Although Large Language Models (LLM) and Artificial Intelligence (AI) tools have enabled a rapid increase in the generation rate of predicted materials, the rate of new materials discovery has lagged behind. This is due to the challenges associated with designing a sequence of chemical reactions to predictably produce new materials, especially in new structure types. Here, we report a study of human and LLM generated recipes for the synthesis of known and new materials. The success of the recipes is determined through in-lab experimentation, and the results are passed back to the humans and LLMs in a closed-loop process to study the effects of their collaboration. The Ruddlesden-Popper homologous series was selected for all material candidates to provide a materials phase space that is simultaneously well studied and likely to host undiscovered materials. We find that humans (H) and LLM (L) have similar success rates: 83(8)% (H) and 75(9)% (L) [known materials, round one], 17(9)% (H) and 22(10)% (L) [unknown materials, round one], 79(8)% (H) and 71(9)% (L) [known materials, round two], and 22(7)% (H) and 14(6)% (L) [unknown materials, round two]. Through this collaborative human-LLM effort, we discovered Ba3PtO5, a material with a new structural prototype that constitutes the missing 1D member of the herein reported dimensionally tunable Rock-Salt Perovskite (RSP) homologous series of the form (AX)m(ABX3)p, of which the Ruddlesden-Popper series is a subset.
Developing reliable synthesis routes for complex materials remains a major bottleneck in accelerating materials discovery. This study establishes a large language model-based framework for predicting and optimizing synthesis conditions directly from the literature data. Key synthesis information, including target compounds, precursors, and processing parameters, was systematically extracted from 4407 open-access solid-state synthesis papers and organized into a structured recipe dataset. Using a retrieval-augmented generation (RAG) approach, the system first retrieves similar recipes from the corpus and then generates a new candidate recipe conditioned on those exemplars. The generated recipes were benchmarked against literature data using quantitative scoring metrics, achieving strong agreement with experimentally reported conditions. To validate the predictive capability, the framework was applied to unreported solid-state electrolyte candidates identified through first-principles screening, and multiple oxy-selenide compounds were successfully synthesized through iterative feedback between the model and experiment. The recipe generator accurately refined synthesis parameters over successive trials, demonstrating its ability to reproduce phase-pure products while minimizing trial-and-error. This approach establishes a data-driven, feedback-optimized route to accelerate synthesis design, offering a generalizable paradigm for integrating language models into experimental materials research.
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon et al.· Advances in Materials· 0 citations
By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.
Yun-Tong Chen, Ju Huang, Yu Liu et al.· 0 citations
Large Language Models (LLMs) have emerged as a cutting-edge tool in the era of advanced science and technology, in materials science by enabling novel approaches such as the development of open-source libraries, intelligent research tools, AI accelerated design platforms, AI driven morphology prediction and related innovations. By 2026, a remarkable transition is evident from experimental adoption of artificial intelligence towards its large-scale integration across diverse industries driven by LLMs to understand complex tasks and execute with high accuracy. This review underscores the potential of LLMs in the advancement of nanomaterial and nanoparticle research and positioning them as an alternative-approaches to conventional computational and experimental methods. This study also analyzes and summarizes procedure of LLM-based methodologies in nanomaterial research like automated knowledge extraction, Retrieval-Augmented Generation (RAG), domain specific fine tuning, hypothesis generation and synthesis planning, knowledge graph construction, LLM based agents and research workflows. Moreover, the implementation of LLMs across various domains, such as environmental sustainability, drug delivery systems, health surveillance, biomaterials, and tissue engineering, is discussed in this literature review, which also proposes future research directions to guide further advancements in this rapidly evolving field.
M. Nafiz, Chowdhury Emon, J. N. Ethica et al.· AI for Materials· 0 citations
Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are highly tunable in pore structure and chemical environment, yet their discovery remains slow and fragmented. Synthesis reports are often difficult to compare, characterization data are laborious to interpret, and computational predictions rarely guide experiments directly. Recent advances in large language models (LLM) have enabled the development of artificial intelligence (AI) agents that can interpret research goals, search the literature and databases, call external tools, and adapt workflows based on intermediate results. In this review, we distinguish three stages of AI-agent development in MOFs and COFs research: LLM-native, human-mediated systems; database-grounded, tool-using agents; and experiment-integrated, feedback-driven platforms. This progression reflects increasing scientific grounding and experimental agency. In our view, further progress will depend less on scaling language models alone than on developing traceable machine-actionable data, chemistry-aware validation, persistent experimental memory, and robust interfaces between AI agents and laboratory automation.
Jia-Yu Yu, Zihao Jiang, Donglin He· AI Agent· 0 citations
This work introduces a domain specific language (DSL)-guided strategy to improve the reasoning and design capability of LLM agents by translating natural language design rules into symbolic predicates encoded in a predefined chemistry DSL, and developed a multi-agent materials design framework.
Dong Hyeon Mok, Seoin Back, Victor Fung et al.· 0 citations
Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial
intelligence is transforming the field, helping to predict molecular behavior, find potential designs,
and automate certain aspects of the discovery process, introducing a new kind of intuition. Over
the past decade, advances in machine learning, natural language processing, robotics, and automation
have enabled new areas of research. These are broadening the applications for retrosynthetic analysis,
reaction optimization, and computer-aided synthetic planning. This study examines the evolution of
computer-aided synthesis, describing its development from rule-based approaches to advanced deep
learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate
predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid
hypothesis generation, reaction screening, and the improvement of synthetic methods. The review
encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver
discoveries more quickly and minimize waste and environmental impact. It examines current challenges,
such as data scarcity, sporadic reporting, model interpretability, and practical applications.
More broadly, the need for sustainable, collaborative research has increased, and cross-border work
through cloud-based laboratories and shared databases enables chemists worldwide to share resources.
The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities
for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic
and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition,
bringing together research that traditional methods would never have allowed, on a scale never before
possible without AI.
Rizvee Ahmad Samir, Yu-Meng Zhang, Zi-Shan Xu et al.· Letters in Organic Chemistry· 0 citations
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