The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening
Abstract
Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. With increased interest in development of advanced electrical energy storage systems, it is essential to find new electrolytes that function effectively. This Perspective reviews various methods for screening electrolytes and then describes a hierarchical computational scheme to screen multiple properties of advanced electrical energy storage electrolytes using high-throughput quantum chemical calculations. The approach effectively down-selects a large pool of candidates based on successive property evaluation. As an example, results of screening are presented for redox potentials, solvation energies, and structural changes of ∼1400 organic molecules for nonaqueous redox flow batteries. Importantly, on the basis of high-throughput screening, in silico design of suitable candidate molecules for synthesis and electrochemical testing can be achieved. We anticipate that the computational approach described in this Perspective coupled with experimentation will have a significant role to play in the discovery of materials for future energy needs.
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Cites
Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767
Li-O2 and Li-S batteries with high energy storage
Bruce, P. G., Freunberger, S. A., Hardwick, L. J., & Tarascon, J.-M. (2012) · Nature Materials · doi:10.1038/nmat3191 · Cited by 9,479
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
Self-citationComputational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
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
The rechargeable revolution: a better battery
Van Noorden, R. (2014) · Nature · doi:10.1038/507026a · Cited by 1,047
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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
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
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
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56
Electrolytomics: A Unified Big Data Approach for Electrolyte Design and Discovery
Kumar, R., Vu, M. C., Ma, P., & Amanchukwu, C. V. (2025) · Chemistry of Materials · doi:10.1021/acs.chemmater.4c03196 · Cited by 45
Wang, X., Zeng, Q., Xu, D.-H., Zhang, L., Jiang, G., & Yang, M. (2026) · Journal of Materials Informatics · doi:10.20517/jmi.2025.69 · Cited by 2
Metadata
- Authors
- Cheng, L., Assary, R. S., Qu, X., Jain, A., Ong, S. P., Rajput, N. N., Persson, K., & Curtiss, L. A.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-01-06
- Volume
- 6
- Issue
- 2
- Pages
- 283-291
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2166969166
- Cited by
- 356
- References
- 6
- FWCI
- 13.27
- Type
- journal article
- Language
- en
- Open access
- closed
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