I was wondering if all the recent success of perovskite solar cells was accomplished purely experimentally or if there was some materials modeling aspect in it.
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3$\begingroup$ Related: link.springer.com/referenceworkentry/10.1007/… $\endgroup$– Tyberius ♦Commented Apr 29, 2020 at 3:49
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$\begingroup$ Experimental tag can possibly stay: This is certainly a question that belongs on our site (since it's literally about materials modeling) but it also has to do with experimental materials science. Many experimental groups do maybe 60% experiments and 40% calculations (nowadays the other way around due to COVID lockdown) so they're welcome on this site, and would find it useful to be able to track this tag. If in 2 years there's only 3 questions with this tag, then maybe we can remove it :) $\endgroup$– Nike Dattani - No Free TimeCommented Jun 26, 2020 at 22:08
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2 Answers
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If you are looking for theoretical prediction of candidate perovskite solar cell materials; I have come across several papers that couple high-throughput density functional theory calculations along with machine learning for new material prediction. I haven't seen any solar cell compound discovery theoretical work that eventually validated their predictions through experiments though. However, this is not a result of an extensive search, so concluding "materials modelling has not contributed to solar cell materials discovery" is not right.
- Lu, S., Zhou, Q., Ma, L., Guo, Y., Wang, J., Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning. Small Methods 2019, 3, 1900360. https://doi.org/10.1002/smtd.201900360
- Im, J., Lee, S., Ko, T. et al. Identifying Pb-free perovskites for solar cells by machine learning. npj Comput Mater 5, 37 (2019). https://doi.org/10.1038/s41524-019-0177-0
- Jacobs, R., Luo, G., Morgan, D., Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High‐Efficiency Solar Cells. Adv. Funct. Mater. 2019, 29, 1804354. https://doi.org/10.1002/adfm.201804354
- Choudhary et al. Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods Chem. Mater. 2019, 31, 15, 5900–5908 Publication Date:July 17, 2019 https://doi.org/10.1021/acs.chemmater.9b02166
I did find machine learning ferroelectric perovskite modelling paper that validated predictions through experiments.
answered Jun 13, 2020 at 11:29
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1$\begingroup$ Interesting references, but your comment "I haven't seen any solar cell compound discovery theoretical work that eventually validated their predictions through experiments though" indicates that the search is still on!!! $\endgroup$ Commented Jun 13, 2020 at 17:35
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1$\begingroup$ That's right. But please do note that I wrote this answer to present some of the interesting papers I had seen on this topic. This is not a result of an extensive search and therefore it's not right to conclude "there's no theoretical contribution for solar cell material synthesis". $\endgroup$ Commented Jun 13, 2020 at 18:44
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1$\begingroup$ It might be helpful if you could include "This is not a result of an extensive search" in your answer, so that this is made clear. $\endgroup$ Commented Jun 13, 2020 at 18:50
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To add to the previous answer: the new lead-free halide double perovskite $\ce{Cs_2InAgCl_6}$ was discovered by first-principles and then synthesized:
- G. Volonakis et al. $\ce{Cs_2InAgCl_6}$: A New Lead-Free Halide Double Perovskite with Direct Band Gap, J. Phys. Chem. Lett. 8, 772 (2017) https://pubs.acs.org/doi/10.1021/acs.jpclett.6b02682
I'm sure there are other such examples. In addition ab-initio calculations made many predictions on specific materials properties of existing perovskites before they were measured (more common than discovering new ones).
