐ion battery based on CHAIN framework19Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives7High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments11Data-Driven Discovery of Robust Materials for Photocatalytic Energy Conversion16Autonomous Drug Design with Multi-Armed Bandits7A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows14Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research35By how much can closed-loop frameworks accelerate computational materials discovery?21Conceptual Foundations for Value-Sensitive Design of Materials AI Systems0Domain-specific chatbots for science using embeddings29Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization33Integrating autonomy into automated research platforms32Orchestrating nimble experiments across interconnected labs37Realizing the cooking recipe of materials synthesis through large language models14The Future of Marketing and Sales Automation6Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence43Porous Molecular Materials1Need for UAI–Anatomy of the Paradigm of Usable Artificial Intelligence for Domain-Specific AI Applicability19The Functional Materials Beamline at CHESS20Machine Learning Identification of Organic Compounds Using Visible Light11Self-driving laboratories: A paradigm shift in nanomedicine development84Combinatorial synthesis for AI-driven materials discovery114WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions14ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Generative Models as an Emerging Paradigm in the Chemical Sciences313Autonomous x-ray scattering26Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science8Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously29Artificial intelligence for science and engineering: A priority for public investment in research and development2Eroom’s Law and the decline in the productivity of biopharmaceutical R&D0High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem0Interpretability: Should – and can – we understand the reasoning of machine-learning systems?0Lessons from shortcomings in machine learning for medical imaging1Preface0Quantifying the “cognitive extent” of science and how it has changed over time and across countries0The end of Moore’s Law? Innovation in computer systems continues at a high pace0Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission26Enabling Scalability in the Cloud for Scientific Workflows: An Earth Science Use Case2Brokering between tenants for an international materials acceleration platform46Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures18Deep Learning for Determination of Properties of Semidilute Polymer Solutions3Mechanisms of Biomolecular Self‐Assembly Investigated Through In Situ Observations of Structures and Dynamics36Mechanisms of Biomolecular Self‐Assembly Investigated Through In Situ Observations of Structures and Dynamics2Revolutionizing drug formulation development: The increasing impact of machine learning128CRESt – Copilot for Real-world Experimental Scientist4CRESt – Copilot for Real-world Experimental Scientist2Robotization of Three-Point Bending Mechanical Tests Using PLA/TPU Blends as an Example in the 0–100% Range11CRESt – Copilot for Real-world Experimental Scientist9On the use of Machine Learning modeling tools in chemical process and product engineering0Diversifying self-assembled phases in block copolymer thin films via blending8Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts59Recent advancements in laboratory automation technology and their impact on scientific research and laboratory procedures6Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug27Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory13An Affordable Platform for Automated Synthesis and Electrochemical Characterization1Equivariant Parameter Sharing for Porous Crystalline Materials2Precise control of process parameters for >23% efficiency perovskite solar cells in ambient air using an automated device acceleration platform42The future of self-driving laboratories: from human in the loop interactive AI to gamification102Towards a science exocortex11Virtual laboratories: transforming research with AI25Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects55A dynamic knowledge graph approach to distributed self-driving laboratories108Human-in-the-loop for Bayesian autonomous materials phase mapping25A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments45Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application4Designing workflows for materials characterization22Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics64A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community6Uncovering Zeolitic Imidazolate Frameworks through Digitization of Chemical Insights1AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers33Paving the way to hybrid quantum–classical scientific workflows34Low-cost automated flat-sheet membrane casting: An open-source, advanced manufacturing approach7Exploring Metal Electroplating for Energy Storage by Quartz Crystal Microbalance: A Review5

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  1. Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

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  2. Battery 2030+ Roadmap

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  3. Benchmarking the acceleration of materials discovery by sequential learning

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  4. 6．機械学習を用いたX線吸収スペクトル解析の自動化

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  5. Exploring the landscape between synthetic and biosynthetic materials discovery: important considerations via systems connectivity, cooperation and scale-driven convergence in biomanufacturing

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  6. Role of Artificial Intelligence and Machine Learning in Nanosafety

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  7. Materials Acceleration Platforms: On the way to autonomous experimentation

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  8. Scanning electrochemical microscopy and its potential for studying biofilms and antimicrobial coatings

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  9. High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages

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  10. Designing and understanding light-harvesting devices with machine learning

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  11. Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

    Scientific Reports · 2020-11-03

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  12. On-the-fly closed-loop materials discovery via Bayesian active learning

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  13. Die Materialsynthesemaschine

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  14. Can we predict materials that can be synthesised?

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  15. Accelerating organic solar cell material's discovery: high-throughput screening and big data

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  16. Golem: an algorithm for robust experiment and process optimization

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  17. Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation

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  18. Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening

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  19. AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

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  21. Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation

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  22. Olympus: a benchmarking framework for noisy optimization and experiment planning

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  23. Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

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  24. Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

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  25. Advancing the Pareto front for thin-film materials using a self-driving laboratory

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  32. The potential of scanning electrochemical probe microscopy and scanning droplet cells in battery research

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  34. Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration

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  35. CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry

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  36. High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications

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  37. Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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  38. Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective

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  39. Accelerating Battery Characterization Using Neutron and Synchrotron Techniques: Toward a Multi‐Modal and Multi‐Scale Standardized Experimental Workflow

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  40. Flexible automation accelerates materials discovery

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  41. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams

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  42. Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

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  43. Progress in Materials Data Availability and Application: A review

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  44. Bayesian optimization with known experimental and design constraints for chemistry applications

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  45. High-throughput exploration of activity and stability for identifying photoelectrochemical water splitting materials

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  46. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

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  47. The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study

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  48. Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

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  49. Machine learning in nuclear materials research

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  50. CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry

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