BetaCitation Atlas

RESEARCH ARTICLE · The Journal of Physical Chemistry Letters · 2021

Machine Learning Roadmap for Perovskite Photovoltaics

Meghna Srivastava, John M. Howard, Tao Gong, Mariama Rebello Sousa Dias, Marina S. Leite

Read original paper ↗

See where this research leads.

85 direct citing records in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · Saved source query complete
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
85 direct citing records saved

Machine Learning Roadmap for Perovskite PhotovoltaicsOriginalMetal Oxide-Induced Instability and Its Mitigation in Halide Perovskite Solar Cells43Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble25A dataquake for solar cells2Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence62Prediction of dielectric constants of ABO 3 -type perovskites using machine learning and first-principles calculations34Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform20Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy26Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method9Al–Au Thin Films for Thermally Stable and Highly Sensitive Plasmonic Sensors12Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies21Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water11Materials Data toward Machine Learning: Advances and Challenges62Accelerate process optimization in perovskite solar cell manufacturing with machine learning16Recent Progress in Mixed A‐Site Cation Halide Perovskite Thin‐Films and Nanocrystals for Solar Cells and Light‐Emitting Diodes160The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning10Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance38A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data2Applications of machine learning in perovskite materials80Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives53The Spring of Processing Chemistry in Perovskite Solar Cells–Bayesian Optimization16Experimentally validated machine learning predictions of ultralow thermal conductivity for SnSe materials22Perovskite-Based Materials for Photovoltaic Applications: A Machine Learning Approach0Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Ytterbium-Doped Lead–Halide Perovskite Nanocrystals: Synthesis, Near-Infrared Emission, and Open-Source Machine Learning Model for Prediction of Optical Properties20Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells19Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy44High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap35Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Machine learning for perovskite solar cell design58Perspective on machine learning based investigated materials to realize Stable-Efficient-Toxin free (S-E-T) perovskite solar cells7First-principles and machine learning modeling on adsorption of atmospheric gases on two-dimensional Ruddlesden–Popper halide perovskite surface7The role of machine learning in perovskite solar cell research42Probing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learning21Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells40Machine learning for fast development of advanced energy materials23Emerging opportunities for hybrid perovskite solar cells using machine learning8Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces14Optoelectronic perovskite film characterization via machine vision7Assessing Performance and Limitations of a Device‐Level Machine Learning Approach for Perovskite Solar Cells with an Application to Hole Transport Materials2Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression27Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Bandgap Prediction of Hybrid Organic–Inorganic Perovskite Solar Cell Using Machine Learning15Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives10Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets11High-Throughput Multi-Scale Experiments to Probe the Stability of Metal Halide Perovskites11Physics-based extraction of material parameters from perovskite experiments via Bayesian optimization16Physiochemical machine learning models predict operational lifetimes of CH 3 NH 3 PbI 3 perovskite solar cells25Ionic liquid-assisted preparation of Ag–Zn–In–S quaternary quantum dot thin films and luminescence performance optimized by machine learning7High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications10Identifying Performance Limiting Parameters in Perovskite Solar Cells Using Machine Learning8Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells37Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells8Accelerating discovery of tunable optical materials (ATOM)3Machine learning guided efficiency improvement for Sn‐based perovskite solar cells with efficiency exceeding 20%43Progress and prospects of quantum emission from perovskites6Adapting Explainable Machine Learning to Study Mechanical Properties of 2D Hybrid Halide Perovskites16Machine learning for perovskite optoelectronics: a review24Imaging Locally Inhomogeneous Properties of Metal Halide Perovskites13Machine Learning Optimization of Non-Kasha Behavior and of Transient Dynamics in Model Retinal Isomerization3Machine learning enhanced characterization and optimization of photonic cured MAPbI3 for efficient perovskite solar cells0An AI-accelerated pathway for reproducible and stable halide perovskites17Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36High-performance perovskite quantum dot synthesis investigated through exploratory data analysis3Hybrid approach for simulating graded PeDA-DJ perovskite solar cells: SCAPS-1D and machine learning2Accelerating ionic liquid research in perovskite solar cells through machine learning:Opportunities and challenges5Machine Learning Prediction of Organic–Inorganic Halide Perovskite Solar Cell Performance from Optical Properties42Accelerating the discovery of direct bandgap perovskites for solar energy via machine learning6Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling1Optimizing Solar Photovoltaic Power Injection Using Machine Learning: Minimizing Harmonic Distortion Under Inertia Constraints0Machine Learning Accelerated Enhanced Power Conversion Efficiency in FASnI3 Perovskite Solar Cell4Unlocking a self-driving research workflow for perovskite photovoltaics12Comparative convolutional neural networks for perovskite solar cell PCE predictions6Silicon–Perovskite Tandem Solar Cells: An Alternative to the Market-Dominated Silicon-Based Solar Cell Technology8Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics5Autoencoder for Parameter Estimation and Current-Voltage Curve Simulation of Perovskite Solar Cells0Autoencoder for parameter estimation and current-voltage curve simulation of perovskite solar cells1AI-driven design and applications of quantum dots9Machine learning applications in the design, optimization, and manufacturing of perovskite solar cells3CsPbI3 Perovskites at the Edge of Commercialization: Persistent Barriers, Multidisciplinary Solutions, and the Emerging Role of AI4Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing3Curated Data Set, Structure–Property Relationships, and Machine Learning Prediction of Band Gaps for One-Dimensional Bismuth(III) and Antimony(III) Halides0High‐Accuracy Machine Learning Projections of Composition‐Dependent Thermal Stability in Halide Perovskites0From first-principles to device performance: a computational perspective on perovskite solar cell design0A systematic review of AI assisted SCAPS 1D modeling for hydrophilic degradation analysis and efficiency retention in perovskite solar cells0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close86 nodes / 165 citation links in this initial view
Browse Matching Papers

Papers that cite this work

85 direct citing records saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Metal Oxide-Induced Instability and Its Mitigation in Halide Perovskite Solar Cells

    The Journal of Physical Chemistry Letters · 2021

    Sampreetha Thampy, Weijie Xu, Julia W. P. Hsu

    Read original paper ↗ ·

  2. Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble

    SmartMat · 2021

    Yajing Sun, Wenping Hu

    Read original paper ↗ ·

  3. A dataquake for solar cells

    Nature Energy · 2021

    Marina S. Leite

    Read original paper ↗ ·

  4. Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence

    Energy Materials · 2022

    Jiechun Liang, Tingting Wu2, Ziwei Wang, Yunduo Yu, Linfeng Hu, Huamei Li, Xiaohong Zhang, Xi Zhu, Yu Zhao

    Read original paper ↗ ·

  5. Prediction of dielectric constants of ABO 3 -type perovskites using machine learning and first-principles calculations

    Physical Chemistry Chemical Physics · 2022

    Eunsong Kim, Joonchul Kim, Kyoungmin Min

    Read original paper ↗ ·

  6. Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform

    The Journal of Physical Chemistry Letters · 2022

    Bipeng Wang, Weibin Chu, Oleg V. Prezhdo

    Read original paper ↗ ·

  7. Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy

    The Journal of Physical Chemistry Letters · 2022

    Felix Mayr, Milan Harth, Ioannis Kouroudis, Michael Rinderle, Alessio Gagliardi

    Read original paper ↗ ·

  8. Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method

    Nanomaterials · 2022

    Soo Min Kim, Syed Dildar Haider Naqvi, Min Gu Kang, Hee‐eun Song, SeJin Ahn

    Read original paper ↗ ·

  9. Al–Au Thin Films for Thermally Stable and Highly Sensitive Plasmonic Sensors

    The Journal of Physical Chemistry C · 2022

    Molly Kate Kreider, Abdul Qadeer Rehan, Robert M. Kent, Anibal Thiago Bezerra, Mariama Rebello Sousa Dias

    Read original paper ↗ ·

  10. Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies

    Physchem · 2022

    Sergei Manzhos, Sergei Manzhos

    Read original paper ↗ ·

  11. Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water

    The Journal of Physical Chemistry C · 2022

    Lei Zhang, Wenguang Hu, Mu He, Kun Xu, Zhengwei Pan

    Read original paper ↗ ·

  12. Materials Data toward Machine Learning: Advances and Challenges

    The Journal of Physical Chemistry Letters · 2022

    Linggang Zhu, Jian Xin Zhou, Zhimei Sun

    Read original paper ↗ ·

  13. Accelerate process optimization in perovskite solar cell manufacturing with machine learning

    Matter · 2022

    Julia W. P. Hsu, Weijie Xu

    Read original paper ↗ ·

  14. Recent Progress in Mixed A‐Site Cation Halide Perovskite Thin‐Films and Nanocrystals for Solar Cells and Light‐Emitting Diodes

    Advanced Optical Materials · 2022

    Mahdi Malekshahi Byranvand, Clara Otero‐Martínez, Junzhi Ye, Weiwei Zuo, Liberato Manna, Michael Saliba, Robert L. Z. Hoye, Lakshminarayana Polavarapu

    Read original paper ↗ ·

  15. The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

    Crystals · 2022

    Hartmut Schlenz, Stefan Baumann, Wilhelm Albert Meulenberg, Olivier Guillon

    Read original paper ↗ ·

  16. Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance

    The Journal of Physical Chemistry C · 2022

    Marcos del Cueto, Charles Rawski-Furman, Juan Aragó, Enrique Ortı́, Alessandro Troisi

    Read original paper ↗ ·

  17. A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

    Journal of Chemical Information and Modeling · 2022

    Madeleine Breshears, Rajiv Giridharagopal, Justin Pothoof, David S. Ginger

    Read original paper ↗ ·

  18. Applications of machine learning in perovskite materials

    Advanced Composites and Hybrid Materials · 2022

    Ziman Wang, Ming Yang, Xixi Xie, Chenyang Yu, Qinglong Jiang, Mina Huang, Hassan Algadi, Zhanhu Guo, Hang Zhang

    Read original paper ↗ ·

  19. Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives

    Advanced Energy Materials · 2022

    Chang Woo Myung, Amir Hajibabaei, Ji‐Hyun Cha, Miran Ha, Junu Kim, Kwang S. Kim

    Read original paper ↗ ·

  20. The Spring of Processing Chemistry in Perovskite Solar Cells–Bayesian Optimization

    The Journal of Physical Chemistry Letters · 2022

    Qizhen Song, Yang Bai, Qi Henry Chen

    Read original paper ↗ ·

  21. Experimentally validated machine learning predictions of ultralow thermal conductivity for SnSe materials

    Journal of Materials Chemistry C · 2023

    Nikhil Kumar Barua, Andrew Golabek, Anton O. Oliynyk, Holger Kleinke

    Read original paper ↗ ·

  22. Perovskite-Based Materials for Photovoltaic Applications: A Machine Learning Approach

    Springer eBooks · 2023

    Ramandeep Kaur, Rajan Saini, Janpreet Singh

    Read original paper ↗ ·

  23. Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

    Solar RRL · 2023

    Felix Laufer, Sebastian Ziegler, Fabian Schackmar, Edwin A. Moreno Viteri, Markus Götz, Charlotte Debus, Fabian Isensee, Ulrich W. Paetzold

    Read original paper ↗ ·

  24. Ytterbium-Doped Lead–Halide Perovskite Nanocrystals: Synthesis, Near-Infrared Emission, and Open-Source Machine Learning Model for Prediction of Optical Properties

    Nanomaterials · 2023

    Yuliya A. Timkina, Vladislav S. Tuchin, Aleksandr P. Litvin, Elena V. Ushakova, Andrey L. Rogach

    Read original paper ↗ ·

  25. Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

    Solar RRL · 2023

    Mohamed M. Elsenety, Eleftherios Christopoulos, Polycarpos Falaras

    Read original paper ↗ ·

  26. Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy

    ACS Energy Letters · 2023

    Meghna Srivastava, Abigail R. Hering, Yu An, Juan‐Pablo Correa‐Baena, Marina S. Leite

    Read original paper ↗ ·

  27. High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap

    Materials Today Communications · 2023

    Wenguang Hu, Lei Zhang

    Read original paper ↗ ·

  28. Accelerated prediction of perovskite material properties with classical machine learning and graph neural network

    Materials Express · 2023

    Zhihao Dong, Youyong Li, Youyong Li

    Read original paper ↗ ·

  29. Machine learning for perovskite solar cell design

    Computational Materials Science · 2023

    Zhan Hui, Min Wang, Xiang Qian Yin, Yanan Wang, Yunliang Yue

    Read original paper ↗ ·

  30. Perspective on machine learning based investigated materials to realize Stable-Efficient-Toxin free (S-E-T) perovskite solar cells

    Materials Today Proceedings · 2023

    Vinitha B. Elza, V. Suresh Babu, Nelsa Abraham

    Read original paper ↗ ·

  31. First-principles and machine learning modeling on adsorption of atmospheric gases on two-dimensional Ruddlesden–Popper halide perovskite surface

    Modelling and Simulation in Materials Science and Engineering · 2023

    Lei Zhang, Shenyue Li, Wenguang Hu

    Read original paper ↗ ·

  32. The role of machine learning in perovskite solar cell research

    Journal of Alloys and Compounds · 2023

    Chen Chen, Ayman Maqsood, T. Jesper Jacobsson

    Read original paper ↗ ·

  33. Probing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learning

    ACS Applied Materials & Interfaces · 2023

    Nishi Parikh, Seçkin Akın, Abul Kalam, Daniel Prochowicz, Pankaj Yadav

    Read original paper ↗ ·

  34. Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

    Advanced Functional Materials · 2023

    Vijay Chandrakant Karade, Santosh Shashikant Sutar, Seung Wook Shin, Mahesh P. Suryawanshi, Jun Sung Jang, Kuldeep Singh Gour, Rajanish K. Kamat, Jae Ho Yun, Tukaram Dattatray Dongale, Jin Hyeok Kim

    Read original paper ↗ ·

  35. Machine learning for fast development of advanced energy materials

    Next Materials · 2023

    Bita Farhadi, Jiaxue You, Dexu Zheng, Lu Liu, Sajian Wu, Jianxun Li, Zhipeng Li, Kai Wang, Shengzhong Frank Liu

    Read original paper ↗ ·

  36. Emerging opportunities for hybrid perovskite solar cells using machine learning

    APL Energy · 2023

    Abigail R. Hering, Mansha Dubey, Marina S. Leite

    Read original paper ↗ ·

  37. Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces

    Artificial Intelligence Chemistry · 2023

    Sergei Manzhos, Tucker Carrington, Sergei Manzhos

    Read original paper ↗ ·

  38. Optoelectronic perovskite film characterization via machine vision

    Solar Energy · 2023

    Milan Harth, Luigi Vesce, I. Kouroudis, Maurizio Stefanelli, Aldo Di Carlo, Alessio Gagliardi

    Read original paper ↗ ·

  39. Assessing Performance and Limitations of a Device‐Level Machine Learning Approach for Perovskite Solar Cells with an Application to Hole Transport Materials

    Solar RRL · 2023

    Simone Sala, Eleonora Quadrivi, Paolo Biagini, Riccardo Pó

    Read original paper ↗ ·

  40. Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression

    The Journal of Physical Chemistry A · 2023

    Sergei Manzhos, Sergei Manzhos

    Read original paper ↗ ·

  41. Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

    Energy Technology · 2023

    Nitin Kumar Bansal, Snehangshu Mishra, Himanshu Dixit, Shivam Porwal, Paulomi Singh, Trilok Singh

    Read original paper ↗ ·

  42. Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

    Solar RRL · 2023

    Tiantian Liu, Sen Wang, Yinguang Shi, Lei Wu, Ruiyu Zhu, Yong Wang, Jun Zhou, Wallace C. H. Choy

    Read original paper ↗ ·

  43. Bandgap Prediction of Hybrid Organic–Inorganic Perovskite Solar Cell Using Machine Learning

    Journal of The Institution of Engineers (India) Series D · 2023

    Debmalya Sadhu, Debasis De, Devansh Dattatreya, Arjun Deo, Subir Gupta

    Read original paper ↗ ·

  44. Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

    Chinese Physics B · 2023

    Cheng-Zhou 城洲 Zhang 张, Xiao-Qian 小倩 Fu 付

    Read original paper ↗ ·

  45. Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets

    The Journal of Physical Chemistry Letters · 2023

    Lyuben Borislаvov, Miroslava Nedyalkova, Alia Tadjer, Önder Aydemir, Julia Romanova

    Read original paper ↗ ·

  46. High-Throughput Multi-Scale Experiments to Probe the Stability of Metal Halide Perovskites

    ACS Energy Letters · 2023

    Yalan Zhang, Yuanyuan Zhou

    Read original paper ↗ ·

  47. Physics-based extraction of material parameters from perovskite experiments via Bayesian optimization

    Energy & Environmental Science · 2024

    Hualin Zhan, V. U. Ahmad, Azul Osorio Mayon, Grace Dansoa Tabi, Anh Dinh Bui, Zhuofeng Li, Daniel Walter, Hieu Trong Nguyen, Klaus Weber, Thomas P. White, Kylie Catchpole

    Read original paper ↗ ·

  48. Physiochemical machine learning models predict operational lifetimes of CH 3 NH 3 PbI 3 perovskite solar cells

    Journal of Materials Chemistry A · 2024

    Wiley A. Dunlap-Shohl, Yuhuan Meng, Preetham P. Sunkari, David A. C. Beck, Marina Meilă, Hugh W. Hillhouse

    Read original paper ↗ ·

  49. Ionic liquid-assisted preparation of Ag–Zn–In–S quaternary quantum dot thin films and luminescence performance optimized by machine learning

    Dyes and Pigments · 2024

    Song Wei, Xiang Luo, Shaofeng Shao, Lei Zhang

    Read original paper ↗ ·

  50. High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications

    Cell Reports Physical Science · 2024

    Ali Ramazani, Brett A. Duell, Eric J. Popczun, Sittichai Natesakhawat, Tarak Nandi, Jonathan W. Lekse, Yuhua Duan

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.