Automating alloy design and discovery with physics-aware multimodal multiagent AI
Open accessAbstract
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLMs) and the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multiobjective design tasks and opens avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability.
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These papers typically split the workflow into role-specific agents for planning, structure retrieval, simulation execution, analysis, and quality control.
“These papers typically split the workflow into role-specific agents for planning, structure retrieval, simulation execution, analysis, and quality control [18,19,21,25,26,35-38,41,42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [18]
The Results and Discussion and Experiment I sections describe role-specific agents and tools for planning, knowledge retrieval, atomistic simulations, and analysis; a critic also reviews the plan. The retrieved information is material properties from papers, not structures, and the critic’s plan review does not establish a general quality-control stage. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlloy 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 [18]
In several papers, the strongest reproducibility evidence came from public code, tutorials, datasets, or supplementary traces.
“In several papers, the strongest reproducibility evidence came from public code, tutorials, datasets, or supplementary traces [18-21,23,25,28,30,31,35-42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [18]
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 [18]
Other papers were more demonstrative, emphasizing workflow feasibility, design navigation, or case-based success rather than tightly standardized metrics.
“Other papers were more demonstrative, emphasizing workflow feasibility, design navigation, or case-based success rather than tightly standardized metrics [17,18,21,22,35,37-39,41].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [18]
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Self-citationJablonka, K. M., Ai, Q., Al‐Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S. A., Brinson, L. C., Choudhary, K. K., Çırcı, D., et al. (2023) · PubMed Central · doi:10.25418/crick.24297949.v1 · Cited by 29 · see citing works
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Self-citationMultiscale Modeling of Deformation in Polycrystalline Thin Metal Films on Substrates
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Self-citationPRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model
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Self-citationNeural network for predicting Peierls barrier spectrum and its influence on dislocation motion
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An Interdisciplinary Outlook on Large Language Models for Scientific ResearchPreprint
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AI-accelerated materials informatics method for the discovery of ductile alloys
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Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent SystemsPreprint
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Self-citationCephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and DesignPreprint
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Self-citation
- Authors
- Ghafarollahi, A., & Buehler, M. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Buehler, Markus J.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2025-01-24
- Volume
- 122
- Issue
- 4
- Pages
- e2414074122-e2414074122
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC11789045
- OpenAlex
- W4406794696
- Cited by
- 72
- References
- 103
- Citations by year
- 2026: 56 · 2025: 15 · 2023: 1
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- USDA (2021-69012-35978)
- Swiss National Science Foundation (#P500PT_214448)
- ARO (79058LSCSB W911NF-22-2-0213 and W911NF2120130)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Material Selection and Properties
- Physical Sciences › Engineering › Mechanical Engineering › Thermodynamic and Structural Properties of Metals and Alloys
- Keywords
- alloy design, multi-agent systems, large language models, generative AI, physics-informed machine learning, multimodal data integration, knowledge retrieval, solid solution alloying, alloys
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