The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Progress and prospects for accelerating materials science with automated and autonomous workflows
Open accessAbstract
Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.
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
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.002 · Cited by 1,148
Biofabrication strategies for 3D in vitro models and regenerative medicine
Moroni, L., Burdick, J. A., Highley, C., Lee, S. J., Morimoto, Y., Takeuchi, S., & Yoo, J. J. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0006-y · Cited by 753
King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L. N., et al. (2009) · Science · doi:10.1126/science.1165620 · Cited by 679
Next-Generation Experimentation with Self-Driving Laboratories
Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019) · Trends in Chemistry · doi:10.1016/j.trechm.2019.02.007 · Cited by 426
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379
Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
Jia, X., Lynch, A., Huang, Y., Danielson, M., Lang’at, I., Milder, A., Ruby, A. E., Wang, H., Friedler, S. A., Norquist, A. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1540-5 · Cited by 222
Cited by
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-citationGenerative Models as an Emerging Paradigm in the Chemical Sciences
Anstine, D. M., & Isayev, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c13467 · Cited by 313
Nobel Turing Challenge: creating the engine for scientific discovery
Kitano, H. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00189-3 · Cited by 149
The future of self-driving laboratories: from human in the loop interactive AI to gamification
Hysmith, H., Foadian, E., Padhy, S. P., Kalinin, S. V., Moore, R. G., Ovchinnikova, O. S., & Ahmadi, M. (2024) · Digital Discovery · doi:10.1039/d4dd00040d · Cited by 103
Bayesian optimization with known experimental and design constraints for chemistry applications
Hickman, R. J., Aldeghi, M., Häse, F., & Aspuru-Guzik, A. (2022) · Digital Discovery · doi:10.1039/d2dd00028h · Cited by 98
Metadata
- Authors
- Stein, H. S., & Gregoire, J. M.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 10
- Issue
- 42
- Pages
- 9640-9649
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- OpenAlex
- W2974675506
- Cited by
- 228
- References
- 12
- FWCI
- 6.95
- Type
- journal article
- Language
- en
- Open access
- diamond
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: Progress and prospects for accelerating materials science with automated and autonomous workflows. https://app.cochat.ai/papers/10.1039/c9sc03766g
- The paper
- Stein, H. S., & Gregoire, J. M. (2019). Progress and prospects for accelerating materials science with automated and autonomous workflows. Chemical Science. https://doi.org/10.1039/c9sc03766g
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.
