ArticleChemical science2020
Autonomous intelligent agents for accelerated materials discovery.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Interpretable Bayesian optimization for catalyst discovery.Faraday discussions · 2026Article
- Docking-score landscapes shape active-learning performance across Vina, Glide, and SILCS.Journal of computer-aided molecular design · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- EcoRxAgent: an AI agent for generating economically substitutable prescriptions.NPJ digital medicine · 2026Article
- Wide-ranging predictions of new stable compounds powered by recommendation engines.Science advances · 2025Article
- Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions.Journal of the American Chemical Society · 2024Article
- An electrochemical series for materials.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.Chemical science · 2024Article
- 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.Sensors (Basel, Switzerland) · 2022Article
- Novel inorganic crystal structures predicted using autonomous simulation agents.Scientific data · 2022Article
- Theory-guided experimental design in battery materials research.Science advances · 2022Review
- Agents for sequential learning using multiple-fidelity data.Scientific reports · 2022Article
- Review
- Data-Driven Methods for Accelerating Polymer Design.ACS polymers Au · 2022Review
- The materials tetrahedron has a "digital twin".MRS bulletin · 2022Article
- Machine learning-accelerated design and synthesis of polyelemental heterostructures.Science advances · 2021Article
- Accelerating high-throughput virtual screening through molecular pool-based active learning.Chemical science · 2021Article
- Towards Predictive Synthesis of Inorganic Materials Using Network Science.Frontiers in chemistry · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.
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Registered trials
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