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Machine learning empowers efficient design of ternary organic solar cells with PM6 donor

Journal of Energy Chemistry 2025年第1期100卷 337-347页

作者： kiran a.nirmal Tukaram D.Dongale Santosh S.Sutar Atul C.Khot Tae Geun Kim School of Electrical Engineering Korea UniversityAnam-ro 145Seongbuk-guSeoulRepublic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India Yashwantrao Chavan School of Rural Development Shivaji UniversityKolhapur 416004India

Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical ***, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 ***, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关键词： Machine learning Ternary organic solarcells PM6 donor PCE

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Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑Generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： kiran a.nirmal Tukaram D.Dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae Geun Kim School of Electrical Engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(Rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

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2D Ti_(3)C_(2)T_(x)MXene-derived self-assembled 3D TiO_(2)nanoflowers for nonvolatile memory and synaptic learning applications

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Journal of Materials Science & Technology 2023年第19期150卷 1-10页

作者： Atul C.Khot Tukaram D.Dongale kiran a.nirmal Jayan K.Deepthi Santosh S.Sutar Tae Geun Kim School of Electrical Engineering Korea UniversityAnam-ro 145Seongbuk-guSeoul 02841Republic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India Rajagiri School of Engineering and Technology(Autonomous) Rajagiri ValleyKakkanadKochiKerala 682039India Yashwantrao Chavan School of Rural Development Shivaji UniversityKolhapur 416004India

Two-dimensional(2D)semiconducting materials and transition-metal oxides are promising materials for nonvolatile memory and brain-inspired neuromorphic computing ***,it remains chal-lenging to obtain high-quality stacked 2D films with low energy consumptions(or drive currents)be-cause of their high interfacial *** this study,we synthesized 2D Ti_(3)C_(2)T_(x)MXene-derived three-dimensional(3D)TiO_(2)nanoflowers(NFs)as a feasible resistive switching(RS)material with outstanding electronic properties and synaptic learning *** electrical and optical characteristics of the synthesized material were determined through density functional theory *** measure-ments of the Al/Ti_(3)C_(2)T_(x)-TiO_(2)NF/Pt memory device indicated the occurrence of forming-free switching phenomena with extremely low switching voltages(0.68-0.53 V),stable ON/OFF ratio(2.3×103),and retention greater than 105 *** Holt-Winters exponential smoothing technique was used for mod-eling and predicting the switching voltages of the RS *** mechanism underlying the reliable RS was confirmed by observing the dense conductive filaments through conductive atomic force ***,the 2D Ti_(3)C_(2)T_(x)MXene-derived 3D TiO_(2)NF-based RS device mimicked the po-tentiation/depression and spike-time-dependent plasticity of a biological ***,a convolutional neural network was implemented based on the observed synaptic weights of Al/Ti_(3)C_(2)T_(x)-TiO_(2)NF/Pt for image-edge detection.

关键词： MXene Ti_(3)C_(2)T_(x)-TiO_(2)nanoflowers Resistive switching Synaptic learning Density functional theory Time-series analysis

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