BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2021

Machine learning for perovskite materials design and discovery

Qiuling Tao, Pengcheng Xu, Minjie Li, Wencong Lu

Read original paper ↗

See where this research leads.

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

Open in your workspace →Saved 2026-10-14 · complete coverage
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
42 direct citing works

Machine learning for perovskite materials design and discoveryOriginalThe efficacy of Lewis affinity scale metrics to represent solvent interactions with reagent salts in all-inorganic metal halide perovskite solutions34Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors39Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High‐Throughput Engineering Platform31Predicting Perovskite Performance with Multiple Machine-Learning Algorithms30Is machine learning redefining the perovskite solar cells?59Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy71Recent developments on hybrid perovskite materials for solar energy conversion and environmental protection25Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Metal Oxide-Induced Instability and Its Mitigation in Halide Perovskite Solar Cells43Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites68Roadmap on organic–inorganic hybrid perovskite semiconductors and devices201Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Bismuth-based halide perovskite and perovskite-inspired light absorbing materials for photovoltaics59Novel Materials for Urban Farming91Machine learning for high-throughput experimental exploration of metal halide perovskites117A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Photocatalysis in alkali activated cementitious materials23Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning47Δ-Machine learning-driven discovery of double hybrid organic–inorganic perovskites31Account of Structural, Theoretical, and Photovoltaic Properties of ABO3 Oxide Perovskites Photoanode‐Based Dye‐Sensitized Solar Cells31Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI 3– x Br x )53An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites43Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence62Intelligent control of nanoparticle synthesis through machine learning83Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prediction of dielectric constants of ABO 3 -type perovskites using machine learning and first-principles calculations34RbSnX 3 (X = Cl, Br, I): promising lead-free metal halide perovskites for photovoltaics and optoelectronics114Recent progress in the data-driven discovery of novel photovoltaic materials44Plasmonic–perovskite solar cells, light emitters, and sensors161Analysis of photoelectrochemical water splitting using machine learning48Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites43Machine learning analysis of gas phase photocatalytic CO2 reduction for hydrogen production42Screening Perovskites from ABO3 Combinations Generated by Constraint Satisfaction Techniques Using Machine Learning35Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Machine learning in energy storage materials106Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach72Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up35Machine learning and symbolic regression for adsorption of atmospheric molecules on low-dimensional TiO223Nanophotonics for Perovskite Solar Cells34Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning24Recent progress in nanocrystalline Sm–Co based magnets35Machine learning in concrete science: applications, challenges, and best practices379Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength36Material machine learning for alloys: Applications, challenges and perspectives150Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics74Recent Progress in Mixed A‐Site Cation Halide Perovskite Thin‐Films and Nanocrystals for Solar Cells and Light‐Emitting Diodes160Calcium copper titanate a perovskite oxide structure: effect of fabrication techniques and doping on electrical properties—a review25The effects of organic cation rotation in hybrid Organic-Inorganic Perovskites: A critical review48Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance38Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations36Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102Machine learning-facilitated multiscale imaging for energy materials27Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells86Applications of machine learning in perovskite materials80Machine learned synthesizability predictions aided by density functional theory30Rapid discovery of inorganic-organic solid composite electrolytes by unsupervised learning31Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Challenges and Opportunities of Chemiresistors Based on Microelectromechanical Systems for Chemical Olfaction52Inverse design of truss lattice materials with superior buckling resistance111Thermal evaporation and hybrid deposition of perovskite solar cells and mini-modules191Accelerated chemical space search using a quantum-inspired cluster expansion approach25Machine learning assisted synthetic acceleration of Ruddlesden-Popper and Dion-Jacobson 2D lead halide perovskites30Design of polyimides with targeted glass transition temperature using a graph neural network50Lead-free perovskite-inspired semiconductors for indoor light-harvesting – the present and the future53Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Rapid Data‐Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion57Analysis and evaluation of machine learning applications in materials design and discovery33A Critical Review of Machine Learning Techniques on Thermoelectric Materials66Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning34Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Small data machine learning in materials science866Machine Learning for Halide Perovskite Materials ABX3 (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy46Structural, topological, dielectric, and electrical properties of a novel calcium bismuth tungstate ceramic for some device applications56Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite56Feature Selection in Machine Learning for Perovskite Materials Design and Discovery59Structural, Electronic, Elastic, and Optical Properties of Cubic BaLiX3 (X = F, Cl, Br, or I) Perovskites: An Ab-initio DFT Study56Methods, progresses, and opportunities of materials informatics72Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells40A review on computational modeling of instability and degradation issues of halide perovskite photovoltaic materials24Perovskite micro-/nanoarchitecture for photonic applications40Passivation strategies for enhancing device performance of perovskite solar cells119Optimizing and extending ion dielectric polarizability database for microwave frequencies using machine learning methods55Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells43High-Throughput Screening on Halide Perovskite Derivatives and Rational Design of Cs 3 LuCl 629Meta-analysis of CO2 conversion, energy efficiency, and other performance data of plasma-catalysis reactors with the open access PIONEER database42MIC-SHAP: An ensemble feature selection method for materials machine learning35Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer28Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial243Application of machine learning models in groundwater quality assessment and prediction: progress and challenges32Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis161Machine Learning-Accelerated Discovery of A 2 BC 2 Ternary Electrides with Diverse Anionic Electron Densities55Quantitative evaluation of collector flotation performance II: The creation of a collector property index based on molecular structure24Accelerating stability of ABX3 perovskites analysis with machine learning46An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics52Machine learning – informed development of high entropy alloys with enhanced corrosion resistance35Combining component screening, machine learning and molecular engineering for the design of high-performance inverted perovskite solar cells28Applications of perovskite oxides for the cleanup and mechanism of action of emerging contaminants/steroid hormones in water29Lead-Free Double Perovskites: A Review of the Structural, Optoelectronic, Mechanical, and Thermoelectric Properties Derived from First-Principles Calculations, and Materials Design Applicable for Pedagogical Purposes94Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection41Machine learning as a characterization method for analysis and design of perovskite solar cells26Perovskite Multiple Quantum Well Superlattices: Potentials and Challenges25High-Throughput Ensemble-Learning-Driven Band Gap Prediction of Double Perovskites Solar Cells Absorber30Phase Transitions and Dynamics in Mixed Three- and Low-Dimensional Lead Halide Perovskites134Prediction of collector flotation performance based on machine learning and quantum chemistry: A case of sulfide minerals36Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells37Rubidium based new lead free high performance perovskite solar cells with SnS2 as an electron transport layer45Machine learning for CO 2 capture and conversion: A review60B-site deficient hexagonal perovskites: Structural stability, ionic order-disorder and electrical properties36Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes32Augmenting large language models with chemistry tools762AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity36Machine learning guided efficiency improvement for Sn‐based perovskite solar cells with efficiency exceeding 20%43Machine learning studies for magnetic compositionally complex alloys: A critical review33The mastery of details in the workflow of materials machine learning64Advanced prediction of perovskite stability for solar energy using machine learning34Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization29i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations70Osteoinductive biomaterials: Machine learning for prediction and interpretation25Machine learning for perovskite optoelectronics: a review24Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers60Perovskite-based solar cells in photovoltaics for commercial scalability: Current progress, challenges, mitigations and future prospectus74A review on research progress of double perovskite oxides for oxygen evolution reaction electrocatalysts and supercapacitors46Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36Lead-free perovskites for next-generation applications: a comprehensive computational and data-driven review27Molecular representation learning: cross-domain foundations and future Frontiers27Machine learning strategies for small sample size in materials science41MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration78Atomistic Origin of Microsecond Carrier Lifetimes at Perovskite Grain Boundaries: Machine Learning-Assisted Nonadiabatic Molecular Dynamics27AI-Driven Inverse Design of Materials: Past, Present, and Future78Revolutionizing the Future of Smart Materials: A Review of 4D Printing, Design, Optimization, and Machine Learning Integration39Machine Learning-Assisted Prediction and Control of Bandgap for Organic–Inorganic Metal Halide Perovskites31Machine Learning Prediction of Organic–Inorganic Halide Perovskite Solar Cell Performance from Optical Properties42Multi-objective optimization in machine learning assisted materials design and discovery25Progress and Developments in the Fabrication and Characterization of Metal Halide Perovskites for Photovoltaic Applications42AI‐Generated Ammonium Ligands for High‐Efficiency and Stable 2D/3D Heterojunction Perovskite Solar Cells32Design Strategies and Emerging Applications of Perovskite‐Based Sensors40LLM-Prop: predicting the properties of crystalline materials using large language models32Applications of machine learning in heavy metal adsorption modeling: A review42Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization44Machine learning in polymer science: A new lens for physical and chemical exploration35From Formability to Bandgap: Machine Learning Accelerates the Discovery and Application of Perovskite Materials28Leveraging machine learning for the design and optimization of environmentally friendly K2CuCrCl6 perovskite solar cells25Recent Advancements and Perspectives of Low-Dimensional Halide Perovskites for Visual Perception and Optoelectronic Applications32Review of machine learning approaches for predicting mechanical behavior of composite materials83Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Predictive modeling of thermoplastic nanocomposites using machine learning algorithms41

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 close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

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

  1. Modelling of phase stability: integrating computational materials science and materials informatics

    Journal of Materials Informatics · 2021-01-01

    Xiaoyan Song, Kai Guo, Hao Lu, Dong Liu, Fawei Tang

    Read original paper ↗ ·

  2. The efficacy of Lewis affinity scale metrics to represent solvent interactions with reagent salts in all-inorganic metal halide perovskite solutions

    Journal of Materials Chemistry A · 2021-01-01

    Oluwaseun Romiluyi, Yannick L. Eatmon, Ruihao Ni, Barry P. Rand, Paulette Clancy

    Read original paper ↗ ·

  3. A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

    ChemPlusChem · 2021-05-01

    Murat Onur Yildirim, Elif Ceren Gök, Naveen Harindu Hemasiri, Esin Eren, Samrana Kazim, Ayşegül Uygun Öksüz, Shahzada Ahmad

    Read original paper ↗ ·

  4. Exploring structure-composition relationships of cubic perovskite oxides via extreme feature engineering and automated machine learning

    Materials Today Communications · 2021-06-26

    Qin Deng, Bin Lin

    Read original paper ↗ ·

  5. Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors

    Computational Materials Science · 2021-07-05

    Yuxin Li, Rongzhi Dong, Wenhui Yang, Jianjun Hu

    Read original paper ↗ ·

  6. Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High‐Throughput Engineering Platform

    ChemSusChem · 2021-07-08

    Rong Wang, Larry Lüer, Stefan Langner, Thomas Heumueller, Karen K. Forberich, Heyi Zhang, Jens Hauch, Ning Li, Christoph Josef Brabec

    Read original paper ↗ ·

  7. Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

    Crystals · 2021-07-14

    Ruoyu Li, Qin Deng, Dong Tian, Daoye Zhu, Bin Lin

    Read original paper ↗ ·

  8. (INVITED) Revisiting Duffy's model for Sb3+ and Bi3+ in double halide perovskites: Emergence of a descriptor for machine learning

    Optical Materials X · 2021-07-21

    Philippe Boutinaud

    Read original paper ↗ ·

  9. Is machine learning redefining the perovskite solar cells?

    Journal of Energy Chemistry · 2021-07-30

    Nishi Parikh, Meera R. Karamta, Neha Yadav, Mohammad Mahdi Tavakoli, Daniel Prochowicz, Seçkin Akın, Abul Kalam, Soumitra Satapathi, Pankaj Yadav

    Read original paper ↗ ·

  10. Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy

    The Journal of Physical Chemistry Letters · 2021-07-31

    Shilin Zhang, Tian Lu, Pengcheng Xu, Qiuling Tao, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  11. Recent developments on hybrid perovskite materials for solar energy conversion and environmental protection

    Current Opinion in Chemical Engineering · 2021-08-09

    Andreas Kaltzoglou, Polycarpos Falaras

    Read original paper ↗ ·

  12. Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

    Journal of Chemical Information and Modeling · 2021-08-10

    Pengcheng Xu, Dongping Chang, Tian Lu, Long Li, Minjie Li, Wencong Lu

    Read original paper ↗ ·

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

    The Journal of Physical Chemistry Letters · 2021-08-27

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

    Read original paper ↗ ·

  14. Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

    ChemRxiv · 2021-08-30

    Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

    Read original paper ↗ ·

  15. Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

    Unknown journal · 2021-08-30

    Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

    Read original paper ↗ ·

  16. Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple

    The Journal of Physical Chemistry Letters · 2021-08-31

    Tianhao Su, Yaning Cui, Zhengheng Lian, Minglang Hu, Minjie Li, Wencong Lu, Wei Li Ren

    Read original paper ↗ ·

  17. Investigation of Opto-Electronic Properties and Stability of Mixed-Cation Mixed-Halide Perovskite Materials with Machine-Learning Implementation

    Energies · 2021-09-01

    Nicolae Filipoiu, Tudor Luca Mitran, Dragoş-Victor Anghel, Mihaela Florea, Ioana Pintilie, Andrei R. Manolescu, George Alexandru Nemneş

    Read original paper ↗ ·

  18. Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening

    Advanced Theory and Simulations · 2021-09-04

    Joonchul Kim, Eunsong Kim, Kyoungmin Min

    Read original paper ↗ ·

  19. Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites

    Chemistry of Materials · 2021-09-13

    Ekaterina I. Marchenko, Vadim Korolev, Sergey A. Fateev, Artem A. Mitrofanov, Nikolay N. Eremin, Eugene Alekseevich Goodilin, Alexey B. Tarasov

    Read original paper ↗ ·

  20. Roadmap on organic–inorganic hybrid perovskite semiconductors and devices

    APL Materials · 2021-10-01

    Lukas Schmidt‐Mende, Vladimir Dyakonov, Selina Olthof, Feray Ünlü, Khan Lê, Sanjay Mathur, Andrei D. Karabanov, Doru Constantin Lupascu, Laura M. Herz, Alexander Hinderhofer, Frank Schreiber, Alexey Chernikov, David A. Egger, Oleksandra Shargaieva, Caterina Cocchi, Eva Unger, Michael Saliba, Mahdi Malekshahi Byranvand, Martin Kroll, Frederik Nehm, Karl Leo, Alex Redinger, Julian Höcker, Thomas Kirchartz, Jonathan H. Warby, Emilio Gutierrez‐Partida, Dieter Neher, Martin Stolterfoht, Uli Würfel, Moritz Unmüssig, Jan Herterich, Clemens Baretzky, John Mohanraj, Mukundan Thelakkat, Clément Maheu, Wolfram Jaegermann, Thomas Mayer, Janek Rieger, Thomas Fauster, Daniel Niesner, Fengjiu Yang, Steve Albrecht, Thomas Riedl, Azhar Fakharuddin, Maria Vasilopoulou, Yana Vaynzof, Davide Moia, Joachim Maier, Marius Franckevičius, Vidmantas Gulbinas, Ross A. Kerner, Lianfeng Zhao, Barry P. Rand, Nadja Glück, Thomas Bein, Fabio Matteocci, Luigi Angelo Castriotta, Aldo Di Carlo, Matthias Scheffler, Claudia Draxl

    Read original paper ↗ ·

  21. Machine Learning Aided Discovery of the Layered Double Hydroxides with the Largest Basal Spacing for Super-Capacitors

    International Journal of Electrochemical Science · 2021-10-12

    Kailiang Lu, Dongping Chang, Xiaobo Ji, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  22. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

    Advanced Materials · 2021-10-12

    Watcharop Chaikittisilp, Yusuke Yamauchi, Katsuhiko Ariga

    Read original paper ↗ ·

  23. Bismuth-based halide perovskite and perovskite-inspired light absorbing materials for photovoltaics

    Journal of Physics D Applied Physics · 2021-10-15

    Feray Ünlü, Meenal S. Deo, Sanjay Mathur, Thomas Kirchartz, Ashish Kulkarni

    Read original paper ↗ ·

  24. Novel Materials for Urban Farming

    Advanced Materials · 2021-10-19

    Lifei Xi, Mengyuan Zhang, L. Zhang, Tedrick Thomas Salim Lew, Yeng Ming Lam

    Read original paper ↗ ·

  25. Machine learning for high-throughput experimental exploration of metal halide perovskites

    Joule · 2021-10-25

    Mahshid Ahmadi, Maxim A Ziatdinov, Yuanyuan Zhou, Eric Andrew Lass, Sergei V. Kalinin

    Read original paper ↗ ·

  26. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

    Acta Materialia · 2021-11-01

    Yang Chen, Chang Ren, Yuefei Jia, Gang Wang, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  27. Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

    Unknown journal · 2021-11-12

    Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

    Read original paper ↗ ·

  28. Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

    ChemRxiv · 2021-11-12

    Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

    Read original paper ↗ ·

  29. DeepTMC: A deep learning platform to targeted design doped transition metal compounds

    Energy storage materials · 2021-11-15

    Zhilong Wang, Yanqiang Han, Junfei Cai, Sicheng Wu, Jinjin Li

    Read original paper ↗ ·

  30. Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering

    arXiv (Cornell University) · 2021-11-15

    Utkarsh Saha, Koyendrila Debnath, Soumitra Satapathi

    Read original paper ↗ ·

  31. Photocatalysis in alkali activated cementitious materials

    Journal of Building Engineering · 2021-11-25

    Yidi Wang, Na Zhang, Yi Zhou, Chao Li, Jihan Zhao, Yihe Zhang

    Read original paper ↗ ·

  32. Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning

    Solar RRL · 2021-11-25

    Elif Ceren Gök, Murat Onur Yildirim, Muhammed P. U. Haris, Esin Eren, Meenakshi Pegu, Naveen Harindu Hemasiri, Peng Huang, Samrana Kazim, Ayşegül Uygun Öksüz, Shahzada Ahmad

    Read original paper ↗ ·

  33. XGBoost automates the characterisation of reversibly actuating planar-flow-casted NiTi shape memory alloy foil

    Research Square · 2021-12-02

    Ritaban Dutta, Cherry Chen, David Renshaw, Daniel Liang

    Read original paper ↗ ·

  34. Δ-Machine learning-driven discovery of double hybrid organic–inorganic perovskites

    Journal of Materials Chemistry A · 2021-12-13

    Jialu Chen, Wenjun Xu, Rui-Qin Zhang

    Read original paper ↗ ·

  35. Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

    Unknown journal · 2021-12-16

    Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

    Read original paper ↗ ·

  36. Account of Structural, Theoretical, and Photovoltaic Properties of ABO3 Oxide Perovskites Photoanode‐Based Dye‐Sensitized Solar Cells

    Solar RRL · 2021-12-17

    Karuppanapillai Baskardurai Bhojanaa, A. Soundarya Mary, K. S. Shalini Devi, Nagaraj Pavithra, Alagarsamy Pandikumar

    Read original paper ↗ ·

  37. Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI 3– x Br x )

    ACS Applied Materials & Interfaces · 2021-12-28

    Hasan Al Jame, Saugata Sarker, Md. Shafiqul Islam, Md Tohidul Islam, Abrar Rauf, Sumaiyatul Ahsan, Sadiq Shahriyar Nishat, Md. Rafsun Jani, Kazi Md. Shorowordi, Joaquin O. Carbonara, Saquib Ahmed

    Read original paper ↗ ·

  38. Applications of machine learning in computational nanotechnology

    Nanotechnology · 2021-12-29

    Wenxiang Liu, Yongqiang Wu, Yang Hong, Zhongtao Zhang, Yanan Yue, Jingchao Zhang

    Read original paper ↗ ·

  39. An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites

    ACS Applied Materials & Interfaces · 2021-12-30

    Zhilong Wang, Yanqiang Han, Xirong Lin, Junfei Cai, Sicheng Wu, Jinjin Li

    Read original paper ↗ ·

  40. Combined merits of high dielectric breakdown strength and low sintering temperature acquired in MgO-based dielectric ceramics: from theoretical prediction to experimental validation

    Acta Materialia · 2021-12-30

    Chi Zhang, Jian Yong Huang, Ying Chen, Ziming Cai, Genshui Wang, Xianlin Dong

    Read original paper ↗ ·

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

    Energy Materials · 2022-01-01

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

    Read original paper ↗ ·

  42. Algorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Nguyen Thanh Huy, Pham Quang Minh, Le Thi Bich

    Read original paper ↗ ·

  43. Intelligent control of nanoparticle synthesis through machine learning

    Nanoscale · 2022-01-01

    Honglin Lv, Xueye Chen

    Read original paper ↗ ·

  44. Machine learning for design principles for single atom catalysts towards electrochemical reactions

    Journal of Materials Chemistry A · 2022-01-01

    Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, Patrick Ryan Galligan, Hoilun Wong, Zhenjing Liu, Hongwei Liu, Yuting Cai, William A. Goddard, Zhengtang Luo

    Read original paper ↗ ·

  45. Machine learning-driven advanced development of carbon-based luminescent nanomaterials

    Journal of Materials Chemistry C · 2022-01-01

    Diva Addini Maghribi Muyassiroh, Fitri Aulia Permatasari, Ferry Iskandar

    Read original paper ↗ ·

  46. Machine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength

    Materials Advances · 2022-01-01

    Jianbo Li, Yuzhong Peng, Lupeng Zhao, Guodong Chen, Li Zeng, Guoqiang Wei, Yanhua Xu

    Read original paper ↗ ·

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

    Physical Chemistry Chemical Physics · 2022-01-01

    Eunsong Kim, Joonchul Kim, Kyoungmin Min

    Read original paper ↗ ·

  48. Prediction without Transferability: Domain Shift in Cross-Material AI Inference

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres

    Read original paper ↗ ·

  49. RbSnX 3 (X = Cl, Br, I): promising lead-free metal halide perovskites for photovoltaics and optoelectronics

    RSC Advances · 2022-01-01

    Md. Habibur Rahman, Md Jubair, Md. Zahidur Rahaman, Mohammad Shamim Ahasan, Kostya Ostrikov, M. Roknuzzaman

    Read original paper ↗ ·

  50. Recent progress in the data-driven discovery of novel photovoltaic materials

    Journal of Materials Informatics · 2022-01-01

    Lu Tian, Minjie Li, Wencong Lu, Tong‐Yi Zhang

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