An AI-accelerated pathway for reproducible and stable halide perovskites

Hering, Abigail R.; Sutter-Fella, Carolin M.; Leite, Marina S.

doi:10.1039/d5cs00715a

An AI-accelerated pathway for reproducible and stable halide perovskites

Journal Article · Tue Sep 30 00:00:00 EDT 2025 · Chemical Society Reviews

DOI:https://doi.org/10.1039/d5cs00715a· OSTI ID:3012938

^[1]; ^[2]; ^[1]

Univ. of California, Davis, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry

Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS)

Grant/Contract Number:: AC02-05CH11231; SC0014664

OSTI ID:: 3012938

Journal Information:: Chemical Society Reviews, Journal Name: Chemical Society Reviews Journal Issue: 22 Vol. 54; ISSN 0306-0012; ISSN 1460-4744

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

References (154)

Impact of Processing on Structural and Compositional Evolution in Mixed Metal Halide Perovskites during Film Formation Abdelsamie, Maged; Xu, Junwei; Bruening, Karsten Advanced Functional Materials, Vol. 30, Issue 38 https://doi.org/10.1002/adfm.202001752	journal	July 2020
Vacuum Processed Metal Halide Perovskite Light‐Emitting Diodes Zhou, Chun; Meng, Wenyuan; Kong, Lingmei Advanced Functional Materials, Vol. 34, Issue 8 https://doi.org/10.1002/adfm.202307682	journal	November 2023
Accelerating Materials Discovery by High‐Throughput GIWAXS Characterization of Quasi‐2D Formamidinium Metal Halide Perovskites Yang, Jonghee; Hidalgo, Juanita; Song, Donghoon Advanced Functional Materials, Vol. 34, Issue 49 https://doi.org/10.1002/adfm.202409293	journal	August 2024
Decoding the Broadband Emission of 2D Pb‐Sn Halide Perovskites through High‐Throughput Exploration Foadian, Elham; Yang, Jonghee; Harris, Sumner B. Advanced Functional Materials, Vol. 34, Issue 52 https://doi.org/10.1002/adfm.202411164	journal	August 2024
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot Epps, Robert W.; Bowen, Michael S.; Volk, Amanda A. Advanced Materials, Vol. 32, Issue 30 https://doi.org/10.1002/adma.202001626	journal	June 2020
Universal Dynamic Liquid Interface for Healing Perovskite Solar Cells Guo, Qiyao; Duan, Jialong; Zhang, Junshuai Advanced Materials, Vol. 34, Issue 26 https://doi.org/10.1002/adma.202202301	journal	May 2022
Reversible Phase Transition for Durable Formamidinium‐Dominated Perovskite Photovoltaics Liu, Huifen; Li, Nengxu; Chen, Zehua Advanced Materials, Vol. 34, Issue 39 https://doi.org/10.1002/adma.202204458	journal	August 2022
Combined Vacuum Evaporation and Solution Process for High‐Efficiency Large‐Area Perovskite Solar Cells with Exceptional Reproducibility Tan, Liguo; Zhou, Junjie; Zhao, Xing Advanced Materials, Vol. 35, Issue 13 https://doi.org/10.1002/adma.202205027	journal	February 2023
Surface Defects Control Bulk Carrier Densities in Polycrystalline Pb‐Halide Perovskites Cahen, David; Rakita, Yevgeny; Egger, David A. Advanced Materials, Vol. 36, Issue 50 https://doi.org/10.1002/adma.202407098	journal	October 2024
Materials Acceleration Platforms (MAPs): Accelerating Materials Research and Development to Meet Urgent Societal Challenges Stier, Simon P.; Kreisbeck, Christoph; Ihssen, Holger Advanced Materials, Vol. 36, Issue 45 https://doi.org/10.1002/adma.202407791	journal	September 2024
A High‐Throughput Approach to Identifying Environment‐Friendly Artificial Antisolvents for Efficient Perovskite Solar Cells Shang, Ying; Meng, Wei; Lu, Feiyue Advanced Materials, Vol. 37, Issue 29 https://doi.org/10.1002/adma.202504602	journal	April 2025
Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics Stanley, Jared C.; Mayr, Felix; Gagliardi, Alessio Advanced Theory and Simulations, Vol. 3, Issue 1 https://doi.org/10.1002/adts.201900178	journal	November 2019
Comprehensive Machine Learning Pipeline for Prediction of Power Conversion Efficiency in Perovskite Solar Cells Mohanty, Debasish; Palai, Akshaya Kumar Advanced Theory and Simulations, Vol. 6, Issue 12 https://doi.org/10.1002/adts.202300309	journal	August 2023
Exploring the Stability of Novel Wide Bandgap Perovskites by a Robot Based High Throughput Approach Chen, Shi; Hou, Yi; Chen, Haiwei Advanced Energy Materials, Vol. 8, Issue 6 https://doi.org/10.1002/aenm.201701543	journal	October 2017
Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells Li, Jinxin; Pradhan, Basudev; Gaur, Surya Advanced Energy Materials, Vol. 9, Issue 46 https://doi.org/10.1002/aenm.201901891	journal	October 2019
A Critical Review of Machine Learning of Energy Materials Chen, Chi; Zuo, Yunxing; Ye, Weike Advanced Energy Materials, Vol. 10, Issue 8 https://doi.org/10.1002/aenm.201903242	journal	January 2020
The Role of Grain Boundaries on Ionic Defect Migration in Metal Halide Perovskites Phung, Nga; Al‐Ashouri, Amran; Meloni, Simone Advanced Energy Materials, Vol. 10, Issue 20 https://doi.org/10.1002/aenm.201903735	journal	April 2020
Ink Design Enabling Slot‐Die Coated Perovskite Solar Cells with >22% Power Conversion Efficiency, Micro‐Modules, and 1 Year of Outdoor Performance Evaluation Li, Jinzhao; Dagar, Janardan; Shargaieva, Oleksandra Advanced Energy Materials, Vol. 13, Issue 33 https://doi.org/10.1002/aenm.202203898	journal	March 2023
Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells Zhang, Jiyun; Liu, Bowen; Liu, Ziyi Advanced Energy Materials, Vol. 13, Issue 48 https://doi.org/10.1002/aenm.202302594	journal	November 2023
Autonomous Optimization of Air‐Processed Perovskite Solar Cell in a Multidimensional Parameter Space Zhang, Jiyun; Le Corre, Vincent M.; Wu, Jianchang Advanced Energy Materials, Vol. 15, Issue 19 https://doi.org/10.1002/aenm.202404957	journal	January 2025
AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere Halder, Ansuman; Alghalayini, Maher B.; Cheng, Shuan Advanced Energy Materials, Vol. 15, Issue 34 https://doi.org/10.1002/aenm.202502294	journal	July 2025
Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery Delgado-Licona, Fernando; Abolhasani, Milad Advanced Intelligent Systems, Vol. 5, Issue 4 https://doi.org/10.1002/aisy.202200331	journal	December 2022
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME Salih, Ahmed M.; Raisi‐Estabragh, Zahra; Galazzo, Ilaria Boscolo Advanced Intelligent Systems, Vol. 7, Issue 1 https://doi.org/10.1002/aisy.202400304	journal	June 2024
Thermal‐Triggered Dynamic Disulfide Bond Self‐Heals Inorganic Perovskite Solar Cells Zhang, Qiaoyu; Duan, Jialong; Guo, Qiyao Angewandte Chemie International Edition, Vol. 61, Issue 8 https://doi.org/10.1002/anie.202116632	journal	January 2022
Density Functional Theory - Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden-Popper Phases Allam, Omar; Holmes, Colin; Greenberg, Zev ChemPhysChem, Vol. 19, Issue 19 https://doi.org/10.1002/cphc.201800382	journal	July 2018
Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning Odabaşı, Çağla; Yıldırım, Ramazan Energy Technology, Vol. 8, Issue 12 https://doi.org/10.1002/ente.201901449	journal	February 2020
Single‐Crystal Perovskite Halide: Crystal Growth to Devices Applications Prakash, Krishna; Valeti, Naga Jyoti; Jain, Prince Energy Technology, Vol. 13, Issue 7 https://doi.org/10.1002/ente.202400618	journal	October 2024
Memristors with organic‐inorganic halide perovskites Zhao, Xiaoning; Xu, Haiyang; Wang, Zhongqiang InfoMat https://doi.org/10.1002/inf2.12012	journal	May 2019
Advancing perovskite photovoltaic technology through machine learning‐driven automation Zhang, Jiyun; Wu, Jianchang; Le Corre, Vincent M. InfoMat, Vol. 7, Issue 5 https://doi.org/10.1002/inf2.70005	journal	February 2025
Unveiling the statistical behaviors of metal‐halide perovskites from films to devices through a high‐throughput experimental platform Deng, Can; Tang, Liu; Luo, Pingping InfoMat, Vol. 8, Issue 1 https://doi.org/10.1002/inf2.70039	journal	June 2025
Analysis and Selection of Optimal Perovskite/Silicon Tandem Configuration for Building Integrated Photovoltaics Based on Their Annual Outdoor Energy Yield Predicted by Machine Learning Nguyen, Dong C.; Asada, Tomoki; Raifuku, Itaru Solar RRL, Vol. 8, Issue 9 https://doi.org/10.1002/solr.202400072	journal	April 2024
Machine learning-driven analysis of dielectric response in polymethyl methacrylate nanocomposites reinforced with multi-walled carbon nanotubes Jain, Prince; Thakor, Sanketsinh; Joshi, Anand Journal of Materials Science: Materials in Electronics, Vol. 35, Issue 20 https://doi.org/10.1007/s10854-024-13188-x	journal	July 2024
Machine learning: applications of artificial intelligence to imaging and diagnosis Nichols, James A.; Herbert Chan, Hsien W.; Baker, Matthew A. B. Biophysical Reviews, Vol. 11, Issue 1 https://doi.org/10.1007/s12551-018-0449-9	journal	September 2018
Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques Mammeri, M.; Bencherif, H.; Dehimi, L. Journal of Optics, Vol. 54, Issue 3 https://doi.org/10.1007/s12596-024-01819-9	journal	April 2024
Surface Passivation of Perovskite Solar Cells Toward Improved Efficiency and Stability Li, Zhiqi; Dong, Jiajun; Liu, Chunyu Nano-Micro Letters, Vol. 11, Issue 1 https://doi.org/10.1007/s40820-019-0282-0	journal	June 2019
Layer perovskites of the (CnH2n+1NH3)2MX4 and NH3(CH2)mNH3MX4 families with M = Cd, Cu, Fe, Mn OR Pd and X = Cl OR Br: Importance, solubilities and simple growth techniques Arend, H.; Huber, W.; Mischgofsky, F. H. Journal of Crystal Growth, Vol. 43, Issue 2 https://doi.org/10.1016/0022-0248(78)90170-7	journal	March 1978
Toward α-phase stabilization of formamidinium lead iodide perovskites with dual-trivalent metal regulation Guo, Qiyao; Dou, Jie; Mei, Yuhan Chemical Engineering Journal, Vol. 504 https://doi.org/10.1016/j.cej.2024.158875	journal	January 2025
Balancing exploration and exploitation with information and randomization Wilson, Robert C.; Bonawitz, Elizabeth; Costa, Vincent D. Current Opinion in Behavioral Sciences, Vol. 38 https://doi.org/10.1016/j.cobeha.2020.10.001	journal	April 2021
Absorption coefficients data of lead iodine perovskites using 14 different organic cations Tablero Crespo, César Data in Brief, Vol. 27 https://doi.org/10.1016/j.dib.2019.104636	journal	December 2019
Ion migration in halide perovskite solar cells: Mechanism, characterization, impact and suppression Zai, Huachao; Ma, Yue; Chen, Qi Journal of Energy Chemistry, Vol. 63 https://doi.org/10.1016/j.jechem.2021.08.006	journal	December 2021
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing Correa-Baena, Juan-Pablo; Hippalgaonkar, Kedar; van Duren, Jeroen Joule, Vol. 2, Issue 8 https://doi.org/10.1016/j.joule.2018.05.009	journal	August 2018
Machine Learning for Perovskites' Reap-Rest-Recovery Cycle Howard, John M.; Tennyson, Elizabeth M.; Neves, Bernardo R. A. Joule, Vol. 3, Issue 2 https://doi.org/10.1016/j.joule.2018.11.010	journal	February 2019
Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis Sun, Shijing; Hartono, Noor T. P.; Ren, Zekun D. Joule, Vol. 3, Issue 6 https://doi.org/10.1016/j.joule.2019.05.014	journal	June 2019
Robot-Based High-Throughput Screening of Antisolvents for Lead Halide Perovskites Gu, Ening; Tang, Xiaofeng; Langner, Stefan Joule, Vol. 4, Issue 8 https://doi.org/10.1016/j.joule.2020.06.013	journal	August 2020
A data fusion approach to optimize compositional stability of halide perovskites Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe Matter, Vol. 4, Issue 4 https://doi.org/10.1016/j.matt.2021.01.008	journal	April 2021
Autonomous experimentation systems for materials development: A community perspective Stach, Eric; DeCost, Brian; Kusne, A. Gilad Matter, Vol. 4, Issue 9 https://doi.org/10.1016/j.matt.2021.06.036	journal	September 2021
What is a minimal working example for a self-driving laboratory? Baird, Sterling G.; Sparks, Taylor D. Matter, Vol. 5, Issue 12 https://doi.org/10.1016/j.matt.2022.11.007	journal	December 2022
Human-in-the-loop for Bayesian autonomous materials phase mapping Adams, Felix; McDannald, Austin; Takeuchi, Ichiro Matter, Vol. 7, Issue 2 https://doi.org/10.1016/j.matt.2024.01.005	journal	February 2024
Dual-intermediator guided methodical molecular exchange towards optimized crystallization kinetics of advanced perovskite solar cells Peng, Yue; Guo, Qiyao; Liu, Yueji Nano Energy, Vol. 142 https://doi.org/10.1016/j.nanoen.2025.111169	journal	September 2025
Exploring molecular interactions and dielectric relaxation in n-octanol/DMF binary mixtures: a machine learning-enhanced VNA study Chaudhary, N. A.; Jain, Prince; Thakor, Sanketsinh Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol. 339 https://doi.org/10.1016/j.saa.2025.126271	journal	October 2025
Paths towards high perovskite solar cells stability using machine learning techniques Mammeri, M.; Dehimi, L.; Bencherif, H. Solar Energy, Vol. 249 https://doi.org/10.1016/j.solener.2022.12.002	journal	January 2023
Using ISOS consensus test protocols for development of quantitative life test models in ageing of organic solar cells Kettle, J.; Stoichkov, V.; Kumar, D. Solar Energy Materials and Solar Cells, Vol. 167 https://doi.org/10.1016/j.solmat.2017.04.005	journal	August 2017
Next-Generation Experimentation with Self-Driving Laboratories Häse, Florian; Roch, Loïc M.; Aspuru-Guzik, Alán Trends in Chemistry, Vol. 1, Issue 3 https://doi.org/10.1016/j.trechm.2019.02.007	journal	June 2019
Stabilizing Perovskite Structures by Tuning Tolerance Factor: Formation of Formamidinium and Cesium Lead Iodide Solid-State Alloys Li, Zhen; Yang, Mengjin; Park, Ji-Sang Chemistry of Materials, Vol. 28, Issue 1 https://doi.org/10.1021/acs.chemmater.5b04107	journal	December 2015
Temperature-Dependent Thermal Decomposition Pathway of Organic–Inorganic Halide Perovskite Materials Ma, Lin; Guo, Deqiang; Li, Mengting Chemistry of Materials, Vol. 31, Issue 20 https://doi.org/10.1021/acs.chemmater.9b03190	journal	September 2019
Self-Driving Laboratories for Chemistry and Materials Science Tom, Gary; Schmid, Stefan P.; Baird, Sterling G. Chemical Reviews, Vol. 124, Issue 16, p. 9633-9732 https://doi.org/10.1021/acs.chemrev.4c00055	journal	August 2024
Understanding Degradation Mechanisms and Improving Stability of Perovskite Photovoltaics Boyd, Caleb C.; Cheacharoen, Rongrong; Leijtens, Tomas Chemical Reviews, Vol. 119, Issue 5 https://doi.org/10.1021/acs.chemrev.8b00336	journal	November 2018
Density Functional Theory Estimate of Halide Perovskite Band Gap: When Spin Orbit Coupling Helps Das, Tilak; Di Liberto, Giovanni; Pacchioni, Gianfranco The Journal of Physical Chemistry C, Vol. 126, Issue 4 https://doi.org/10.1021/acs.jpcc.1c09594	journal	January 2022
Machine Learning Roadmap for Perovskite Photovoltaics Srivastava, Meghna; Howard, John M.; Gong, Tao The Journal of Physical Chemistry Letters, Vol. 12, Issue 32 https://doi.org/10.1021/acs.jpclett.1c01961	journal	August 2021
Machine Learning-Enabled Optical Architecture Design of Perovskite Solar Cells Li, Zong-Zheng; Guo, Chaorong; Lv, Wenlei The Journal of Physical Chemistry Letters, Vol. 15, Issue 14 https://doi.org/10.1021/acs.jpclett.4c00320	journal	April 2024
Atomic Layer Deposition of SnO₂ as an Electron Transport Material for Solid-State P-type Dye-Sensitized Solar Cells Cheng, Haoliang; Liu, Yawen; Cai, Bin ACS Applied Energy Materials, Vol. 5, Issue 10 https://doi.org/10.1021/acsaem.2c01328	journal	September 2022
Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning Yang, Chao; Chong, Xiaoyu; Hu, Mingyu ACS Applied Materials & Interfaces, Vol. 15, Issue 34 https://doi.org/10.1021/acsami.3c06392	journal	August 2023
Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching Baumann, Daniel O.; Laufer, Felix; Roger, Julie ACS Applied Materials & Interfaces, Vol. 16, Issue 40 https://doi.org/10.1021/acsami.4c13024	journal	September 2024
Forecasting the Decay of Hybrid Perovskite Performance Using Optical Transmittance or Reflected Dark-Field Imaging Stoddard, Ryan J.; Dunlap-Shohl, Wiley A.; Qiao, Hongbo ACS Energy Letters, Vol. 5, Issue 3 https://doi.org/10.1021/acsenergylett.0c00164	journal	February 2020
Chemical Robotics Enabled Exploration of Stability in Multicomponent Lead Halide Perovskites via Machine Learning Higgins, Kate; Valleti, Sai Mani; Ziatdinov, Maxim ACS Energy Letters, Vol. 5, Issue 11 https://doi.org/10.1021/acsenergylett.0c01749	journal	October 2020
The Perovskite Database Project: A Perspective on Collective Data Sharing Unger, Eva; Jacobsson, T. Jesper ACS Energy Letters, Vol. 7, Issue 3 https://doi.org/10.1021/acsenergylett.2c00330	journal	March 2022
Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy Srivastava, Meghna; Hering, Abigail R.; An, Yu ACS Energy Letters, Vol. 8, Issue 4 https://doi.org/10.1021/acsenergylett.2c02555	journal	March 2023
The Impact of Machine Learning in Energy Materials Research: The Case of Halide Perovskites De Angelis, Filippo ACS Energy Letters, Vol. 8, Issue 2 https://doi.org/10.1021/acsenergylett.3c00182	journal	February 2023
Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells Kouroudis, Ioannis; Tanko, Kenedy Tabah; Karimipour, Masoud ACS Energy Letters, Vol. 9, Issue 4 https://doi.org/10.1021/acsenergylett.4c00328	journal	March 2024
Machine-Learning-Assisted Design of Buried-Interface Engineering Materials for High-Efficiency and Stable Perovskite Solar Cells Zhang, Qi; Wang, Han; Zhao, Qiangqiang ACS Energy Letters, Vol. 9, Issue 12 https://doi.org/10.1021/acsenergylett.4c02610	journal	November 2024
First-Principles Modeling of Defects in Lead Halide Perovskites: Best Practices and Open Issues Meggiolaro, Daniele; De Angelis, Filippo ACS Energy Letters, Vol. 3, Issue 9 https://doi.org/10.1021/acsenergylett.8b01212	journal	August 2018
From Sunlight to Solutions: Closing the Loop on Halide Perovskites Foadian, Elham; Sanchez, Sheryl; Kalinin, Sergei V. ACS Materials Au, Vol. 5, Issue 1 https://doi.org/10.1021/acsmaterialsau.4c00096	journal	October 2024
Scalable Impregnation Method for Preparing a Self-Assembled Monolayer in High-Performance Vapor-Deposited Lead-Halide Perovskite Solar Cells Hu, Shenghan; Zeng, Shuang; Deng, Xinyu ACS Nano, Vol. 19, Issue 15 https://doi.org/10.1021/acsnano.5c01479	journal	April 2025
Machine Learning-Assisted Analysis of Perovskite Solar Cell Long-Term Stability under Multiple Environmental Factors Zhao, Shanshan; Zhou, Sijia; Guo, Zhongli ACS Sustainable Chemistry & Engineering, Vol. 13, Issue 19 https://doi.org/10.1021/acssuschemeng.5c01361	journal	May 2025
Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells Kojima, Akihiro; Teshima, Kenjiro; Shirai, Yasuo Journal of the American Chemical Society, Vol. 131, Issue 17, p. 6050-6051 https://doi.org/10.1021/ja809598r	journal	May 2009
Predictive Design Model for Low-Dimensional Organic–Inorganic Halide Perovskites Assisted by Machine Learning Lyu, Ruiyang; Moore, Curtis E.; Liu, Tianyu Journal of the American Chemical Society, Vol. 143, Issue 32 https://doi.org/10.1021/jacs.1c05441	journal	August 2021
Machine Learning-Driven Insights for Phase-Stable FA_xCs_1–xPb(I_yBr_1–y)₃ Perovskites in Tandem Solar Cells Luo, Ran; Jia, Xiangkun; Niu, Xiuxiu JACS Au, Vol. 5, Issue 4 https://doi.org/10.1021/jacsau.5c00033	journal	March 2025
Defect Tolerant Semiconductors for Solar Energy Conversion Zakutayev, Andriy; Caskey, Christopher M.; Fioretti, Angela N. The Journal of Physical Chemistry Letters, Vol. 5, Issue 7 https://doi.org/10.1021/jz5001787	journal	March 2014
Bright light-emitting diodes based on organometal halide perovskite Tan, Zhi-Kuang; Moghaddam, Reza Saberi; Lai, May Ling Nature Nanotechnology, Vol. 9, Issue 9 https://doi.org/10.1038/nnano.2014.149	journal	August 2014
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning Lu, Shuaihua; Zhou, Qionghua; Ouyang, Yixin Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05761-w	journal	August 2018
Benign ferroelastic twin boundaries in halide perovskites for charge carrier transport and recombination Xiao, Xun; Li, Wenhao; Fang, Yanjun Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-16075-1	journal	May 2020
On-the-fly closed-loop materials discovery via Bayesian active learning Kusne, A. Gilad; Yu, Heshan; Wu, Changming Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19597-w	journal	November 2020
Fundamental limits to learning closed-form mathematical models from data Fajardo-Fontiveros, Oscar; Reichardt, Ignasi; De Los Ríos, Harry R. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-36657-z	journal	February 2023
Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset Hartono, Noor Titan Putri; Köbler, Hans; Graniero, Paolo Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-40585-3	journal	August 2023
Inhibition of defect-induced α-to-δ phase transition for efficient and stable formamidinium perovskite solar cells Chen, Tian; Xie, Jiangsheng; Wen, Bin Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-41853-y	journal	September 2023
How spin relaxes and dephases in bulk halide perovskites Xu, Junqing; Li, Kejun; Huynh, Uyen N. Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-023-42835-w	journal	January 2024
Surface chemical polishing and passivation minimize non-radiative recombination for all-perovskite tandem solar cells Pan, Yongyan; Wang, Jianan; Sun, Zhenxing Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-024-51703-0	journal	August 2024
Autonomous platform for solution processing of electronic polymers Wang, Chengshi; Kim, Yeon-Ju; Vriza, Aikaterini Nature Communications, Vol. 16, Issue 1 https://doi.org/10.1038/s41467-024-55655-3	journal	February 2025
Flexibility meets rigidity: a self-assembled monolayer materials strategy for perovskite solar cells Yang, Jie; Qu, Geping; Qiao, Ying Nature Communications, Vol. 16, Issue 1 https://doi.org/10.1038/s41467-025-62388-4	journal	July 2025
Perovskite synthesizability using graph neural networks Gu, Geun Ho; Jang, Jidon; Noh, Juhwan npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00757-z	journal	April 2022
Designing mechanically tough graphene oxide materials using deep reinforcement learning Zheng, Bowen; Zheng, Zeyu; Gu, Grace X. npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00919-z	journal	November 2022
A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing Shetty, Pranav; Rajan, Arunkumar Chitteth; Kuenneth, Chris npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01003-w	journal	April 2023
Machine learning for automated experimentation in scanning transmission electron microscopy Kalinin, Sergei V.; Mukherjee, Debangshu; Roccapriore, Kevin npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01142-0	journal	December 2023
Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics Kim, Jin-Soo; Noh, Juhwan; Im, Jino npj Computational Materials, Vol. 10, Issue 1 https://doi.org/10.1038/s41524-024-01270-1	journal	May 2024
Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material Wang, Shaojun; Huang, Yiru; Hu, Wenguang npj Computational Materials, Vol. 10, Issue 1 https://doi.org/10.1038/s41524-024-01297-4	journal	May 2024
Deep reinforcement learning for inverse inorganic materials design Karpovich, Christopher; Pan, Elton; Olivetti, Elsa A. npj Computational Materials, Vol. 10, Issue 1 https://doi.org/10.1038/s41524-024-01474-5	journal	December 2024
Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery Li, Xiaobo; Che, Yu; Chen, Linjiang Nature Chemistry, Vol. 16, Issue 8 https://doi.org/10.1038/s41557-024-01546-5	journal	June 2024
Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures Khenkin, Mark V.; Katz, Eugene A.; Abate, Antonio Nature Energy, Vol. 5, Issue 1 https://doi.org/10.1038/s41560-019-0529-5	journal	January 2020
An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles Jacobsson, T. Jesper; Hultqvist, Adam; García-Fernández, Alberto Nature Energy, Vol. 7, Issue 1 https://doi.org/10.1038/s41560-021-00941-3	journal	December 2021
Anion optimization for bifunctional surface passivation in perovskite solar cells Xu, Jian; Chen, Hao; Grater, Luke Nature Materials, Vol. 22, Issue 12 https://doi.org/10.1038/s41563-023-01705-y	journal	October 2023
Mesoporous structured MoS2 as an electron transport layer for efficient and stable perovskite solar cells Koo, Donghwan; Choi, Yunseong; Kim, Ungsoo Nature Nanotechnology, Vol. 20, Issue 1 https://doi.org/10.1038/s41565-024-01799-8	journal	October 2024
An autonomous laboratory for the accelerated synthesis of novel materials Szymanski, Nathan J.; Rendy, Bernardus; Fei, Yuxing Nature, Vol. 624, Issue 7990 https://doi.org/10.1038/s41586-023-06734-w	journal	November 2023
Autonomous mobile robots for exploratory synthetic chemistry Dai, Tianwei; Vijayakrishnan, Sriram; Szczypiński, Filip T. Nature, Vol. 635, Issue 8040 https://doi.org/10.1038/s41586-024-08173-7	journal	November 2024
Interface engineering and defect passivation for enhanced hole extraction, ion migration, and optimal charge dynamics in both lead-based and lead-free perovskite solar cells Noman, Muhammad; Khan, Abdul Haseeb Hassan; Jan, Shayan Tariq Scientific Reports, Vol. 14, Issue 1 https://doi.org/10.1038/s41598-024-56246-4	journal	March 2024
Evaluation metrics and statistical tests for machine learning Rainio, Oona; Teuho, Jarmo; Klén, Riku Scientific Reports, Vol. 14, Issue 1 https://doi.org/10.1038/s41598-024-56706-x	journal	March 2024
Performance and optimization study of selected 4-terminal tandem solar cells shokrollahi, Zeinab; Piralaee, Mina; Asgari, Asghar Scientific Reports, Vol. 14, Issue 1 https://doi.org/10.1038/s41598-024-62085-0	journal	May 2024
Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization Huang, Haiyang; Wang, Yingfan; Rudin, Cynthia Communications Biology, Vol. 5 https://doi.org/10.1038/s42003-022-03628-x	journal	July 2022
Data-science driven autonomous process optimization Christensen, Melodie; Yunker, Lars P. E.; Adedeji, Folarin Communications Chemistry, Vol. 4, Issue 1 https://doi.org/10.1038/s42004-021-00550-x	journal	August 2021
Physics-informed machine learning Karniadakis, George Em; Kevrekidis, Ioannis G.; Lu, Lu Nature Reviews Physics, Vol. 3, Issue 6 https://doi.org/10.1038/s42254-021-00314-5	journal	May 2021
High-throughput exploration of halide perovskite compositionally-graded films and degradation mechanisms Moradi, Shahram; Kundu, Soumya; Rezazadeh, Milad Communications Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s43246-022-00235-5	journal	March 2022
Degradation pathways in perovskite solar cells and how to meet international standards Zhang, Deyi; Li, Daiyu; Hu, Yue Communications Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s43246-022-00281-z	journal	August 2022
Probing out-of-distribution generalization in machine learning for materials Li, Kangming; Rubungo, Andre Niyongabo; Lei, Xiangyun Communications Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s43246-024-00731-w	journal	January 2025
A robotic platform for the synthesis of colloidal nanocrystals Zhao, Haitao; Chen, Wei; Huang, Hao Nature Synthesis, Vol. 2, Issue 6 https://doi.org/10.1038/s44160-023-00250-5	journal	March 2023
The FAIR Guiding Principles for scientific data management and stewardship Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.18	journal	March 2016
Lead Iodide Perovskite Sensitized All-Solid-State Submicron Thin Film Mesoscopic Solar Cell with Efficiency Exceeding 9% Kim, Hui-Seon; Lee, Chang-Ryul; Im, Jeong-Hyeok Scientific Reports, Vol. 2, Issue 1 https://doi.org/10.1038/srep00591	journal	August 2012
Formamidinium lead trihalide: a broadly tunable perovskite for efficient planar heterojunction solar cells Eperon, Giles E.; Stranks, Samuel D.; Menelaou, Christopher Energy & Environmental Science, Vol. 7, Issue 3 https://doi.org/10.1039/C3EE43822H	journal	January 2014
Light and oxygen induced degradation limits the operational stability of methylammonium lead triiodide perovskite solar cells Bryant, Daniel; Aristidou, Nicholas; Pont, Sebastian Energy & Environmental Science, Vol. 9, Issue 5 https://doi.org/10.1039/C6EE00409A	journal	January 2016
A technoeconomic analysis of perovskite solar module manufacturing with low-cost materials and techniques Song, Zhaoning; McElvany, Chad L.; Phillips, Adam B. Energy & Environmental Science, Vol. 10, Issue 6 https://doi.org/10.1039/C7EE00757D	journal	January 2017
Emerging perovskite materials for high density data storage and artificial synapses Wang, Yan; Lv, Ziyu; Zhou, Li Journal of Materials Chemistry C, Vol. 6, Issue 7 https://doi.org/10.1039/C7TC05326F	journal	January 2018
Imperfections and their passivation in halide perovskite solar cells Chen, Bo; Rudd, Peter N.; Yang, Shuang Chemical Society Reviews, Vol. 48, Issue 14 https://doi.org/10.1039/C8CS00853A	journal	January 2019
Iodine chemistry determines the defect tolerance of lead-halide perovskites Meggiolaro, Daniele; Motti, Silvia G.; Mosconi, Edoardo Energy & Environmental Science, Vol. 11, Issue 3 https://doi.org/10.1039/C8EE00124C	journal	January 2018
Chemical stability and instability of inorganic halide perovskites Zhou, Yuanyuan; Zhao, Yixin Energy & Environmental Science, Vol. 12, Issue 5 https://doi.org/10.1039/C8EE03559H	journal	January 2019
A pencil-and-paper method for elucidating halide double perovskite band structures Slavney, Adam H.; Connor, Bridget A.; Leppert, Linn Chemical Science, Vol. 10, Issue 48 https://doi.org/10.1039/C9SC03219C	journal	January 2019
Upper limit to the photovoltaic efficiency of imperfect crystals from first principles Kim, Sunghyun; Márquez, José A.; Unold, Thomas Energy & Environmental Science, Vol. 13, Issue 5 https://doi.org/10.1039/D0EE00291G	journal	January 2020
Identifying high-performance and durable methylammonium-free lead halide perovskites via high-throughput synthesis and characterization An, Yu; Perini, Carlo Andrea Riccardo; Hidalgo, Juanita Energy & Environmental Science, Vol. 14, Issue 12 https://doi.org/10.1039/D1EE02691G	journal	January 2021
A high-throughput computational dataset of halide perovskite alloys Yang, Jiaqi; Manganaris, Panayotis; Mannodi-Kanakkithodi, Arun Digital Discovery, Vol. 2, Issue 3 https://doi.org/10.1039/D3DD00015J	journal	January 2023
A universal ligand for lead coordination and tailored crystal growth in perovskite solar cells Yang, Bowen; Suo, Jiajia; Bogachuk, Dmitry Energy & Environmental Science, Vol. 17, Issue 4 https://doi.org/10.1039/D3EE02344C	journal	January 2024
Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab Sadeghi, Sina; Bateni, Fazel; Kim, Taekhoon Nanoscale, Vol. 16, Issue 2 https://doi.org/10.1039/D3NR05034C	journal	January 2024
ChatGPT integration in perovskite research: unveiling pros and cons of AI integration for scientific advancements Castriotta, Luigi Angelo Sustainable Energy & Fuels, Vol. 8, Issue 4 https://doi.org/10.1039/D3SE01562A	journal	January 2024
Dimensional diversity (0D, 1D, 2D, and 3D) in perovskite solar cells: exploring the potential of mixed-dimensional integrations Li, Xin; Aftab, Sikandar; Hussain, Sajjad Journal of Materials Chemistry A, Vol. 12, Issue 8 https://doi.org/10.1039/D3TA06953B	journal	January 2024
PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys Cakan, Deniz N.; Kumar, Rishi E.; Oberholtz, Eric Digital Discovery, Vol. 3, Issue 6 https://doi.org/10.1039/D4DD00075G	journal	January 2024
Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors Cakan, Deniz N.; Oberholtz, Eric; Kaushal, Ken Materials Advances, Vol. 6, Issue 2 https://doi.org/10.1039/D4MA00747F	journal	January 2025
Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films via precise automated characterization methods and machine learning assisted analysis Wieczorek, Alexander; Kuba, Austin G.; Sommerhäuser, Jan Journal of Materials Chemistry A, Vol. 12, Issue 12 https://doi.org/10.1039/d3ta07274f	journal	January 2024
Materials integration of gallium arsenide and silicon by wafer bonding Kopperschmidt, P.; Senz, S.; Kästner, G. Applied Physics Letters, Vol. 72, Issue 24 https://doi.org/10.1063/1.121586	journal	June 1998
Emerging opportunities for hybrid perovskite solar cells using machine learning Hering, Abigail R.; Dubey, Mansha; Leite, Marina S. APL Energy, Vol. 1, Issue 2 https://doi.org/10.1063/5.0146828	journal	July 2023
Data-driven design for enhanced efficiency of Sn-based perovskite solar cells using machine learning Rumman, Abdul Hamid; Sahriar, Miah Abdullah; Islam, Md Tohidul APL Machine Learning, Vol. 1, Issue 4 https://doi.org/10.1063/5.0177271	journal	December 2023
Enhancing overall performance of thermophotovoltaics via deep reinforcement learning-based optimization Yu, Shilv; Chen, Zihe; Liao, Wentao Journal of Applied Physics, Vol. 136, Issue 2 https://doi.org/10.1063/5.0213211	journal	July 2024
Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design Gomes, Guilherme Ferreira; Bendine, Kouider; Pereira, Joao Luiz Junho Mechanics Based Design of Structures and Machines, Vol. 53, Issue 8 https://doi.org/10.1080/15397734.2025.2476041	journal	March 2025
The environmental footprint of data centers in the United States Siddik, Md Abu Bakar; Shehabi, Arman; Marston, Landon Environmental Research Letters, Vol. 16, Issue 6 https://doi.org/10.1088/1748-9326/abfba1	journal	May 2021
The NOMAD laboratory: from data sharing to artificial intelligence Draxl, Claudia; Scheffler, Matthias Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab13bb	journal	May 2019
The 4D Camera: An 87 kHz Direct Electron Detector for Scanning/Transmission Electron Microscopy Ercius, Peter; Johnson, Ian J.; Pelz, Philipp Microscopy and Microanalysis https://doi.org/10.1093/mam/ozae086	journal	September 2024
Electron Transport in Single-Domain, Ferroelectric Barium Titanate Berglund, C. N.; Baer, W. S. Physical Review, Vol. 157, Issue 2 https://doi.org/10.1103/PhysRev.157.358	journal	May 1967
Optimized perturbation theory Stevenson, P. M. Physical Review D, Vol. 23, Issue 12 https://doi.org/10.1103/PhysRevD.23.2916	journal	June 1981
Using GPUs for machine learning algorithms Steinkraus, D.; Buck, I.; Simard, P. Y. Eighth International Conference on Document Analysis and Recognition (ICDAR'05) https://doi.org/10.1109/ICDAR.2005.251	conference	January 2005
Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. Moore, Gordon E. IEEE Solid-State Circuits Society Newsletter, Vol. 11, Issue 3 https://doi.org/10.1109/N-SSC.2006.4785860	journal	September 2006
Ultrahigh sensitivity of methylammonium lead tribromide perovskite single crystals to environmental gases Fang, Hong-Hua; Adjokatse, Sampson; Wei, Haotong Science Advances, Vol. 2, Issue 7 https://doi.org/10.1126/sciadv.1600534	journal	July 2016
Self-driving laboratory for accelerated discovery of thin-film materials MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz8867	journal	May 2020
Autonomous robotic nanofabrication with reinforcement learning Leinen, Philipp; Esders, Malte; Schütt, Kristof T. Science Advances, Vol. 6, Issue 36 https://doi.org/10.1126/sciadv.abb6987	journal	September 2020
Efficient Hybrid Solar Cells Based on Meso-Superstructured Organometal Halide Perovskites Lee, M. M.; Teuscher, J.; Miyasaka, T. Science, Vol. 338, Issue 6107, p. 643-647 https://doi.org/10.1126/science.1228604	journal	October 2012
Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Anti-Solvent Crystallization Strategies for Highly Efficient Perovskite Solar Cells Konstantakou, Maria; Perganti, Dorothea; Falaras, Polycarpos Crystals, Vol. 7, Issue 10 https://doi.org/10.3390/cryst7100291	journal	September 2017
Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP) Gebreyesus, Yibrah; Dalton, Damian; Nixon, Sebastian Future Internet, Vol. 15, Issue 3 https://doi.org/10.3390/fi15030088	journal	February 2023
Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites Hering, Abigail R.; Dubey, Mansha; Hosseini, Elahe arXiv https://doi.org/10.48550/arXiv.2504.04002	preprint	January 2025

Similar Records

A Review and Perspective on Cathodoluminescence Analysis of Halide Perovskites

Journal Article · Wed Feb 26 19:00:00 EST 2020 · Advanced Energy Materials · OSTI ID:1603929

An AI-accelerated pathway for reproducible and stable halide perovskites

Citation Formats

References (154)

Similar Records

Related Subjects