M eta S cientist: A Human- AI Synergistic Framework for Automated Mechanical Metamaterial Design (2025)

Abstract: The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field.

## introduction metamaterials are microstructured materials whose properties go beyond those of the ingredient materials, often possessing unusual mechanical and/or functional properties @xcite @xcite @xcite . the advancements in metamaterials have fostered a vast amount of technological innovations across various key domains, such as structural, sensing, actuation, and multifunctional materials innovations. however, human scientists are facing two critical challenges in designing and discovering novel metamaterials. first, as the field of metamaterial expands exponentially, it becomes increasingly challenging for researchers to keep pace with the rapid accumulation of knowledge. for instance, the number of metamaterial-related publications has grown from 156 papers in 2000, 7,170 papers in 2010, to over 30,000 papers in 2023 based on google scholar statistics. the rapid proliferation of research has created significant barriers to effectively understanding and building upon existing work, ultimately slowing down the pace of technological innovation. second, existing workflows for metamaterial design impose substantial cognitive and resource burdens throughout their multi-stage development process. from initial conceptualization through theoretical modeling, computational simulation, and experimental validation, each phase demands significant human expertise and creativity while incurring considerable time and financial costs. these combined constraints severely limit both the exploration of new concepts and the scaling of successful designs for practical applications. recent breakthroughs in large language models (llms) have inspired the development of autonomous scientists to automate aspects of the research process, such as generating novel research ideas and outlining implementation procedures @xcite . however, in specialized scientific domains like metamaterial design, several recent studies @xcite @xcite have highlighted significant limitations even in state-of-the-art models like @xcite . while these models can boldly synthesize vast amounts of scientific knowledge and propose testable hypotheses, they are prone to high factual error rates, often stemming from confusion caused by similar nomenclatures across different fields or lack of rigorous logical reasoning and mathematical derivation capabilities, which are critical for scientific research. in addition, these models typically fail to assess the plausibility of their hypotheses effectively, and their proposals are often overly generic, offering limited practical value for real-world experiments. 2 to tackle these challenges, we introduce meta-scientist, a human-in-the-loop system for assisting and accelerating novel metamaterial design, with a particular focus on mechanical metamaterials (e.g., lattice-based structures with superior mechanical properties). as depicted in figure 1 , metascientist comprises two key phases: (1) hypothesis generation: given an input research question, we perform complex reasoning upon evidence and inductive biases retrieved from a large collection of materials science literature to generate novel hypotheses. (2) 3d structure synthesis: we build a 3d diffusion model to transform the generated textual hypotheses into 3d metamaterial structures, and then refine the generated structures with a llm-based refinement model to achieve better structure properties (e.g., symmetry and periodicity) 3 . throughout both phases, we involve human scientists to actively interact with our system and iteratively provide diverse types of feedback, including cutting-edge insights, error correction, and supplementary materials, to ensure the generated hypotheses are feasible, novel, and aligned with upto-date domain knowledge in metamaterials. our contributions are summarized as follows: from google scholar based on a list of keywords that are provided by a domain expert and related to metamaterial design or general domain knowledge of materials science. we then design a comprehensive data-cleaning process and convert them into plain text. 4 based on this corpus, we finetune an open-sourced llm, i.e., llama3-8b-instruct, using the low-rank adaptation (lora) technique @xcite , and tailor the model to the metamaterials science domain. to qualitatively evaluate this model, we ask a domain expert to provide a list of question-answer pairs and compare it against an open-source llm baseline. foot_3 ## related works automated scientific discovery has progressed significantly given the recent advancements of large foundation models (llms) @xcite @xcite @xcite @xcite , such as logical reasoning @xcite , multi-agent collaboration @xcite @xcite , automatic evaluation @xcite @xcite , and tool and retrieval-based augmentation @xcite @xcite . however, the application of ai and llms for autonomous novel material discovery faces several challenges, including high error rates and hallucinations @xcite , lacking inter-pretability @xcite , rigorous logical and mathematical reasoning capabilities @xcite @xcite , leading to scientifically unsound or unverifiable hypotheses. additionally, llms often struggle to extrapolate beyond existing knowledge due to the nature of their pre-training objective @xcite . to address these challenges, metascientist employs a human-inthe-loop approach to integrate expert feedback and domain-specific knowledge into the design of novel materials, which significantly mitigates hallucinations and aligns outputs with scientific principles. ## conclusion we propose metascientist that accelerates the discovery of novel metamaterials by integrating advanced ai capabilities with expert oversight. experiments and human evaluation demonstrate that metascientist can generate reasonable and valid metamaterial design given the user's research question. future works include improving the validity of lattice properties using more advanced retrieval techniques and building more powerful models to generate 3d metamaterial structures in more diverse scenarios. we believe our work has significant potential to advance the field of metamaterial design and other broad 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