Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design
Open accessAbstract
Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.
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The remaining literature was distributed across general materials-workflow papers, atomistic simulation infrastructures, MOF and porous-material screening, and polymer simulation.
“The remaining literature was distributed across general materials-workflow papers, atomistic simulation infrastructures, MOF and porous-material screening, and polymer simulation [17,21,22,24-26,32,35-42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [17]
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 [17]
In other papers, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete.
“In others, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete [17,22,24,26,27,29,33,34].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [17]
The Results and Discussion section describes agent workflows and says Table 9 summarizes a conversation; the Figure 8 caption says generated code is stored for later retrieval. Searches for supporting information, prompt logs, failure traces, and decision histories found no passage assessing whether those records were incomplete or the workflow could only be partially reconstructed. The cited paper’s text therefore does not establish this claim about its reporting. (LLM, conf=0.70)
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Development and testing of a general amber force field
Wang, J.-M., Wolf, R., Caldwell, J., Kollman, P., & Case, D. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 18,318 · see citing works
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218 · see citing works
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 2,523 · see citing works
Density-Functional Theory of Atoms and Molecules
Parr, R. G., & Weitao, Y. (1995) · Oxford University Press eBooks · doi:10.1093/oso/9780195092769.001.0001 · Cited by 2,237 · see citing works
Anderson, T. L., & Anderson, T. L. (2005) · CRC Press eBooks · doi:10.1201/9781420058215 · Cited by 1,862 · see citing works
P y SCF: the Python‐based simulations of chemistry framework
Sun, Q., Berkelbach, T. C., Blunt, N. S., Booth, G. H., Guo, S., Li, Z., Liu, J., McClain, J. D., Sayfutyarova, E. R., Sharma, S., et al. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1340 · Cited by 1,795 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · see citing works
Recent developments in the P <scp>y</scp> SCF program package
Sun, Q., Zhang, X., Banerjee, S., Bao, P., Barbry, M., Blunt, N. S., Bogdanov, N. A., Booth, G. H., Chen, J., Cui, Z.-H., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0006074 · Cited by 1,259 · see citing works
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023) · arXiv · doi:10.48550/arxiv.2305.10601 · Cited by 1,158 · see citing works
Toolformer: Language Models Can Teach Themselves to Use Tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023) · arXiv · doi:10.48550/arxiv.2302.04761 · Cited by 1,150 · see citing works
Nanofibrils in nature and materials engineering
Ling, S., Kaplan, D. L., & Buehler, M. J. (2018) · Nature Reviews Materials · doi:10.1038/natrevmats.2018.16 · Cited by 732 · see citing works
Self-citationLeanDojo: Theorem Proving with Retrieval-Augmented Language ModelsPreprint
Yang, K., Swope, A. M., Gu, A., et al. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2306.15626 · Cited by 635 · see citing works
Galactica: A Large Language Model for Science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., & Stojnic, R. (2022) · arXiv · doi:10.48550/arxiv.2211.09085 · Cited by 605 · see citing works
QLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14314 · Cited by 507 · see citing works
Harrer, S. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104512 · Cited by 480 · see citing works
Shape insensitive optimal adhesion of nanoscale fibrillar structures
Gao, H., & Yao, H. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400757101 · Cited by 458 · see citing works
Protein-Folding Dynamics: Overview of Molecular Simulation Techniques
Scheraga, H. A., Khalili, M., & Liwo, A. (2006) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.58.032806.104614 · Cited by 398 · see citing works
OpenAGI: When LLM Meets Domain ExpertsPreprint
Ge, Y.-Q., Hua, W.-Y., Mei, K., et al. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2304.04370 · Cited by 378 · see citing works
General theory of natural equivalences
Eilenberg, S., & MacLane, S. (1945) · Transactions of the American Mathematical Society · doi:10.1090/s0002-9947-1945-0013131-6 · Cited by 372 · see citing works
The Hausdorff dimension of self-affine fractals
Falconer, K. J. (1988) · Mathematical Proceedings of the Cambridge Philosophical Society · doi:10.1017/s0305004100064926 · Cited by 361 · see citing works
Libcint: An efficient general integral library for Gaussian basis functions
Sun, Q. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23981 · Cited by 356 · see citing works
Large Language Model Guided Tree-of-ThoughtPreprint
Long, J.-Y. (2023) · arXiv.org · doi:10.48550/arxiv.2305.08291 · Cited by 350 · see citing works
Hyperelasticity governs dynamic fracture at a critical length scale
Buehler, M. J., ABRAHAM, F. F., & Gao, H. (2003) · Nature · doi:10.1038/nature02096 · Cited by 331 · see citing works
Self-citationLarge language models and the perils of their hallucinations
Azamfirei, R., Kudchadkar, S. R., & Fackler, J. C. (2023) · Critical Care · doi:10.1186/s13054-023-04393-x · Cited by 324 · see citing works
Dynamical fracture instabilities due to local hyperelasticity at crack tips
Buehler, M. J., & Gao, H. (2006) · Nature · doi:10.1038/nature04408 · Cited by 299 · see citing works
Self-citationSources of Hallucination by Large Language Models on Inference TasksPreprint
McKenna, N., Li, T., Cheng, L., Hosseini, M., Johnson, M., & Steedman, M. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2305.14552 · Cited by 297 · see citing works
Physics, Topology, Logic and Computation: A Rosetta Stone
Baez, J. C., & Stay, M. (2010) · Lecture notes in physics · doi:10.1007/978-3-642-12821-9_2 · Cited by 264 · see citing works
Flaw Insensitive Fracture in Nanocrystalline Graphene
Zhang, T., Li, X., Kadkhodaei, S., & Gao, H. (2012) · Nano Letters · doi:10.1021/nl301908b · Cited by 250 · see citing works
Atomistic Modeling of Materials Failure
Buehler, M. J. (2008) · Springer eBooks · doi:10.1007/978-0-387-76426-9 · Cited by 228 · see citing works
Self-citationAutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.08155 · Cited by 193 · see citing works
Large language models challenge the future of higher education
Milano, S., McGrane, J. A., & Leonelli, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00644-2 · Cited by 169 · see citing works
Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., & Launay, J. (2023) · arXiv · doi:10.48550/arxiv.2306.01116 · Cited by 164 · see citing works
Augmented Language Models: a Survey
Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.07842 · Cited by 160 · see citing works
Eilenberg, S., & MacLane, S. (1942) · Annals of Mathematics · doi:10.2307/1968966 · Cited by 155 · see citing works
Large Language Models as Optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., & Chen, X. (2023) · arXiv · doi:10.48550/arxiv.2309.03409 · Cited by 151 · see citing works
Large Language Models Struggle to Learn Long-Tail Knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2211.08411 · Cited by 143 · see citing works
Buckling of Carbon Nanotubes: A State of the Art Review
Shima, H. (2011) · Materials · doi:10.3390/ma5010047 · Cited by 142 · see citing works
Sources of Hallucination by Large Language Models on Inference Tasks
McKenna, N., Li, T., Cheng, L., Hosseini, M., Johnson, M., & Steedman, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.182 · Cited by 142 · see citing works
Buehler, M. J. (2023) · Applied Mechanics Reviews · doi:10.1115/1.4063843 · Cited by 132 · see citing works
Self-citationNougat: Neural Optical Understanding for Academic DocumentsPreprint
Blecher, L., Cucurull, G., Scialom, T., & Stojnic, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.13418 · Cited by 132 · see citing works
Varshney, N., Yao, W., Zhang, H., Chen, J., & Yu, D. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.03987 · Cited by 128 · see citing works
The Social Impact of Generative AI: An Analysis on ChatGPT
Baldassarre, M. T., Caivano, D., Nieto, B. F., Gigante, D., & Ragone, A. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609555 · Cited by 127 · see citing works
Chen, W., Ma, X., Wang, X., & Cohen, W. W. (2022) · arXiv · doi:10.48550/arxiv.2211.12588 · Cited by 125 · see citing works
Yu, C., Zhao Qin, A., Martín‐Martínez, F. J., & Buehler, M. J. (2019) · ACS Nano · doi:10.1021/acsnano.9b02180 · Cited by 123 · see citing works
Self-citationDeep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
Fuhr, A., & Sumpter, B. G. (2022) · Frontiers in Materials · doi:10.3389/fmats.2022.865270 · Cited by 120 · see citing works
Luu, R. K., & Buehler, M. J. (2023) · Advanced Science · doi:10.1002/advs.202306724 · Cited by 119 · see citing works
Self-citationDeep learning model to predict fracture mechanisms of graphene
Lew, A. J., Yu, C., Hsu, Y.-C., & Buehler, M. J. (2021) · npj 2D Materials and Applications · doi:10.1038/s41699-021-00228-x · Cited by 87 · see citing works
Self-citationNi, B., Kaplan, D. L., & Buehler, M. J. (2023) · Chem · doi:10.1016/j.chempr.2023.03.020 · Cited by 87 · see citing works
Self-citationAlpha-Helical Protein Networks Are Self-Protective and Flaw-Tolerant
Ackbarow, T., Sen, D., Thaulow, C. M., & Buehler, M. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006015 · Cited by 82 · see citing works
Self-citationDeep language models for interpretative and predictive materials science
Hu, Y., & Buehler, M. J. (2023) · APL Machine Learning · doi:10.1063/5.0134317 · Cited by 80 · see citing works
Self-citationCategory Theoretic Analysis of Hierarchical Protein Materials and Social Networks
Spivak, D. I., Giesa, T., Wood, E., & Buehler, M. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023911 · Cited by 75 · see citing works
Self-citationMicrowave synthesis of molybdenene from MoS2
Sahu, T. K., Kumar, N., Chahal, S., Jana, R., Paul, S., Mukherjee, M., Tavabi, A. H., Datta, A., Dunin‐Borkowski, R. E., Valov, I., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01484-2 · Cited by 71 · see citing works
Lew, A. J., & Buehler, M. J. (2023) · Materials Today · doi:10.1016/j.mattod.2023.03.007 · Cited by 70 · see citing works
Self-citationBuehler, M. J. (2023) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105454 · Cited by 68 · see citing works
Self-citationCranford, S. W., & Buehler, M. J. (2012) · Springer series in materials science · doi:10.1007/978-94-007-1611-7 · Cited by 62 · see citing works
Self-citationBuehler, M. J., Hartmaier, A., & Gao, H. (2003) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2003.09.024 · Cited by 56 · see citing works
Self-citationBuehler, M. J. (2022) · Materials Today · doi:10.1016/j.mattod.2022.05.020 · Cited by 52 · see citing works
Self-citationYu, C., Chen, W., Chiang, Y.-H., Guo, K., Martín‐Moldes, Z., Kaplan, D. L., & Buehler, M. J. (2022) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.1c01343 · Cited by 51 · see citing works
Self-citationLuu, R. K., Wysokowski, M., & Buehler, M. J. (2023) · Applied Physics Letters · doi:10.1063/5.0155890 · Cited by 51 · see citing works
Self-citationReoccurring Patterns in Hierarchical Protein Materials and Music: The Power of Analogies
Giesa, T., Spivak, D. I., Buehler, M. J., Media, S. S., Giesa, T., Spivak, D. I., & Buehler, M. J. (2011) · Cited by 50 · see citing works
Self-citationBuehler, E. L., & Buehler, M. J. (2022) · Biomedical Engineering Advances · doi:10.1016/j.bea.2022.100038 · Cited by 49 · see citing works
Self-citationMaterials by design: Merging proteins and music
Wong, J. Y., McDonald, J., Taylor-Pinney, M., Spivak, D. I., Kaplan, D. L., & Buehler, M. J. (2012) · Nano Today · doi:10.1016/j.nantod.2012.09.001 · Cited by 45 · see citing works
Self-citationLu, W., Lee, N., & Buehler, M. J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305273120 · Cited by 45 · see citing works
Self-citationReoccurring Patterns in Hierarchical Protein Materials and Music: The Power of Analogies
Giesa, T., Spivak, D. I., & Buehler, M. J. (2011) · BioNanoScience · doi:10.1007/s12668-011-0022-5 · Cited by 44 · see citing works
Self-citationSelf-Folding and Unfolding of Carbon Nanotubes
Buehler, M. J., Kong, Y., Gao, H., & Huang, Y. Y. (2005) · Journal of Engineering Materials and Technology · doi:10.1115/1.1857938 · Cited by 42 · see citing works
Self-citationCategory Theory Based Solution for the Building Block Replacement Problem in Materials Design
Giesa, T., Spivak, D. I., & Buehler, M. J. (2012) · Advanced Engineering Materials · doi:10.1002/adem.201200109 · Cited by 42 · see citing works
Self-citationChain-of-Verification Reduces Hallucination in Large Language Models
Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023) · arXiv · doi:10.48550/arxiv.2309.11495 · Cited by 42 · see citing works
ROBUSTNESS-STRENGTH PERFORMANCE OF HIERARCHICAL ALPHA-HELICAL PROTEIN FILAMENTS
Zhao Qin, A., Cranford, S. W., Ackbarow, T., & Buehler, M. J. (2009) · International Journal of Applied Mechanics · doi:10.1142/s1758825109000058 · Cited by 38 · see citing works
Self-citationKhare, E., González‐Obeso, C., Kaplan, D. L., & Buehler, M. J. (2022) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.2c00737 · Cited by 37 · see citing works
Self-citationColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences
Yu, C., Khare, E., Narayan, O. P., Parker, R. N., Kaplan, D. L., & Buehler, M. J. (2021) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2021.104921 · Cited by 36 · see citing works
Self-citationHierarchical multi-scale modelling of plasticity of submicron thin metal films
Buehler, M. J., Hartmaier, A., & Gao, H. (2004) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/12/4/s07 · Cited by 32 · see citing works
Self-citationPerspective: Large Language Models in Applied Mechanics
Brodnik, N. R., Carton, S., Muir, C., Ghosh, S., Downey, D., Echlin, M. P., Pollock, T. M., & Daly, S. H. (2023) · Journal of Applied Mechanics · doi:10.1115/1.4062773 · Cited by 31 · see citing works
Buehler, M. J. (2023) · Journal of Applied Physics · doi:10.1063/5.0157367 · Cited by 30 · see citing works
Self-citationHu, Y., & Buehler, M. J. (2022) · ACS Nano · doi:10.1021/acsnano.2c07681 · Cited by 28 · see citing works
Self-citationDrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule GraphsPreprint
Liang, Y., Zhang, R., Zhang, L., & Xie, P. (2023) · doi:10.36227/techrxiv.22945922 · Cited by 27 · see citing works
Inguva, P., Bhute, V. J., Cheng, T. N. H., & Walker, P. J. (2021) · Education for Chemical Engineers · doi:10.1016/j.ece.2021.01.011 · Cited by 22 · see citing works
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Giesa, T., Jagadeesan, R., Spivak, D. I., & Buehler, M. J. (2015) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.5b00251 · Cited by 18 · see citing works
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Luu, R. K., & Buehler, M. J. (2023) · Journal of Applied Mechanics · doi:10.1115/1.4062310 · Cited by 18 · see citing works
Self-citationPRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model
Liu, F. Y. C., Ni, B., & Buehler, M. J. (2022) · Extreme Mechanics Letters · doi:10.1016/j.eml.2022.101803 · Cited by 17 · see citing works
Self-citationBuehler, M. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100692 · Cited by 17 · see citing works
Self-citationScientific Language Models for Biomedical Knowledge Base Completion: An Empirical StudyPreprint
Nadkarni, R., Wadden, D., Beltagy, I., Smith, N. A., Hajishirzi, H., & Hope, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09700 · Cited by 17 · see citing works
Milazzo, M., Anderson, G. I., & Buehler, M. J. (2021) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ac338a · Cited by 16 · see citing works
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Kelly, C. M., & Gage, M. J. (2021) · Biomedicines · doi:10.3390/biomedicines9101395 · Cited by 15 · see citing works
Scientific Modeling and Simulations
de la Rubia, T. D., & Yip, S. (2008) · Lecture notes in computational science and engineering · doi:10.1007/978-1-4020-9741-6_1 · Cited by 14 · see citing works
Categorical prototyping: incorporating molecular mechanisms into 3D printing
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Self-citationBuehler, M. J., Dodson, J., van Duin, A. C. T., & Goddard, W. A. (2005) · MRS Proceedings · doi:10.1557/proc-0894-ll03-03 · Cited by 12 · see citing works
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Lu, W., Kaplan, D. L., & Buehler, M. J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.10170 · Cited by 2 · see citing works
Self-citationBuehler, M. J., & Gao, H. (n.d.) · Gruppo Italiano Frattura Digital Repository (Gruppo Italiano Frattura) · Cited by 1 · see citing works
Self-citationBuehler, M. J., & Gao, H. (2005) · MRS Proceedings · doi:10.1557/proc-0904-bb03-05
Self-citationOpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Flagged first, then by citations
Research works printed but not linked: 5
- Ge Y.; Hua W.; Mei K.; Ji J.; Tan J.; Xu S.; Li Z.; Zhang Y. OpenAGI: When LLM Meets Domain Experts, (2023). arXiv:2304.04370v5. arXiv.org e-Print archive. https://arxiv.org/abs/2304.04370v5 (accessed September 23, 2023). · arXiv
- OpenAI, GPT-4 Technical Report, (2023). arxiv:2303.08774v3. arXiv.org e-Print archive. https://arxiv.org/abs/2303.08774v3 (accessed September 23, 2023). · arXiv
- Cranford S. W.; Buehler M. J. Biomateriomics; Springer: Netherlands, 2012.
- van der Zant T.; Kouw M.; Schomaker L. Generative artificial intelligence. In Studies in Applied Philosophy, Epistemology and Rational Ethics; Springer: Berlin Heidelberg, 2013; Vol. 5, pp 107– 120. 10.1007/978-3-642-31674-6_8/COVER. · DOI
- Anderson P. M.; Hirth J. P.; Lothe J. Theory of Dislocations, Third ed.; Cambridge University Press, 2017; p 1543.
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Self-citation
- Authors
- Buehler, M. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Buehler, Markus J.
- ORCID
- Buehler, Markus J.
- Venue
- ACS Engineering Au
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-01-12
- Volume
- 4
- Issue
- 2
- Pages
- 241-277
- ISSN
- 2694-2488 (ISSN-L)
- PMC
- PMC11027160
- OpenAlex
- W4390824394
- Cited by
- 82
- References
- 73
- Citations by year
- 2026: 36 · 2025: 28 · 2024: 18
- FWCI
- 8.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ACS Engineering Au (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Army Research Office (W911NF1920098, W911NF2220213)
- Massachusetts Institute of Technology
- Office of Naval Research (N00014-19-1-2375, N00014-20-1-2189)
- National Institutes of Health
- MIT-IBM Watson AI Lab
- U.S. Department of Agriculture (2021-69012-35978)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, materials design, retrieval-augmented generation, multi-agent systems, fine-tuning, hallucination mitigation, code generation, program execution, force field development, density functional theory, nonlinear sampling, question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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