The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot
Abstract
The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, an Artificial Chemist is presented: the integration of machine‐learning‐based experiment selection and high‐efficiency autonomous flow chemistry. With the self‐driving Artificial Chemist, made‐to‐measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. Utilizing the Artificial Chemist, eleven precision‐tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, the Artificial Chemist is pre‐trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. The knowledge‐transfer strategy further enhances the optoelectronic properties of the in‐flow synthesized QDs (within the same resources as the no‐prior‐knowledge experiments) and mitigates the issues of batch‐to‐batch precursor variability, resulting in QDs averaging within 1 meV from their target peak emission energy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Protesescu, L., Yakunin, S., Bodnarchuk, M. I., Krieg, F., Caputo, R., Hendon, C. H., Yang, R. X., Walsh, A., & Kovalenko, M. V. (2015) · Nano Letters · doi:10.1021/nl5048779 · Cited by 9,209
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397
Nedelcu, G., Protesescu, L., Yakunin, S., Bodnarchuk, M. I., Grotevent, M. J., & Kovalenko, M. V. (2015) · Nano Letters · doi:10.1021/acs.nanolett.5b02404 · Cited by 2,356
Genesis, challenges and opportunities for colloidal lead halide perovskite nanocrystals
Akkerman, Q. A., Rainò, G., Kovalenko, M. V., & Manna, L. (2018) · Nature Materials · doi:10.1038/s41563-018-0018-4 · Cited by 2,287
The past, present and potential for microfluidic reactor technology in chemical synthesis
Elvira, K. S., i Solvas, X. C., Wootton, R. C. R., & deMello, A. J. (2013) · Nature Chemistry · doi:10.1038/nchem.1753 · Cited by 1,203
Hansen, N., & Ostermeier, A. (2002) · Proceedings of IEEE International Conference on Evolutionary Computation · doi:10.1109/icec.1996.542381 · Cited by 1,106
A robotic platform for flow synthesis of organic compounds informed by AI planning
Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V., Hart, T., Fishman, J. S., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,052
Controlling an organic synthesis robot with machine learning to search for new reactivity
Granda, J. M., Donina, L., Dragone, V., Long, D.-L., & Cronin, L. (2018) · Nature · doi:10.1038/s41586-018-0307-8 · Cited by 740
A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679
Colloidal quantum dot solar cells
Sargent, E. H. (2012) · Nature Photonics · doi:10.1038/nphoton.2012.33 · Cited by 650
Cited by
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Machine learning for a sustainable energy future
Yao, Z., Lum, Y., Johnston, A., Mejia-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru-Guzik, A., Sargent, E. H., & Seh, Z. W. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00490-5 · Cited by 517
Autonomous experimentation systems for materials development: A community perspective
Stach, E., DeCost, B., Kusne, A. G., Hattrick-Simpers, J., Brown, K. A., Reyes, K. G., Schrier, J., Billinge, S., Buonassisi, T., Foster, I., et al. (2021) · Matter · doi:10.1016/j.matt.2021.06.036 · Cited by 398
Self-citationNandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302
Metadata
- Authors
- Epps, R. W., Bowen, M. S., Volk, A. A., Abdel‐Latif, K., Han, S., Reyes, K. G., Amassian, A., & Abolhasani, M.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2020-06-04
- Volume
- 32
- Issue
- 30
- Pages
- e2001626-e2001626
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3031157367
- Cited by
- 323
- References
- 40
- FWCI
- 17.90
- Type
- journal article
- Language
- en
- Open access
- closed
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. https://app.cochat.ai/papers/10.1002/adma.202001626
- The paper
- Epps, R. W., Bowen, M. S., Volk, A. A., Abdel‐Latif, K., Han, S., Reyes, K. G., Amassian, A., & Abolhasani, M. (2020). Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. Advanced Materials. https://doi.org/10.1002/adma.202001626
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
