Abstract
Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions1,2, but the substantial potential of this traditionally black-box approach to uncovering new chemical knowledge has remained largely untapped. Here we report the integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), to yield chemical insights in parallel with optimization of objective functions. CLT was used to examine the factors dictating the photostability in solution of light-harvesting donor–acceptor molecules used in a variety of organic electronics applications, and showed fundamental insights including the importance of high-energy regions of the triplet state manifold. This was possible following automated modular synthesis and experimental characterization of only around 1.5% of the theoretical chemical space. This physics-informed model for photostability was strengthened using multiple experimental test sets and validated by tuning the triplet excited-state energy of the solvent to break out of the observed plateau in the closed-loop photostability optimization process. Further applications of CLT to additional materials systems support the generalizability of this strategy for augmenting closed-loop strategies. Broadly, these findings show that combining interpretable supervised learning models and physics-based features with closed-loop discovery processes can rapidly provide fundamental chemical insights.
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Data availability
The data that support the findings of this study are available online (https://github.com/TheJacksonLab/ClosedLoopTransfer), including experimental photostability measurement data for all molecules synthesized in this work, DFT- and RDKit-derived molecular featurizations and predicted photostabilities across the full space of 2,200 molecules. Regression models and scripts used to train and perform all analysis with the associated data are also provided. Datasets are available at Zenodo (https://doi.org/10.5281/zenodo.11580889)49. Source Data are provided with this paper.
Code availability
All supervised learning model codes are available at GitHub (https://github.com/TheJacksonLab/ClosedLoopTransfer). Gryffin is available at GitHub (https://github.com/aspuru-guzik-group/gryffin). Codes are available at Zenodo (https://doi.org/10.5281/zenodo.11580889)49.
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Acknowledgements
This work was supported by the Molecule Maker Lab Institute, an AI Research Institutes programme supported by the US National Science Foundation under grant no. 2019897 (to N.E.J., Y.D., C.M.S. and M.D.B.). A.A.-G. and A.H.C. acknowledge support from the Canada 150 Research Chairs Program and the Acceleration Consortium at the University of Toronto, as well as the generous support of A. G. Frøseth. T.C.T.-F., Y.D. and C.M.S. acknowledge support by the IBM–Illinois Discovery Accelerator Institute. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the US National Science Foundation.
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N.H.A., D.M.F., C.H. and S.Y. contributed equally to this project. A.H.C. and T.C.T.-F. contributed equally to this project. The project was designed by N.H.A., D.M.F., C.H., S.Y., E.R.J., A.A.-G., M.D.B., C.M.S., Y.D. and N.E.J. Molecule synthesis was conducted by N.H.A., S.Y., T.C.T.-F., E.R.J. and W.W. Solution testing was conducted by C.H. BO and regression model training was conducted by D.M.F. and A.H.C. N.H.A., D.M.F., C.H., S.Y., A.A.-G., M.D.B., C.M.S., Y.D. and N.E.J. wrote the manuscript with contributions from all authors.
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The University of Illinois has filed patent applications related to MIDA and TIDA boronates with M.D.B., N.H.A. and W.W. as inventors.
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Extended data figures and tables
Extended Data Fig. 1 Chemical design space of acceptor moieties.
Chemical diversity down-selected set of acceptor blocks used in populating the design space for the D-B-A motif utilized in this work.
Extended Data Fig. 2 Digital project manager.
Visualization of Streamlit web app used in this work showcasing integration of the Gryffin Bayesian optimizer, building block fragment assembler, and suggestion window for similar molecules as synthetic backups.
Extended Data Fig. 3 The small molecule synthesizer used in this work.
a, Picture of the hardware. b, Design schematic.
Extended Data Fig. 4 Triplet Density of States (TDOS) for all experimentally measured molecules.
DB08_A096 (the high performer in the predicted Bottom 7) is shown in red. All others are in gray, with the highest T80 in darker colors, and the lowest T80 in lighter colors.
Extended Data Fig. 5 Results from CLT phase I.
a, All support vector regression leave-one-out validation (LOOV) results for predicting T80 of the 30 molecules characterized in Phase I from 2-feature combinations. b, Comparison of the prediction strength of all possible 4-feature models containing either the TDOS at 4.0 eV or the T1 energy. Compare to Fig. 5b & c.
Extended Data Fig. 6 Physics-driven discovery in phase III.
a, The distribution of photostabilities of the 44 molecules synthesized through Phase II of the CLT campaign. b, The best 4 feature model for predicting T80 from Phase II. Note the similarities of the TDOS features to those in the original physics based T80 model c, The relative photostability of 3 molecules in CB, toluene, and decane, showing the improved photostability for all molecules in the absence of Dexter energy transfer, and that the improvement correlates with the TDOS at 4.0 eV. d, The impact of adding cyclooctatetraene (COT) triplet quencher to the CB solution (red) and chemically attaching it to DB_11_A_002 (blue). The structure shown is DB_11_A_002 (the highest performing molecule in Fig. 4a, the control), with the dodecyl side chain replaced with a hexyl-COT side chain. Results support the Dexter energy transfer hypothesis as explained in SI Section 6.
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Angello, N.H., Friday, D.M., Hwang, C. et al. Closed-loop transfer enables artificial intelligence to yield chemical knowledge. Nature 633, 351–358 (2024). https://doi.org/10.1038/s41586-024-07892-1
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DOI: https://doi.org/10.1038/s41586-024-07892-1
