References

1.
Xie, Y. Dynamic documents with R and knitr. (Chapman; Hall/CRC, 2015).
2.
3.
Team, R. C. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).
4.
Wickham, H., François, R., Henry, L. & Müller, K. Dplyr: A grammar of data manipulation. (2020).
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Rudis, B., Lohmann, N., Bandyopadhyay, D. & Kettner, L. Ndjson: Wicked-fast streaming ’JSON’ (’ndjson’) reader. (2019).
15.
Urbanek, S. Png: Read and write PNG images. (2013).
16.
Henry, L. & Wickham, H. Purrr: Functional programming tools. (2020).
17.
18.
Wickham, H., Hester, J. & Francois, R. Readr: Read rectangular text data. (2018).
19.
Ushey, K., Allaire, J. & Tang, Y. Reticulate: Interface to ’python’. (2020).
20.
Couture-Beil, A. Rjson: JSON for r. (2018).
21.
Allaire, J. et al. Rmarkdown: Dynamic documents for r. (2020).
22.
23.
24.
25.
Müller, K. & Wickham, H. Tibble: Simple data frames. (2020).
26.
Wickham, H. & Henry, L. Tidyr: Tidy messy data. (2020).
27.
28.
29.
Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer-Verlag New York, 2016).
30.
Xie, Y. Dynamic documents with R and knitr. (Chapman; Hall/CRC, 2015).
31.
Xie, Y., Allaire, J. J. & Grolemund, G. R markdown: The definitive guide. (Chapman; Hall/CRC, 2018).
32.
Wickham, H. et al. Welcome to the tidyverse. Journal of Open Source Software 4, 1686 (2019).
33.
Becke, A. D. Perspective: Fifty years of density-functional theory in chemical physics. The Journal of Chemical Physics 140, 18A301 (2014).
34.
Lewis, N. S. Powering the planet. MRS Bulletin 32, 808–820 (2007).
35.
Fert, A. The origin, development and future of spintronics. Phys. Usp. 51, 1336 (2008).
36.
Fert, A. Nobel lecture: Origin, development, and future of spintronics. Rev. Mod. Phys. 80, 1517–1530 (2008).
37.
38.
Mahapatra, S. et al. Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking. medRxiv (2020) doi:10.1101/2020.04.05.20054254.
39.
Xie, Y. Knitr: A comprehensive tool for reproducible research in R. in Implementing reproducible computational research (eds. Stodden, V., Leisch, F. & Peng, R. D.) (Chapman; Hall/CRC, 2014).
40.
Blum, L. C. & Reymond, J.-L. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. Journal of the American Chemical Society 131, 8732–8733 (2009).
41.
Ruddigkeit, L., Deursen, R. van, Blum, L. C. & Reymond, J.-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. Journal of Chemical Information and Modeling 52, 2864–2875 (2012).
42.
Ruddigkeit, L., Blum, L. C. & Reymond, J.-L. Visualization and virtual screening of the chemical universe database GDB-17. Journal of Chemical Information and Modeling 53, 56–65 (2013).
43.
Redox-active organic electrodes for pseudocapacitor applications. ECS Meeting Abstracts (2017) doi:10.1149/ma2017-01/1/98.
44.
Organic redox active molecular engineer for non-aqueous redox flow battery. ECS Meeting Abstracts (2014) doi:10.1149/ma2014-03/2/7.
45.
Ionic liquids-promoted utilization of redox-active organic materials for flow batteries. ECS Meeting Abstracts (2019) doi:10.1149/ma2019-04/5/242.
46.
(Invited) redox-active organic electrode materials for safe energy storage. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-02/55/1969.
47.
Redox-active organic molecules for non-aqueous flow batteries. ECS Meeting Abstracts (2012) doi:10.1149/ma2012-02/5/410.
48.
(Keynote) redox-active organic species for rechargeable batteries, and beyond! ECS Meeting Abstracts (2018) doi:10.1149/ma2018-02/2/89.
49.
Organic active species for nonaqueous redox flow batteries. ECS Meeting Abstracts (2015) doi:10.1149/ma2015-03/3/685.
50.
51.
Phenothiazine-based redox polymers as cathode-active materials in li/organic batteries. ECS Meeting Abstracts (2019) doi:10.1149/ma2019-04/6/349.
52.
53.
Wang, X., Chai, J. & Jiang, J. “Jimmy”. Redox flow batteries based on insoluble redox-active materials. A review. Nano Materials Science 3, 17–24 (2021).
54.
Chen, R. Redox flow batteries for energy storage: Recent advances in using organic active materials. Current Opinion in Electrochemistry 21, 40–45 (2020).
55.
Wang, S., Li, F., Easley, A. D. & Lutkenhaus, J. L. Real-time insight into the doping mechanism of redox-active organic radical polymers. Nature Materials 18, 69–75 (2018).
56.
Highly stable active materials for nonaqueous redox flow batteries. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-02/1/68.
57.
(Invited) computational discovery of metal-organic frameworks for hydrogen storage: Combining high-throughput screening, machine learning, and experimental demonstration. ECS Meeting Abstracts (2019) doi:10.1149/ma2019-02/42/1995.
58.
Hybrid organic and inorganic redox active components for non-aqueous redox flow batteries. ECS Meeting Abstracts (2014) doi:10.1149/ma2014-01/4/396.
59.
Organic anolyte species for aqueous redox flow batteries. ECS Meeting Abstracts (2017) doi:10.1149/ma2017-02/1/16.
60.
Derivatives of naphthoquinone as potential electroactive species for redox FLOW batteries. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-02/2/124.
61.
Multi-redox molecule for high-energy redox flow batteries. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-02/5/375.
62.
Properties of redox couples for use in organic redox flow batteries. ECS Meeting Abstracts (2015) doi:10.1149/ma2015-01/3/704.
63.
(Invited) organic molecules for redox flow batteries. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-03/4/234.
64.
Developing new chemistries for redox flow batteries. ECS Meeting Abstracts (2019) doi:10.1149/ma2019-02/1/4.
65.
Flexible ceramic membranes for redox flow batteries. ECS Meeting Abstracts (2018) doi:10.1149/ma2018-01/41/2373.
66.
(Invited) high-energy-density redox-flow batteries: Fundamental redox processes and materials design strategies. ECS Meeting Abstracts (2019) doi:10.1149/ma2019-01/3/424.
67.
Pan, F. & Wang, Q. Redox species of redox flow batteries: A review. Molecules 20, 20499–20517 (2015).
68.
69.
Goodfellow, I. J. et al. Generative Adversarial Networks. http://arxiv.org/abs/1406.2661 (2014).
70.
71.
G’omez-Bombarelli, R. & Aspuru-Guzik, A. Machine Learning and Big-Data in Computational Chemistry. Handb. Mater. Model. 1–24 (2018) doi:gjprg7.
72.
G’omez-Bombarelli, R. et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 4, 268–276 (2018).