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RESEARCH PAPER · Chemical Science · 2020

Autonomous intelligent agents for accelerated materials discovery

Joseph H. Montoya, Kirsten T. Winther, Raul A. Flores, Thomas Bligaard, Jens S. Hummelshøj, Muratahan Aykol

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Autonomous intelligent agents for accelerated materials discoveryOriginalActive Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction144Machine-learning predictions of polymer properties with Polymer Genome276Emerging materials intelligence ecosystems propelled by machine learning440Accelerating high-throughput virtual screening through molecular pool-based active learning301Integrated “Generate, Make, and Test” for Formulated Products using Knowledge Graphs9Toward autonomous design and synthesis of novel inorganic materials159Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime264An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models12Rational Solid-State Synthesis Routes for Inorganic Materials115Computational design of moiré assemblies aided by artificial intelligence22Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Autonomous experimentation systems for materials development: A community perspective398Machine learning for materials discovery: Two-dimensional topological insulators71NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering83Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing51Towards autonomous high-throughput multiscale modelling of battery interfaces33Towards Predictive Synthesis of Inorganic Materials Using Network Science5Machine learning–accelerated design and synthesis of polyelemental heterostructures126Data-Driven Methods for Accelerating Polymer Design122Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design0New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research53From Platform to Knowledge Graph: Evolution of Laboratory Automation108Toward autonomous materials research: Recent progress and future challenges60The materials tetrahedron has a “digital twin”34Novel inorganic crystal structures predicted using autonomous simulation agents0Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries34Autonomous high-throughput computations in catalysis29Interpretable machine learning for knowledge generation in heterogeneous catalysis429Agents for sequential learning using multiple-fidelity data38Theory-guided experimental design in battery materials research156Novel inorganic crystal structures predicted using autonomous simulation agents24Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell19Reflections on one million compounds in the open quantum materials database (OQMD)49Toward Automated Computational Discovery of Battery Materials13Strategies for Modulating the Catalytic Activity and Selectivity of Manganese Antimonates for the Oxygen Reduction Reaction19A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment0Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118By how much can closed-loop frameworks accelerate computational materials discovery?21Integrating autonomy into automated research platforms32Quantifying the performance of machine learning models in materials discovery47Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Active learning for optimum experimental design—insight into perovskite oxides8Scalable multi-agent lab framework for lab optimization34Computer-assisted discovery and rational synthesis of ternary oxides3Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources13Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors71AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems1Data driven design of alkali-activated concrete using sequential learning31AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems1QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery25Autonomous Exploration of an Unexpected Electrode Material for Lithium Batteries11Data-augmented modeling for yield strength of refractory high entropy alloys: A Bayesian approach61Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system7Accelerating the prediction of stable materials with machine learning104Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap7AI-driven inorganic materials by design0GLAS: an open-source easily expandable Git-based scheduling architecture for integral lab automation3How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science21ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning105Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys26Data science accelerates energy device development1Data science accelerates energy device development1Machine Learning-Enabled Image Classification for Automated Electron Microscopy8Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray8From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design188High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning32Data-driven analysis of battery formation reveals the role of electrode utilization in extending cycle life60A Python tool for parameter estimation of “black box” macro- and micro-kinetic models with Bayesian optimization – petBOA5An electrochemical series for materials5Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization8Employing artificial intelligence to steer exascale workflows with colmena7Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions5Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors63Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17Wide-ranging predictions of new stable compounds powered by recommendation engines12Anomalous reversal of stability in Mo-containing oxides: A difficult case exhibiting sensitivity to DFT + U and distortion2Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis24Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Machine learning application in thermal CO2 hydrogenation: catalyst design, process optimization, and mechanism insights13The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories9Combinatorial to autonomous experimentation0Exploring quantum active learning for materials design and discovery11Interpretable Bayesian optimization for catalyst discovery0Synergistic Experimental and Computational Approaches for Next-Generation Materials Discovery and Design0Agentic material science5Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes2AI agents for automating materials research: a case study of crystal plasticity simulations0EcoRxAgent: an AI agent for generating economically substitutable prescriptions1Real-time Multi-instrument Autonomous Materials Screening for Discovery of Novel Phase-change Memory Materials0Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design0Managing autonomous materials labs with multi-agent AI and its implications for the science of science1Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2Artificial intelligence for sustainable chemical synthesis: Advances in catalysis, biocatalysis, solvents, materials, and life cycle assessment0Docking-score landscapes shape active-learning performance across Vina, Glide, and SILCS0

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  1. Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

    Chemistry of Materials · 2020-06-18

    Raul A. Flores, Christopher Paolucci, Kirsten T. Winther, Ankit Jain, José Antonio Garrido Torres, Muratahan Aykol, Joseph H. Montoya, Jens K. Nørskov, Michal Bajdich, Thomas Bligaard

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  2. Machine-learning predictions of polymer properties with Polymer Genome

    Journal of Applied Physics · 2020-11-05

    Tran Doan Huan, Chiho Kim, Lihua Chen, Anand R. Chandrasekaran, Rohit Batra, Shruti Venkatram, Deepak Kamal, Jordan P. Lightstone, Rishi Gurnani, Pranav Shetty, Manav Ramprasad, Julia Laws, Madeline Shelton, Rampi Ramprasad

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  3. Emerging materials intelligence ecosystems propelled by machine learning

    Nature Reviews Materials · 2020-11-09

    Rohit Batra, Le Song, Rampi Ramprasad

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  4. Accelerating high-throughput virtual screening through molecular pool-based active learning

    Chemical Science · 2021-01-01

    David E. Graff, Eugene I. Shakhnovich, Connor W. Coley

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  5. Integrated “Generate, Make, and Test” for Formulated Products using Knowledge Graphs

    Data Intelligence · 2021-01-01

    Sagar Sunkle, Deepak Jain, K B Saxena, Ashwini Patil, Tushita Singh, Beena Rai, Vinay Kulkarni

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  6. Toward autonomous design and synthesis of novel inorganic materials

    Materials Horizons · 2021-01-01

    Nathan J. Szymanski, Yan Zeng, Haoyan Huo, Christopher J. Bartel, Haegyeom Kim, Gerbrand Ceder

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  7. Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime

    Journal of The Electrochemical Society · 2021-03-01

    Muratahan Aykol, Chirranjeevi Balaji Gopal, Abraham Anapolsky, Patrick K. Herring, Bruis van Vlijmen, Marc D. Berliner, Martin Z. Bazant, Richard D. Braatz, William C. Chueh, Brian D. Storey

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  8. An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

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    Jason R. Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howie Joress, Winnie Wong-Ng, Debra L. Kaiser, Andriy Zakutayev, Caleb T. Phillips, Shijing Sun, Janak Thapa, Heshan Yu, Ichiro Takeuchi, Tonio Buonassisi

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  9. Rational Solid-State Synthesis Routes for Inorganic Materials

    Journal of the American Chemical Society · 2021-06-11

    Muratahan Aykol, Joseph H. Montoya, Jens S. Hummelshøj

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  10. Computational design of moiré assemblies aided by artificial intelligence

    Applied Physics Reviews · 2021-07-12

    Georgios A. Tritsaris, Stephen Carr, Gabriel Ravanhani Schleder

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  11. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

    Chemical Reviews · 2021-07-14

    Aditya Nandy, Chenru Duan, Michael G. Taylor, Fang Liu, Adam H. Steeves, Heather J. Kulik

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  12. Autonomous experimentation systems for materials development: A community perspective

    Matter · 2021-07-26

    Eric A. Stach, Brian DeCost, Aaron Gilad Kusne, Jason R. Hattrick-Simpers, Keith A. Brown, Kristofer G. Reyes, Joshua Schrier, Simon J. L. Billinge, Tonio Buonassisi, Ian T Foster, Carla Pedro Gomes, John M. Gregoire, Apurva Mehta, Joseph H. Montoya, Elsa Olivetti, Chiwoo Park, Eli Rotenberg, Semion K. Saikin, Sylvia Smullin, Valentin Stanev, Benji Maruyama

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  13. Machine learning for materials discovery: Two-dimensional topological insulators

    Applied Physics Reviews · 2021-08-03

    Gabriel Ravanhani Schleder, Bruno Focassio, A. Fazzio

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  14. NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

    Journal of Chemical Information and Modeling · 2021-10-25

    Yifan Wang, Tai-Ying Chen, Dionisios G. Vlachos

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  15. Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

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    Logan T. Ward, Ganesh Sivaraman, J. Gregory Pauloski, Yadu Babuji, Ryan Chard, Naveen Kumar Dandu, Paul C. Redfern, Rajeev S. Assary, Kyle Chard, Larry A. Curtiss, Rajeev Thakur, Ian T Foster

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  16. Towards autonomous high-throughput multiscale modelling of battery interfaces

    Energy & Environmental Science · 2021-12-20

    Zeyu Deng, Vipin Kumar, Felix T. Bölle, Fernando Caro, Alejandro A. Franco, Ivano E. Castelli, Pieremanuele Canepa, Zhi Wei Seh

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  17. Towards Predictive Synthesis of Inorganic Materials Using Network Science

    Frontiers in Chemistry · 2021-12-21

    Alex Aziz, Javier Carrasco

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  18. Machine learning–accelerated design and synthesis of polyelemental heterostructures

    Science Advances · 2021-12-22

    Carolin B. Wahl, Muratahan Aykol, Jordan H. Swisher, Joseph H. Montoya, Santosh K. Suram, Chad Alexander Mirkin

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  19. Data-Driven Methods for Accelerating Polymer Design

    ACS Polymers Au · 2021-12-28

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  20. Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

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  21. New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research

    Energy & Environmental Science · 2022-01-01

    Michaela Burke Stevens, Megha Anand, Melissa E. Kreider, Eliza K. Price, Jose Andres Zamora Zeledon, Liang Wang, Jiayu Peng, Hao Li, John M. Gregoire, Jens S. Hummelshøj, Thomas Francisco Jaramillo, Hongfei Jia, Jens K. Nørskov, Yuriy Román‐Leshkov, Yang Shao‐Horn, Brian D. Storey, Santosh K. Suram, Steven B. Torrisi, Joseph H. Montoya

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  22. From Platform to Knowledge Graph: Evolution of Laboratory Automation

    JACS Au · 2022-01-10

    Jiaru Bai, Liwei Cao, Sebastian Mosbach, Jethro Akroyd, Alexei A. Lapkin, Markus Kraft

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  23. Toward autonomous materials research: Recent progress and future challenges

    Applied Physics Reviews · 2022-01-13

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  24. The materials tetrahedron has a “digital twin”

    MRS Bulletin · 2022-02-01

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  25. Novel inorganic crystal structures predicted using autonomous simulation agents

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  26. Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

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  27. Autonomous high-throughput computations in catalysis

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  28. Interpretable machine learning for knowledge generation in heterogeneous catalysis

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  29. Agents for sequential learning using multiple-fidelity data

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  30. Theory-guided experimental design in battery materials research

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  31. Novel inorganic crystal structures predicted using autonomous simulation agents

    Scientific Data · 2022-06-14

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  32. Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell

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  33. Reflections on one million compounds in the open quantum materials database (OQMD)

    Journal of Physics Materials · 2022-06-23

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  34. Toward Automated Computational Discovery of Battery Materials

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  35. Strategies for Modulating the Catalytic Activity and Selectivity of Manganese Antimonates for the Oxygen Reduction Reaction

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  36. A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment

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  37. Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

    Progress in Materials Science · 2022-11-11

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  38. By how much can closed-loop frameworks accelerate computational materials discovery?

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  39. Integrating autonomy into automated research platforms

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  40. Quantifying the performance of machine learning models in materials discovery

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  41. Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

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  42. Active learning for optimum experimental design—insight into perovskite oxides

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  43. Scalable multi-agent lab framework for lab optimization

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  44. Computer-assisted discovery and rational synthesis of ternary oxides

    ChemRxiv · 2023-04-28

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  45. Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

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  46. Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors

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  47. AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems

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  48. Data driven design of alkali-activated concrete using sequential learning

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  49. AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems

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  50. QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery

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