Agentic AI for autonomous discovery of radiation-tolerant CoCrFeMnNi high entropy alloys
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A third group relied on more compact planner-executor or single-agent loops that repeatedly called tools and updated decisions from the observed outputs.
“A third group relied on more compact planner-executor or single-agent loops that repeatedly called tools and updated decisions from the observed outputs [24,28,29,32-34,39,40].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [29]
Alloy design and evaluation formed the second-largest block with five studies.
“Alloy design and evaluation formed the second-largest block with five studies [18,23,29,31,33].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [29]
Some studies are simulator-native: the agent configures or executes atomistic or thermodynamic tools directly.
“Some studies are simulator-native: the agent configures or executes atomistic or thermodynamic tools directly [21,25,28,29,31,33,36,38-42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [29]
The most persuasive papers linked the language model to DFT, molecular dynamics, adsorption screening, process simulation, CALPHAD, crystallographic curation, or robotic execution.
“Instead, they linked it to DFT, molecular dynamics, adsorption screening, process simulation, CALPHAD, crystallographic curation, or robotic execution [18,21,25,28,29,31-36,39-42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [29]
In other papers, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete.
“In others, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete [17,22,24,26,27,29,33,34].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [29]
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Alviz-Meza, A., Valencia-Arias, A., Rojas-Flores, S., & Diaz, F. (2026) · Data · doi:10.3390/data11070180
- Authors
- Roy, A., Devanathan, R., Soulami, A., & Sushko, M. L.
- Affiliations
- Pacific Northwest National Laboratory (US)
- Corresponding author
- Roy, Ankit
- Venue
- Scripta Materialia
- Publisher
- Elsevier BV
- Published
- 2026-07
- Volume
- 279
- Pages
- 117286
- ISSN
- 1359-6462 (ISSN-L)
- OpenAlex
- W7140464812
- Cited by
- 3
- References
- 35
- Citations by year
- 2026: 3
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Scripta Materialia (publishedVersion)
- Funders
- U.S. Department of Energy (DE-AC05-76RL01830)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › High Entropy Alloys Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Fusion materials and technologies
- Keywords
- agentic AI, high-entropy alloys, radioresistance, CoCrFeMnNi, alloy composition design, large language models, radiation-induced defects, compositional space exploration, autonomous materials discovery
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