Agentic additive manufacturing alloy evaluation
Open accessAbstract
Agentic systems enable the intelligent use of research tooling, augmenting a researcher’s ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy selection and evaluation remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch tool calls via Model Context Protocol (MCP) to perform actions such as thermophysical property diagram calculations and lack of fusion process map generation. In addition, the multi-agent system can effectively reason through complex user prompts and provide analysis on the lack of fusion process window of common alloys such as SS316L and IN718 along with proposed composition variants of known alloys. These agents can dynamically adjust their task trajectory to the outcomes of tool call results, effectively enabling autonomous decision-making in practical environments. This work aims to showcase the benefits of adopting a LLM enabled multi-agent system to automate and accelerate the task of evaluating proposed additive manufacturing alloys, both novel and known. • A multi-agent system is developed for the intelligent automation of the proposal, evaluation, and selection of known and novel alloy compositions for the Additive Manufacturing (AM) process. • Material properties of novel alloy compositions are obtained through the generation of thermophysical property diagrams using Thermo-Calc invoked via tool calls. • Print feasibility is assessed by computing lack of fusion process maps using melt pool dimensions generated with Rosenthal’s approximation of a moving heat source. • The Model Context Protocol (MCP) is utilized by the multi-agent system for end-to-end integration with Large Language Model (LLM) providers such as Anthropic, OpenAI, and Google.
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A second group of studies used hybrid human-in-the-loop designs, where the agentic layer guided design or analysis but final judgment remained partly manual.
“A second group of studies used hybrid human-in-the-loop designs, where the agentic layer guided design or analysis but final judgment remained partly manual [20,22,27,30,31].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [31]
The Discussion (p. 26) describes a researcher interacting with the tools and receiving the agent’s interpretive feedback, while the Conclusion (p. 27) calls its predictions a starting point for experimental trials. This supports an agentic system that assists human analysis, but the paper does not establish that final judgments in the reported study were made partly manually. (LLM, conf=0.80)
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 [31]
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 [31]
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 [31]
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 [31]
Hybrid human-in-the-loop systems accounted for four of the six experimentally validated papers, which suggests that human oversight appears to be retained more often where the downstream consequences of an incorrect decision are more costly or irreversible, although the small sample precludes strong inference.
“Hybrid human-in-the-loop systems accounted for four of the six experimentally validated papers, which suggests that human oversight appears to be retained more often where the downstream consequences of an incorrect decision are more costly or irreversible, although the small sample precludes strong inference [20,22,27,30,31].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [31]
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Sorted by citations
Alviz-Meza, A., Valencia-Arias, A., Rojas-Flores, S., & Diaz, F. (2026) · Data · doi:10.3390/data11070180
- Authors
- Pak, P., Chandrasekhar, A., & Barati Farimani, A.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Additive Manufacturing Letters
- Publisher
- Elsevier BV
- Published
- 2026-04
- Volume
- 17
- Pages
- 100355
- ISSN
- 2772-3690 (ISSN-L)
- OpenAlex
- W4415981619
- Cited by
- 4
- References
- 54
- Citations by year
- 2026: 4
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Additive Manufacturing Letters (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Keywords
- agentic systems, large language models, multi-agent systems, alloy selection, additive manufacturing alloys, lack of fusion, process window, Thermo-Calc, SS316L, IN718, alloy composition design, Model Context Protocol, tool calling
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