Adaptive AI decision interface for autonomous electronic material discovery
Gupta, K. M. & Gupta, N. Semiconductor Materials: their Properties, Applications, and Recent Advances. in Advanced Semiconducting Materials and Devices. 3–40 (Springer, 2015).
Fiori, G. et al. Electronics based on two-dimensional materials. Nat. Nanotechnol. 9, 768–779 (2014).
Google Scholar
Kim, M., Lim, H. & Ko, S. H. Liquid metal patterning and unique properties for next-generation soft electronics. Adv. Sci. 10, 2205795 (2023).
Google Scholar
Ostroverkhova, O. Organic optoelectronic materials: mechanisms and applications. Chem. Rev. 116, 13279–13412 (2016).
Google Scholar
Ding, L. et al. Polymer semiconductors: synthesis, processing, and applications. Chem. Rev. 123, 7421–7497 (2023).
Google Scholar
Gongora, A. E. et al. A Bayesian experimental autonomous researcher for mechanical design. Sci. Adv. 6, eaaz1708 (2020).
Google Scholar
Volk, A. A. et al. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nat. Commun. 14, 1403 (2023).
Google Scholar
Angello, N. H. et al. Closed-loop transfer enables artificial intelligence to yield chemical knowledge. Nature 633, 351–358 (2024).
Google Scholar
Avila, C. et al. Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation. Chem. Sci. 13, 12087–12099 (2022).
Google Scholar
Jiang, Y. et al. An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials. Sci. Adv. 8, eabo2626 (2022).
Google Scholar
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Google Scholar
Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 382, eadi1407 (2023).
Google Scholar
Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).
Google Scholar
Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).
Google Scholar
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).
Google Scholar
Wu, T. C. et al. A materials acceleration platform for organic laser discovery. Adv. Mater. 35, e2207070 (2023).
Google Scholar
Xu, P., Ji, X., Li, M. & Lu, W. Small data machine learning in materials science. npj Comput. Mater. 9, 42 (2023).
Google Scholar
Li, Z. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32, 5650–5663 (2020).
Google Scholar
MacLeod, B. P. et al. A self-driving laboratory advances the Pareto front for material properties. Nat. Commun. 13, 995 (2022).
Google Scholar
Du, X. et al. Elucidating the full potential of OPV materials utilizing a high-throughput robot-based platform and machine learning. Joule 5, 495–506 (2021).
Google Scholar
Tom, G. et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 124, 9633–9732 (2024).
Google Scholar
Zhu, L., Zhou, J. & Sun, Z. Materials data toward machine learning: advances and challenges. J. Phys. Chem. Lett. 13, 3965–3977 (2022).
Google Scholar
Achar, S. K. & Keith, J. A. Small data machine learning approaches in molecular and materials science. Chem. Rev. 124, 13571–13573 (2024).
Google Scholar
Taniguchi, H., Sato, H. & Shirakawa, T. A machine learning model with human cognitive biases capable of learning from small and biased datasets. Sci. Rep. 8, 7397 (2018).
Google Scholar
Scheurer, C. & Reuter, K. Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis. Nat. Catal. 8, 13–19 (2025).
Google Scholar
Snapp, K. L. & Brown, K. A. Driving school for self-driving labs. Digit. Discov. 2, 1620–1629 (2023).
Google Scholar
Adams, F., McDannald, A., Takeuchi, I. & Kusne, A. G. Human-in-the-loop for Bayesian autonomous materials phase mapping. Matter 7, 697–709 (2024).
Google Scholar
Kowald, D. et al. Establishing and evaluating trustworthy AI: overview and research challenges. Front. Big Data 7, 1467222 (2024).
Google Scholar
Díaz-Rodríguez, N. et al. Connecting the dots in trustworthy artificial intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf. Fusion 99, 101896 (2023).
Google Scholar
Vriza, A., Chan, H. & Xu, J. Self-driving laboratory for polymer electronics. Chem. Mater. 35, 3046–3056 (2023).
Google Scholar
Paulsen, B. D., Tybrandt, K., Stavrinidou, E. & Rivnay, J. Organic mixed ionic–electronic conductors. Nat. Mater. 19, 13–26 (2020).
Google Scholar
Wang, Y. et al. Designing organic mixed conductors for electrochemical transistor applications. Nat. Rev. Mater. 9, 249–265 (2024).
Google Scholar
Rivnay, J. et al. Organic electrochemical transistors. Nat. Rev. Mater. 3, 17086 (2018).
Google Scholar
Tropp, J., Meli, D. & Rivnay, J. Organic mixed conductors for electrochemical transistors. Matter 6, 3132–3164 (2023).
Google Scholar
LeCroy, G. et al. Role of aggregates and microstructure of mixed-ionic–electronic-conductors on charge transport in electrochemical transistors. Mater. Horiz. 10, 2568–2578 (2023).
Google Scholar
Tsarfati, Y. et al. The hierarchical structure of organic mixed ionic–electronic conductors and its evolution in water. Nat. Mater. 24, 101–108 (2025).
Google Scholar
Wu, R. et al. Bridging length scales in organic mixed ionic–electronic conductors through internal strain and mesoscale dynamics. Nat. Mater. 23, 648–655 (2024).
Google Scholar
Nielsen, C. B. et al. Molecular design of semiconducting polymers for high-performance organic electrochemical transistors. J. Am. Chem. Soc. 138, 10252–10259 (2016).
Google Scholar
Riera-Galindo, S., Tamayo, A. & Mas-Torrent, M. Role of polymorphism and thin-film morphology in organic semiconductors processed by solution shearing. ACS Omega 3, 2329–2339 (2018).
Google Scholar
Luo, C. et al. General strategy for self-assembly of highly oriented nanocrystalline semiconducting polymers with high mobility. Nano Lett. 14, 2764–2771 (2014).
Google Scholar
Tseng, H.-R. et al. High mobility field effect transistors based on macroscopically oriented regioregular copolymers. Nano Lett. 12, 6353–6357 (2012).
Google Scholar
Gu, X., Shaw, L., Gu, K., Toney, M. F. & Bao, Z. The meniscus-guided deposition of semiconducting polymers. Nat. Commun. 9, 534 (2018).
Google Scholar
Xu, Z. et al. Not all aggregates are made the same: distinct structures of solution aggregates drastically modulate assembly pathways, morphology and electronic properties of conjugated polymers. Adv. Mater. 34, 2203055 (2022).
Google Scholar
Liang, Q. et al. Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains. npj Comput. Mater. 7, 188 (2021).
Google Scholar
Wu, Y., Walsh, A. & Ganose, A. M. Race to the bottom: Bayesian optimisation for chemical problems. Digit. Discov. 3, 1086–1100 (2024).
Google Scholar
MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proc. 5th Berkeley Symp. Math. Stat. Probab. 1, 281–297 (Univ. California Press, 1967).
Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).
Google Scholar
Noriega, R. et al. A general relationship between disorder, aggregation and charge transport in conjugated polymers. Nat. Mater. 12, 1038–1044 (2013).
Google Scholar
Xu, J. et al. Multi-scale ordering in highly stretchable polymer semiconducting films. Nat. Mater. 18, 594–601 (2019).
Google Scholar
Paulsen, B. D., Fabiano, S. & Rivnay, J. Mixed ionic-electronic transport in polymers. Annu. Rev. Mater. Res. 51, 1–27 (2021).
Google Scholar
Mosca, S. et al. Raman fingerprints of π-electron delocalization in polythiophene-based insulated molecular wires. Macromolecules 55, 3458–3468 (2022).
Google Scholar
Luo, S. et al. Real-time correlation of crystallization and segmental order in conjugated polymers. Mater. Horiz. 11, 196–206 (2023).
Google Scholar
Huang, L. et al. Porous semiconducting polymers enable high-performance electrochemical transistors. Adv. Mater. 33, 2007041 (2021).
Google Scholar
Bernards, D. & Malliaras, G. Steady-state and transient behavior of organic electrochemical transistors. Adv. Funct. Mater. 17, 3538–3544 (2007).
Google Scholar
Ohayon, D., Druet, V. & Inal, S. A guide for the characterization of organic electrochemical transistors and channel materials. Chem. Soc. Rev. 52, 1001–1023 (2023).
Google Scholar
link
.jpg "IFW Dresden selects Agnitron Agilis 100 MOCVD platform for precursor chemistry and ultra-wide-bandgap materials development")