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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
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					<year>2024</year>
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					<volume>3</volume>
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				<issue>1</issue>
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					<title>Multi-Modal Transformer Architectures for Correlating Processing Conditions to Functional Performance in Perovskite Solar Cells</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Ravi</given_name>
            <surname>Kumar</surname>
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            <given_name>Neha</given_name>
            <surname>Sharma</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Aniket</given_name>
            <surname>Deshmukh</surname>
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								<publication_date>
					<year>2024</year>
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					  <unstructured_citation>Zhou Z, Zheng X, Liu M, Liu P, Han S, Chen Y. Interface engineering for high-performance perovskite solar cells. Adv Mater. 2022;34(5):2105678.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-df5d52eb-ce80-4fb4-8310-c4fe91cdbed2">
					  <unstructured_citation>Aranda Alonso CP, Saliba M. Pathways toward 30% efficient single-junction perovskite solar cells and the role of mobile ions. Adv Energy Mater. 2021;11(21):2100428.</unstructured_citation>
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          					<citation key="rk-10.68159/r181465594-2a2eb9bd-cbc3-4a1f-a6c7-bd8705a344c1">
					  <unstructured_citation>Betancur PF, Osorio-Romero J, Barea A, Aranda C, Saliba M. Perovskite solar cells: An integrated hybrid lifecycle assessment and review in comparison with other photovoltaic technologies. Renew Sustain Energy Rev. 2020;119:109602.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-7a1d58f3-56ad-4ff3-920e-7c9b2248b29a">
					  <unstructured_citation>Zhang M, Lu M, Zhu H, Zhang Q, Tian Y. Hydrophobic carbon quantum dots passivation of perovskite solar cells with enhanced moisture stability. Adv Mater. 2020;32(43):2002801.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-84852781-1674-4fc1-b4d6-96bd64333e36">
					  <unstructured_citation>Wang H, Gao X, Zhang D, Ma H, Jiang X, Wu G, et al. A sequential deposition strategy enables efficient CsPbBr3 perovskite light-emitting diodes. Adv Funct Mater. 2020;30(5):1908108.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-00efa2a0-42c5-46af-821e-56f3fd9b0173">
					  <unstructured_citation>Basumatary P, Agarwal P. A short review on preparation of perovskite thin film solar cell. Mater Today Proc. 2020;32:474-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-e2b05cce-9b99-453b-866b-e02af7c0cc71">
					  <unstructured_citation>Sajid S, Elseman AM, Huang H, Ji J, Dou S, Jiang H, et al. Breakthroughs in NiOx-HTMs towards stable, low-cost and efficient perovskite solar cells. Nano Energy. 2021;80:105566.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-8e1f62bb-6cb3-46dd-80a0-e10f95c18f1b">
					  <unstructured_citation>Arunagiri L, Peng Y, Han X, Li X, Peng X, Li Z, et al. Low-temperature processed, stable n-i-p perovskite solar cells with high performance via interface engineering. Sol RRL. 2020;4(9):2000292.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-0656f81c-500a-44ec-bbd0-37e9645c3fe7">
					  <unstructured_citation>AlSabeh G, Albar A, Kotsovos K, Ghaddar A, Ghafari R, Wark M, et al. Influence of various ammonium salts on the yield and performance of perovskite solar cells. Sol Energy. 2020;207:896-902.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-32fe2248-feea-4b14-b96b-04615573da78">
					  <unstructured_citation>Boro B, Porwal S, Kumar D, Mishra S, Ghosh S, Kansal S, et al. Perovskite solar cells: Assessment of the materials, efficiency, and stability. Catal Res. 2022;2(4):033.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-d9aead58-fdc8-4c6f-92a9-80521cfba3ca">
					  <unstructured_citation>Roy P, Raoui Y, Khare A. Design and simulation of efficient tin based perovskite solar cells through optimization of selective layers: Theoretical insights. Opt Mater. 2022;125:112057.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-6704d615-8804-451c-a530-ed71904132cb">
					  <unstructured_citation>Kim DI, Lee JW, Jeong RH, Yang JH, Park S, Boo JH. A high-efficiency and stable perovskite solar cell fabricated in ambient air using a polyaniline passivation layer. Sci Rep. 2022;12:697.</unstructured_citation>
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          					<citation key="rk-10.68159/r181465594-ef9b8040-38f0-4a0d-878c-433d292cbc3a">
					  <unstructured_citation>Wang K, Liu J, Yin J, Aydin E, Harrison GT, Liu MC, et al. Defect passivation in perovskite solar cells by cyano-based π-conjugated molecules for improved performance and stability. Adv Funct Mater. 2020;30(35):2002861.</unstructured_citation>
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          					<citation key="rk-10.68159/r181465594-0e729b74-bd49-48b0-8531-9806fff51407">
					  <unstructured_citation>Zhao X, Liu T, Burlingame QC, Liu T, Holley R III, Cheng G, et al. Accelerated aging of all-inorganic, interface-stabilized perovskite solar cells. Science. 2022;377(6603):307-10.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-18acc0f8-007f-4914-a442-7d7c438b7c37">
					  <unstructured_citation>Azam M, Khan A, Liang GX, Li GJ, Chen S, Zheng ZH, et al. Examining the interfacial defect passivation with chlorinated organic salt for highly efficient and stable perovskite solar cells. Sol RRL. 2020;4(12):2000358.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-68902258-c9fe-457e-af5c-1a4bf4a9f53b">
					  <unstructured_citation>Liu Z, Rolston N, Flick AC, Colburn TW, Ren Z, Dauskardt RH, et al. Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing. Joule. 2022;6(4):834-49.</unstructured_citation>
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          					<citation key="rk-10.68159/r181465594-e9825502-8c9f-4796-a237-301be9a377aa">
					  <unstructured_citation>Zhi J, Li X, Zhang W, Huang T, Pei Q. 2D perovskites with short interlayer distance for high-performance 2D perovskite solar cells. Adv Funct Mater. 2020;30(36):2002620.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-8f80406a-f591-4cb6-8132-d95b45016df6">
					  <unstructured_citation>Jacobsson TJ, Hultqvist A, García-Fernández A, Anand A, Al-Ashouri A, Hagfeldt A, et al. An open-access database and analysis tool for perovskite solar cell research. Nat Energy. 2022;7(1):107-15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-27d32313-cae7-4571-b4dc-c10f45bdc881">
					  <unstructured_citation>Liu Y, Yan W, Han S, Zhang Y, Xu Z, Wang Z, et al. How machine learning predicts and explains the performance of perovskite solar cells. Sol RRL. 2022;6(2):2100617.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-c2b96a90-3bc1-44df-bde9-e27be959ab5b">
					  <unstructured_citation>Fu N, Hu Y, Shi Y, Kornienko N, Xia Y, Peacock A, et al. Mn-enabled radical-based radical cyclization amid migration for synthesis of indoles. ACS Catal. 2021;11(11):6680-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-a94c6aa5-37de-4b0a-92f2-7bb2d498990b">
					  <unstructured_citation>Rath GS, Priyanga N, Nagappan G, Sudha Priyanga G, Thomas T. Discovery of direct band gap perovskites for light harvesting by using machine learning. Comput Mater Sci. 2022;210:111476.</unstructured_citation>
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          					<citation key="rk-10.68159/r181465594-fddee661-050c-4aaf-8e44-e5ea6c5e4cea">
					  <unstructured_citation>Mahapatra A, Prochowicz D, Tavakoli MM, Trivedi S, Kumar P, Yadav P. A review on stability, electrical and optical characteristics of perovskite solar cells. J Mater Chem A. 2020;8(1):27-54.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-4c64a7c3-8bda-4131-af91-4dd727023372">
					  <unstructured_citation>Ma S, Yuan G, Zhang Y, Yang N, Li Y, Chen Q. Development of encapsulation strategies towards the commercialization of perovskite solar cells. Energy Environ Sci. 2022;15(1):13-55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-322ad89a-7bbf-410a-874b-8b118db740e3">
					  <unstructured_citation>Gao C, Chen C, Ran C, Zheng J, Dong H, Cai W, et al. A-site cation engineering of metal halide perovskites: Version 3.0 of efficient tin-based lead-free perovskite solar cells. Adv Funct Mater. 2020;30(34):2000794.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-493bdf95-e71d-4d70-9192-4d8ef7d6cf0c">
					  <unstructured_citation>Wang P, Xiang H, Ren A, Lan Z, Li Z, Li C, et al. MXene-modulated electrode/SnO2 interface boosting charge transport in perovskite solar cells. ACS Appl Mater Interfaces. 2020;12(48):53973-83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-f700324c-6375-4c21-ba89-1e962aaeda9e">
					  <unstructured_citation>Lee JW, Tan S, Seok SI, Yang Y, Park NG. Rethinking the A cation in halide perovskites. Science. 2022;375(6583):eabj1186.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-6496d565-5bf6-4d1d-a626-f8114056e5f5">
					  <unstructured_citation>Wei L, Xu J, Ren S, Kristensen B, Li M, Shen Z, et al. Crystal composition transformer: Self-learning neural language model for generative and tinkering design of materials. Adv Mater. 2022;34(17):2107995.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-007a58f8-ea76-4e4b-b6fd-bd319e26444e">
					  <unstructured_citation>Choudhary K. AtomGPT: Atomistic generative pretrained transformer for forward and inverse materials design. npj Comput Mater. 2022;8:242.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-532d4642-a2c2-4cd2-acd5-392e18851673">
					  <unstructured_citation>Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanoscale Adv. 2020;2:3115-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-16d7231a-5407-422f-adc1-87fca0e8f0b6">
					  <unstructured_citation>Kirman J, Johnston A, Kuntz DA, Askerka M, Gao Y, Todorović P, et al. Machine-learning-accelerated perovskite crystallization. Matter. 2020;2(4):938-47.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-57b556ec-8f0a-4562-b2b7-835321dfe93b">
					  <unstructured_citation>Pilania G, Balachandran PV, Kim C, Lookman T. Finding new perovskite halides via machine learning. Front Mater. 2016;3:19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-5ba71858-36ef-4f0b-a146-8aecd4eb9e15">
					  <unstructured_citation>Park H, Mall R, Alharbi FH, Sanvito S, Tabet N, Bensmail H, et al. Exploring new perovskites via the A-site cation: A case study on CsPbI3. J Phys Chem A. 2019;123(34):7323-34.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-d10dae6f-455a-42f2-b9b8-58b43b16db97">
					  <unstructured_citation>Nguyen T, Shimizu G, Rajendran A. Post-combustion CO2 capture by vacuum swing adsorption using a hydrophobic metal-organic framework (MOF), CALF-20: Multi-objective optimization and experimental validation. ChemRxiv. 2022.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r181465594-edd44cd6-6908-496a-8793-492817af3bd4">
					  <unstructured_citation>Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inform Process Syst. 2020;33:6840-51.</unstructured_citation>
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