Networking autonomous material exploration systems through transfer learning
Source abstract
Abstract
Abstract Autonomous material exploration systems that integrate robotics, material simulations, and machine learning have advanced rapidly in recent years. Although their number continues to grow, these systems currently operate in isolation, limiting the overall efficiency of autonomous material discovery. In analogy to how human researchers advance materials science by sharing knowledge and collaborating, autonomous systems can also benefit from networking and knowledge exchange. Here, we propose a framework in which multiple autonomous material exploration systems form a network via transfer learning, selectively utilizing relevant knowledge from other systems in real time. We demonstrate this approach using three distinct autonomous systems and show that such networking significantly enhances the efficiency of material discovery. Our results suggest that the proposed framework can enable the development of large-scale autonomous material exploration networks, ultimately accelerating progress in material development.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Neural Network Applications · Modular Robots and Swarm Intelligence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).10.1038/s41524-017-0056-5 ↗
2. Volk, A. A. & Abolhasani, M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nat. Commun. 15, 1378 (2024).10.1038/s41467-024-45569-5 ↗
3. Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).10.1038/s41586-023-06734-w ↗
4. Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).10.1038/s41586-020-2442-2 ↗
5. Wang, C. et al. Autonomous platform for solution processing of electronic polymers. Nat. Commun. 16, 1498 (2025).10.1038/s41467-024-55655-3 ↗
6. Pyzer-Knapp, E. O. et al. Accelerating materials discovery using artificial intelligence, high-performance computing and robotics. npj Comput. Mater. 8, 84 (2022).10.1038/s41524-022-00765-z ↗
7. Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, 6453 (2019).10.1126/science.aax1566 ↗
8. Nikolaev, P. et al. Autonomy in materials research: a case study in carbon nanotube growth. npj Comput Mater. 2, 16031 (2016).10.1038/npjcompumats.2016.31 ↗
9. Shimizu, R., Kobayashi, S., Watanabe, Y., Ando, Y. & Hitosugi, T. Autonomous materials synthesis by machine learning and robotics. APL Mater. 8, 111110 (2020).10.1063/5.0020370 ↗
10. Zhi, L. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32, 5650–5663 (2020).10.1021/acs.chemmater.0c01153 ↗
11. Roch, A. M. et al. ChemOS: orchestrating autonomous experimentation. Sci. Robot 3, 19 (2018).10.1126/scirobotics.aat5559 ↗
12. Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402 (2020).10.1038/s41586-020-1994-5 ↗
13. Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).10.1038/s41586-018-0307-8 ↗
14. Szymanski, N. J. et al. Toward autonomous design and synthesis of novel inorganic materials. Mater. Horiz. 8, 2169–2198 (2021).10.1039/D1MH00495F ↗
15. Ren, Z., Ren, Z., Zhang, Z., Buonassisi, T. & Li, J. Autonomous experiments using active learning and AI. Nat. Rev. Mater. 8, 563–564 (2023).10.1038/s41578-023-00588-4 ↗
16. Tamuda, R., Tsuda, K. & Matsuda, S. NIMS-OS: an automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science. Sci. Technol. Adv. Mater. 3, 2232297 (2023).
17. Ament, S. et al. Autonomous materials synthesis via hierarchical active learning of interfacial energies. Sci. Adv 7, eabg4930 (2021).10.1126/sciadv.abg4930 ↗
18. Iwasaki, Y., Sawada, R., Saitoh, E. & Ishida, M. Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit. Commun Mater 2, 1–7 (2021).10.1038/s43246-021-00135-0 ↗
19. Cubuk, E. D. et al. Accelerated discovery of stable materials with deep learning. Nature 624, 80–085 (2023).10.1038/s41586-023-06735-9 ↗
20. Iwasaki, Y. et al. Autonomous search for half-metallic materials with B 2 structure. Sci. Technol. Adv. Mater. 4, 2403966 (2024).
21. Jalem, R. et al. Bayesian-driven first-principles calculations for accelerating exploration of fast ion conductors for rechargeable battery application. Sci. Rep. 8, 5845 (2018).10.1038/s41598-018-23852-y ↗
22. Furuya, D., Miyashita, T., Miura, Y., Iwasaki, Y. & Kotsugi, M. Autonomous synthesis system integrating theoretical, informatics, and experimental approaches for large-magnetic-anisotropy materials. Sci. Technol. Adv. Mater: Methods 2, 280–293 (2022).
23. Iwasaki, Y., Jaekyun, H., Sakuraba, Y., Kotsugi, M. & Igarashi, Y. Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization. Sci. Technol. Adv. Mater. 2, 365–371 (2022).
24. Ma, X. Y. et al. High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials. Nanoscale 13, 14694–14704 (2021).10.1039/D1NR03886A ↗
25. Iwasaki, Y., Ogawa, D., Kotsugi, M. & Takahashi, Y. K. Autonomous materials search using machine learning and ab initio calculations for L1 0 -FePt-based quaternary alloys. Sci. Technol. Adv. Mater. 5, 2470114 (2025).
26. Gubaev, K., Podryabinkin, E. V., Hart, G. L. W. & Shapeev, A. V. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Comput. Mater. Sci. 156, 148–156 (2019).10.1016/j.commatsci.2018.09.031 ↗
27. Farache, D. E., Verduzco, J. C., McClure, Z. D., Desai, S. & Strachan, A. Active learning and molecular dynamics simulations to find high melting temperature alloys. Comput. Mater. Sci. 209, 111386 (2021).10.1016/j.commatsci.2022.111386 ↗
28. Iwasaki, Y. Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning. Sci. Technol. Adv. Mater. 4, 2399494 (2024).
29. Gupta, V. et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat. Commun. 12, 6595 (2021).10.1038/s41467-021-26921-5 ↗
30. Chang, R., Wang, Y. & Ertekin, E. Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework. npj Comput. Mater. 8, 242 (2022).10.1038/s41524-022-00929-x ↗
31. Minami, S. et al. Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions. npj Comput. Mater. 11, 146 (2025).10.1038/s41524-025-01606-5 ↗
32. Li, X. et al. A transfer learning approach for microstructure reconstruction and structure-property predictions. Sci. Rep. 8, 13461 (2018).10.1038/s41598-018-31571-7 ↗
33. Yamada, H. et al. Predicting materials properties with little data using shotgun transfer learning. ACS Cent. Sci. 5, 1717–1730 (2019).10.1021/acscentsci.9b00804 ↗
34. Hwang, J. & Iwasaki, Y. Improving efficiency of autonomous material search via transfer learning from nontarget properties. Sci. Technol. Adv. Mater. 3, 2254202 (2023).
35. Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst 30, 6405–6416 (2017).
36. Yang, Y., Lv, H. & Chen, N. A survey on ensemble learning under the era of deep learning. Artif. Intell. Rev. 56, 5545–5589 (2022).10.1007/s10462-022-10283-5 ↗
37. Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, 2006).10.7551/mitpress/3206.001.0001 ↗
38. Hutter, F., Hoos, H. H. & Leyton-Brown, K. Sequential model-based optimization for general algorithm configuration. In Proc. International Conference on Learning and Intelligent Optimization, 507–523 (Springer, 2011).10.1007/978-3-642-25566-3_40 ↗
39. Bergstra, J., Bardenet, R., Bengio, Y. & Kégl, B. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems, Vol. 24 (Curran Associates, Inc., 2011).
40. Auer, P. Using confidence bounds for exploitation-exploration trade-off. J. Mach. Learn Res 3, 397–422 (2022).
41. Iwasaki, Y. Dataset of B2-structured ternary alloys for magnetic moment, Curie temperature, and spin polarization calculated by KKR-CPA (AkaiKKR). Mater. Data Repository Natl. Institute Mater. Sci. https://doi.org/10.48505/nims.5364 (2025).10.48505/nims.5364 ↗
42. López-Domínguez, V. et al. A simple vibrating sample magnetometer for macroscopic samples. Rev. Sci. Instrum. 89, 034707 (2018).10.1063/1.5017708 ↗
43. Paulsen, M. et al. An ultra‑low field SQUID magnetometer for measuring antiferromagnetic and weakly remanent magnetic materials at low temperatures. Rev. Sci. Instrum. 94, 103904 (2023).10.1063/5.0135877 ↗
44. Fabian, K., Shcherbakov, V. P. & McEnroe, S. A. Measuring the Curie temperature. Geochem. Geophys. Geosyst. 14, 947–961 (2013).10.1029/2012GC004440 ↗
45. Soulen, R. J. et al. Andreev reflection: a new means to determine the spin polarization of ferromagnetic materials. J. Appl. Phys. 85, 4589–4591 (1999).10.1063/1.370417 ↗
46. Idzuchi, H., Fukuma, Y., Wang, L. & Otani, Y. Spin diffusion characteristics in magnesium nanowires. Appl. Phys. Express 3, 063002 (2010).10.1143/APEX.3.063002 ↗
47. Yaji, K. et al. High-resolution three-dimensional spin- and angle-resolved photoelectron spectrometer using vacuum ultraviolet laser light. Rev. Instrum. 87, 053111 (2016).10.1063/1.4948738 ↗
48. Liang, Q. et al. Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains. npj Comput. Mater. 7, 188 (2021).10.1038/s41524-021-00656-9 ↗
49. Ngyyen T. D. et al. Stable Bayesian Optimization. In Proc. Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23–26, 2017, Proceedings, Part II 10235, 101–113 (Springer, 2017).
50. Klambauer, G., Unterthiner, T., Mayr, A. & Hochreiter, S. Self-normalizing neural networks. Adv. Neural. Inf. Process. Syst. 30, 972–981 (2017).
51. LeCun, Y., Bottou, L., Orr, G. B.–Müller, K. R. Efficient backprop. In: Montavon G., Orr G. B., Müller K.-R. Neural networks: Tricks of the trade 9–50 (Springer, 2002)10.1007/3-540-49430-8_2 ↗