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.
- Open Access
- Review
Large Language Models in Nanoparticles Research: Methods, Applications, Impacts, and Future Directions
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Received: 24 May 2026 | Revised: 10 Jul 2026 | Accepted: 13 Jul 2026 | Published: 28 Jul 2026
Abstract
References
- 1.
Xie, T.; Wan, Y.; Li, W.; et al. Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks. arXiv 2022, arXiv:2212.02805.
- 2.
Xie, T. Leveraging Large Language Models for Nanomaterials Discovery and Synthesis. Ph.D. Thesis, UNSW Sydney, Sydney, Australia, 2025. https://doi.org/10.26190/UNSWORKS/30859.
- 3.
Kim, E.; Huang, K.; Tomala, A.; et al. Machine-learned and codified synthesis parameters of oxide materials. Sci. Data 2017, 4, 170127. https://doi.org/10.1038/sdata.2017.127.
- 4.
Butler, K.T.; Davies, D.W.; Cartwright, H.; et al. Machine learning for molecular and materials science. Nature 2018, 559, 547–555. https://doi.org/10.1038/s41586-018-0337-2.
- 5.
Minaee, S.; Mikolov, T.; Nikzad, N.; et al. Large Language Models: A Survey. arXiv 2024, arXiv:2402.06196.
- 6.
Buehler, M.J. Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design. ACS Eng. Au 2024, 4, 241–277. https://doi.org/10.1021/acsengineeringau.3c00058.
- 7.
Shen, Y.; Zhao, S.; Lv, Y.; et al. Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials. Comput. Mater. Contin. 2025, 84, 1921–1950. https://doi.org/10.32604/cmc.2025.067427.
- 8.
Arya, A.; Garg, D.; Parashar, A.; et al. Nano-AI synergy: Emerging trends, applications, challenges, and future prospects. Nanotechnology 2025, 8, 100309. https://doi.org/10.1016/j.nxnano.2025.100309.
- 9.
Han, S.; Wang, M.; Zhang, J.; et al. A Review of Large Language Models: Fundamental Architectures, Key Technological Evolutions, Interdisciplinary Technologies Integration, Optimization and Compression Techniques, Applications, and Challenges. Electronics 2024, 13, 5040. https://doi.org/10.3390/electronics13245040.
- 10.
Peters, M.E.; Neumann, M.; Iyyer, M.; et al. Deep Contextualized Word Representations. arXiv 2018, arXiv:1802.05365.
- 11.
Mikolov, T.; Chen, K.; Corrado, G.; et al. Efficient Estimation of Word Representations in Vector Space. arXiv 2013, arXiv:1301.3781.
- 12.
Zhang, H.; Yu, P.S.; Zhang, J. A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models. arXiv 2024, arXiv:2406.11289.
- 13.
Duan, Y.; Zhang, Y.; Yu, R.; et al. Information extraction for materials science using large language models: A survey of recent advances and new frontiers. Mater. Today Commun. 2026, 54, 115551.
- 14.
Qin, L.; Chen, Q.; Feng, X.; et al. Large language models meet NLP: A survey. Front. Comput. Sci. 2026, 20, 2011361.
- 15.
Wang, H.; Li, J.; Wu, H.; et al. Pre-Trained Language Models and Their Applications. Engineering 2023, 25, 51–65. https://doi.org/10.1016/j.eng.2022.04.024.
- 16.
Pennington, J.; Socher, R.; Manning, C. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, Association for Computational Linguistics, 25–29 October 2014; pp. 1532–1543. https://doi.org/10.3115/v1/D14-1162.
- 17.
Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention Is All You Need. arXiv 2023, arXiv:1706.03762.
- 18.
Devlin, J.; Chang, M.-W.; Lee, K.; et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv 2019, arXiv:1810.04805.
- 19.
Liu, Y.; Ott, M.; Goyal, N.; et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv 2019, arXiv:1907.11692.
- 20.
He, P.; Liu, X.; Gao, J.; et al. DeBERTa: Decoding-enhanced BERT with Disentangled Attention. arXiv 2020, arXiv:2006.03654.
- 21.
Radford, A.; Wu, J.; Child, R.; et al. Language Models are Unsupervised Multitask Learners. OpenAI Blog 2019, 1, 9.
- 22.
OpenAI.; Achiam, J.; Adler, S.; et al. GPT-4 Technical Report. arXiv 2024, arXiv:2303.08774.
- 23.
Touvron, H.; Lavril, T.; Izacard, G.; et al. LLaMA: Open and Efficient Foundation Language Models. arXiv 2023, arXiv:2302.13971.
- 24.
Raffel, C.; Shazeer, N.; Roberts, A.; et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv 2023, arXiv:1910.10683.
- 25.
Chung, H.W.; Hou, L.; Longpre, S.; et al. Scaling Instruction-Finetuned Language Models arXiv 2022, arXiv:2210.11416.
- 26.
Naveed, H.; Khan, A.U.; Qiu, S.; et al. A Comprehensive Overview of Large Language Models. arXiv 2024, arXiv:2307.06435.
- 27.
Kaplan, J.; McCandlish, S.; Henighan, T.; et al. Scaling Laws for Neural Language Models. arXiv 2020, arXiv:2001.08361.
- 28.
Ouyang, L.; Wu, J.; Jiang, X.; et al. Training language models to follow instructions with human feedback. arXiv 2022, arXiv:2203.02155.
- 29.
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; et al. Parameter-Efficient Transfer Learning for NLP. arXiv 2019, arXiv:1902.00751.
- 30.
Hu, E.J.; Shen, Y.; Wallis, P.; et al. LoRA: Low-Rank Adaptation of Large Language Models. arXiv 2021, arXiv:2106.09685.
- 31.
Lester, B.; Al-Rfou, R.; Constant, N. The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic; Association for Computational Linguistics; 7–11 November 2021; pp. 3045–3059. https://doi.org/10.18653/v1/2021.emnlp-main.243.
- 32.
Du, N.; Huang, Y.; Dai, A.M.; et al. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. arXiv 2022, arXiv:2112.06905.
- 33.
Lewis, P.; Perez, E.; Piktus, A.; et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv 2021, arXiv:2005.11401.
- 34.
Sanh, V.; Debut, L.; Chaumond, J.; et al. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv 2020, arXiv:1910.01108.
- 35.
Liu, H.; Li, C.; Wu, Q.; et al. Visual Instruction Tuning. arXiv 2023, arXiv:2304.08485.
- 36.
Wang, A.; Pruksachatkun, Y.; Nangia, N.; et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. arXiv 2020, arXiv:1905.00537.
- 37.
Hendrycks, D.; Burns, C.; Basart, S.; et al. Measuring Massive Multitask Language Understanding. arXiv 2021, arXiv:2009.03300.
- 38.
Chen, M.; Tworek, J.; Jun, H.; et al. Evaluating Large Language Models Trained on Code. arXiv 2021, arXiv:2107.03374.
- 39.
Jiang, X.; Wang, W.; Tian, S.; et al. Applications of natural language processing and large language models in materials discovery. npj Comput. Mater. 2025, 11, 79. https://doi.org/10.1038/s41524-025-01554-0.
- 40.
Schilling-Wilhelmi, M. From text to insight: Large language models for chemical data extraction. Chem. Soc. Rev. 2025, 54, 1125–1150. https://doi.org/10.1039/D4CS00913D.
- 41.
Zhao, P.; Zhang, H.; Yu, Q.; et al. Retrieval-Augmented Generation for AI-Generated Content: A Survey. arXiv 2024, arXiv:2402.19473.
- 42.
Singhal, K.; Azizi, S.; Tu, T.; et al. Large language models encode clinical knowledge. Nature 2023, 620, 172–180. https://doi.org/10.1038/s41586-023-06291-2.
- 43.
Hogan, A.; Blomqvist, E.; Cochez, M.; et al. Knowledge Graphs. ACM Comput. Surv. 2022, 54, 1–37.
- 44.
Ramos, M.C.; Collison, C.J.; White, A.D. A review of large language models and autonomous agents in chemistry. Chem. Sci. 2025, 16, 2514–2572. https://doi.org/10.1039/D4SC03921A.
- 45.
Dastgeer, G.; Shahzad, Z.M.; Chae, H.; et al. Bipolar Junction Transistor Exhibiting Excellent Output Characteristics with a Prompt Response against the Selective Protein. Adv. Funct. Mater. 2022, 32, 2204781. https://doi.org/10.1002/adfm.202204781.
- 46.
Parveen, S.; Misra, R.; Sahoo, S.K. Nanoparticles: A boon to drug delivery, therapeutics, diagnostics and imaging. Nanomedicine Nanotechnol. Biol. Med. 2012, 8, 147–166. https://doi.org/10.1016/j.nano.2011.05.016.
- 47.
Mura, S.; Nicolas, J.; Couvreur, P. Stimuli-responsive nanocarriers for drug delivery. Nat. Mater. 2013, 12, 991–1003. https://doi.org/10.1038/nmat3776.
- 48.
Topol, E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 2019, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7.
- 49.
O’Brien, F.J. Biomaterials & scaffolds for tissue engineering. Mater. Today 2011, 14, 88–95. https://doi.org/10.1016/S1369-7021(11)70058-X.
- 50.
Dagdelen, J.; Dunn, A.; Lee, S.; et al. Structured information extraction from scientific text with large language models. Nat. Commun. 2024, 15, 1418.
- 51.
Motmans, B. Machine Learning Small Datasets for Cu Nanoparticles: Enhancing Experimental and Computational lab-scale data. Master’s Thesis, Hasselt University, Hasselt, Belgium 2025.
- 52.
Pu, Y.; Huang, L.; Lin, T.; et al. Leveraging Large Language Models for Explaining Material Synthesis Mechanisms: The Foundation of Materials Discovery. In Proceedings of the AI for Accelerated Materials Design—NeurIPS 2024, Amherst, MA, USA, 14 December 2024.
- 53.
Dubrovsky, I.; Dmitrenko, A.; Dmitrenko, A.; et al. Unveiling the Potential of AI for Nanomaterial Morphology Prediction. arXiv 2024, arXiv:2406.02591.
- 54.
Lee, S.; Cruse, K.; Gleason, S.P.; et al. Data-driven analysis of text-mined seed-mediated syntheses of gold nanoparticles. Digit. Discov. 2025, 4, 93–104. https://doi.org/10.1039/D4DD00158C.
- 55.
Lee, S.; Cruse, K.; Gleason, S.P.; et al. Data-driven analysis of text-mined seed-mediated growth AuNP syntheses. ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2024-ncjlp.
- 56.
Sanchez, S.L.; Kim, Y.; Fuhr, A.S.; et al. LLM-Guided Ligand Design for Controlling Nanoscopic Morphology of CsPbBr3 Perovskite Nanocrystals. ChemRxiv 2025. https://doi.org/10.26434/chemrxiv-2025-jpp14.
- 57.
Lv, R. Machine Learning for Controlling the Particle Size Distribution of TiO2 Nanoparticles in Optical Nanocomposites. ACS Appl. Nano Mater. 2025, 8, 15323–15335. https://doi.org/10.1021/acsanm.5c02743.
- 58.
Abdulai, H. Employing Large Language Models for Systematic Extraction of Nanoparticle Designs from Scientific Literature. Master’s Thesis, University of the Balearic Islands, Palma, Spain, 2024.
- 59.
Wang, Y.; Song, H.; Teng, Y.; et al. A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering. ACS Nano 2025, 19, 19394–19407. https://doi.org/10.1021/acsnano.5c03454.
- 60.
Moore, T.L.; Pesce, C.; Greco, A.; et al. Unleashing the Power of Machine Learning in Nanomedicine Formulation Development. Adv. Funct. Mater. 2025, 14387. https://doi.org/10.1002/adfm.202514387.
- 61.
Moayedpour, S.; Broadbent, J.; Riahi, S.; et al. Representations of lipid nanoparticles using large language models for transfection efficiency prediction. Bioinformatics 2024, 40, 342. https://doi.org/10.1093/bioinformatics/btae342.
- 62.
Ha, E.; Ha, S.M.; Gerelkhuu, Z.; et al. AI-based nanotoxicity data extraction and prediction of nanotoxicity. Comput. Struct. Biotechnol. J. 2025, 29, 138–148. https://doi.org/10.1016/j.csbj.2025.03.052.
- 63.
Kim, M.; Park, H.; Shin, J. Nanophotonic device design based on large language models: Multilayer and metasurface examples. Nanophotonics 2025, 14, 1273–1282. https://doi.org/10.1515/nanoph-2024-0674.
- 64.
Liu, X.; Sun, P.; Chen, S.; et al. Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research. arXiv 2025, arXiv:2502.12669.
- 65.
Peng, C.; Wang, B.; Wu, L.; et al. AI-Driven Discovery and Molecular Engineering Design for Enhancing Interface Stability of Black Phosphorus. Angew. Chem. Int. Ed. 2025, 64, 202508454. https://doi.org/10.1002/anie.202508454.
- 66.
Lin, J.; Zhao, D.; Lu, S.; et al. Conversational Large-Language-Model Artificial Intelligence Agent for Accelerated Synthesis of Metal–Organic Frameworks Catalysts in Olefin Hydrogenation. ACS Nano 2025, 19, 23840–23858. https://doi.org/10.1021/acsnano.5c04880.
- 67.
Marshall, J.; Sanchez, S.; Desai, R.; et al. POLARIS: Perovskite Optimization using LLM-Assisted Refinement and Intelligent Screening. ChemRxiv 2025. https://doi.org/10.26434/chemrxiv-2025-qqchd-v2.
- 68.
Faheem, A.B.; Han, Z.; Wu, D.; et al. AI-Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries. Adv. Mater. 2026, 38, 21975. https://doi.org/10.1002/adma.202521975.
- 69.
Zhang, L.; Bing, Q.; Qin, H.; et al. Artificial intelligence for catalyst design and synthesis. Matter 2025, 8, 102138. https://doi.org/10.1016/j.matt.2025.102138.
- 70.
Xian, Z.; Gu, J.; Li, L.; et al. MolRAG: Unlocking the Power of LLMs for Molecular Property Prediction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, 27 July–1 August 2025.
- 71.
Zhou, P.; Tim, L.H.; Cheng, Z.; et al. Enhancing Molecular Property Prediction with Knowledge from Large Language Models. arXiv 2025, arXiv:2509.20664.
- 72.
Krotkov, N.A.; Sbytov, D.A.; Chakhoyan, A.A.; et al. Nanostructured Material Design via a Retrieval-Augmented Generation (RAG) Approach: Bridging Laboratory Practice and Scientific Literature. J. Chem. Inf. Model 2025, 65, 11064–11078. https://doi.org/10.1021/acs.jcim.5c01897.
- 73.
Pu, Y.; Huang, L.; Lin, T.; Chen, H.; et al. Leveraging large language models for nano synthesis mechanism explanation: Solid foundations or mere conjectures? arXiv 2024, arXiv:2407.08922.
- 74.
Chandrasekhar, A.; Farimani, O.B.; Ajenifujah, O.T.; et al. NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research. arXiv 2025, arXiv:2502.20541.
- 75.
Zhang, B.; Li, Z.; Yang, M.; et al. Initiation performance of spherical LLM-105 with micro-nano structure under short-duration pulses. Mater. Today Commun. 2025, 47, 113088. https://doi.org/10.1016/j.mtcomm.2025.113088.
- 76.
Wang; Yin, X.; Wang, M.; et al. EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent. arXiv 2024, arXiv:2412.18100.
- 77.
Song, Z.; Zhou, Q.; Ren, C.; et al. LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Knowledge Discovery. arXiv 2025, arXiv:2503.06512.
- 78.
Yin, T.; Feng, R.; Bao, J.; et al. Learning Advance: Robotics-LLM Guided Hypotheses Generation for the Discovery of Chemical Knowledge. ChemRxiv 2025. https://doi.org/10.26434/chemrxiv-2025-n1b4l.
- 79.
Su, H.; Chen, R.; Tang, S.; et al. Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System. arXiv 2024, arXiv:2410.09403.
- 80.
Bajan, C.; Lambard, G. Exploring the expertise of large language models in materials science and metallurgical engineering. Digit. Discov. 2025, 4, 500–512. https://doi.org/10.1039/D4DD00319E.
- 81.
Han, S.; Bae, T.; Kim, J.; et al. Large language models for porous materials: From text mining to autonomous laboratory. Digit. Discov. 2026, 5, 1470–1500. https://doi.org/10.1039/D5DD00578G.
- 82.
Panapitiya, G.; Saldanha, E.; Job, H.; et al. AutoLabs: Cognitive multi-agent systems with self-correction for autonomous chemical experimentation. Sci. Rep. 2026, 16, 19554. https://doi.org/10.1038/s41598-026-45593-z.
- 83.
Bai, X.; Zheng, Z.; Zhang, X.; et al. MOFReasoner: Think like a scientist—A reasoning large language model via knowledge distillation. Digit. Discov. 2026, 5, 869–877. https://doi.org/10.1039/D5DD00429B.
- 84.
Abhyankar, N.; Kabra, S.; Desai, S.; et al. Llema: Evolutionary Search with Llms for Multi-Objective Materials Discovery. arXiv 2026, arXiv:2510.22503.
- 85.
Zimmermann, Y.; Bazgir, A.; Afzal, Z.; et al. Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry. arXiv 2025, arXiv:2411.15221.
- 86.
Jablonka, K.M.; Schwaller, P.; Ortega-Guerrero, A.; et al. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 2024, 6, 161–169. https://doi.org/10.1038/s42256-023-00788-1.
- 87.
Qian, C.; Tang, H.; Yang, Z.; et al. Can Large Language Models Empower Molecular Property Prediction? arXiv 2023, arXiv:2307.07443.
- 88.
Jacobs, R.; Polak, M.P.; Schultz, L.E.; et al. Regression with Large Language Models for Materials and Molecular Property Prediction. arXiv 2024, arXiv:2409.06080.
- 89.
Bhattacharya, D.; Cassady, H.J.; Hickner, M.A.; et al. Large Language Models as Molecular Design Engines. J. Chem. Inf. Model 2024, 64, 7086–7096. https://doi.org/10.1021/acs.jcim.4c01396.
- 90.
Liu, G.; Sun, M.; Matusik, W.; et al. Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning. arXiv 2024, arXiv:2410.04223.
- 91.
Jia, S.; Zhang, C.; Fung, V. LLMatDesign: Autonomous Materials Discovery with Large Language Models. arXiv 2024, arXiv:2406.13163.
- 92.
Jang, H.; Jang, Y.; Kim, J.; et al. Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity. arXiv 2025, arXiv:2410.03138.
- 93.
Lu, J.; Song, Z.; Zhao, Q.; et al. Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models. arXiv 2024, arXiv:2410.18136.
- 94.
Kristiadi, A.; Strieth-Kalthoff, F.; Skreta, M.; et al. A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? arXiv 2024, arXiv:2402.05015.
- 95.
Miret, S.; Krishnan, N.M.A. Are LLMs Ready for Real-World Materials Discovery? arXiv 2024, arXiv:2402.05200.
- 96.
Li, C.; Ma, L.; Xue, Z.; et al. Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles. Adv. Sci. 2025, 12, 2501000. https://doi.org/10.1002/advs.202501000.
- 97.
Krishnamoorthy, U.; Balasubramani, S. Intelligent Nanomaterial Image Characterizations—A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience. Adv. Theory Simul. 2024, 7, 2400479. https://doi.org/10.1002/adts.202400479.
- 98.
Bran, A.M.; Cox, S.; Schilter, O.; Augmenting large language models with chemistry tools. arXiv 2023, arXiv:2304.05376.
- 99.
Tang, Y.; Xu, W.; Cao, J.; et al. MatterChat: A Multi-Modal LLM for Material Science. arXiv 2025, arXiv:2502.13107.
- 100.
Moro, V.; Loh, C.; Dangovski, R.; et al. Multimodal Foundation Models for Material Property Prediction and Discovery. Newton 2025, 1, 100016. https://doi.org/10.1016/j.newton.2025.100016.
- 101.
Scott-Fordsmand, J.J.; Amorim, M.J.B. Using Machine Learning to make nanomaterials sustainable. Sci. Total Environ. 2023, 859, 160303. https://doi.org/10.1016/j.scitotenv.2022.160303.
- 102.
Serrano, D.R.; Luciano, F.C.; Anaya, B.J.; et al. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics 2024, 16, 1328. https://doi.org/10.3390/pharmaceutics16101328.
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