Critical review of machine learning applications in perovskite solar research

A Critical Review of Machine Learning of Energy Materials

(2020) Chi Chen et al.   Advanced Energy Materials

Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning

(2020) Çağla Odabaşı et al.   Energy Technology

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

(2020) Mark V. Khenkin et al.   Nature Energy

Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network

(2020) Wissam A. Saidi et al.   npj Computational Materials

Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines

(2019) Yongze Yu et al.   ACS Energy Letters

Identifying Pb-free perovskites for solar cells by machine learning

(2019) Jino Im et al.   npj Computational Materials

A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction

(2019) Roman Schmack et al.   Nature Communications

Comprehensive Computational Study of Partial Lead Substitution in Methylammonium Lead Bromide

(2019) Arun Mannodi-Kanakkithodi et al.   CHEMISTRY OF MATERIALS

Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High‐Efficiency Solar Cells

(2019) Ryan Jacobs et al.   ADVANCED FUNCTIONAL MATERIALS

Polyelemental, Multicomponent Perovskite Semiconductor Libraries through Combinatorial Screening

(2019) Michael Saliba   Advanced Energy Materials

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

(2019) Felipe Oviedo et al.   npj Computational Materials

Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

(2019) Kamal Choudhary et al.   CHEMISTRY OF MATERIALS

Learn and Match Molecular Cations for Perovskites

(2019) Heesoo Park et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Data mining new energy materials from structure databases

(2019) Lei Zhang et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Simulation and design of energy materials accelerated by machine learning

(2019) Hongshuai Wang et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Fast and Accurate Artificial Neural Network Potential Model for MAPbI3 Perovskite Materials

(2019) Hsin-An Chen et al.   ACS Omega

Atomistic structure learning

(2019) Mathias S. Jørgensen et al.   JOURNAL OF CHEMICAL PHYSICS

Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications

(2019) Florian Belviso et al.   INORGANIC CHEMISTRY

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning

(2019) Xing-Yu Ma et al.   Journal of Physical Chemistry Letters

Machine learning analysis on stability of perovskite solar cells

(2019) Çağla Odabaşı et al.   SOLAR ENERGY MATERIALS AND SOLAR CELLS

Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations

(2019) Tianmin Wu et al.   Nano Energy

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

(2019) Xiang Li et al.   Applied Sciences-Basel

Design and exploration of semiconductors from first principles: A review of recent advances

(2018) Fumiyasu Oba et al.   Applied Physics Express

Density Functional Theory - Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden-Popper Phases

(2018) Omar Allam et al.   CHEMPHYSCHEM

Critical analysis on the quality of stability studies of perovskite and dye solar cells

(2018) Armi Tiihonen et al.   Energy & Environmental Science

Solar PV output prediction from video streams using convolutional neural networks

(2018) Yuchi Sun et al.   Energy & Environmental Science

Extracting Knowledge from Data through Catalysis Informatics

(2018) Andrew J. Medford et al.   ACS Catalysis

Searching for Hidden Perovskite Materials for Photovoltaic Systems by Combining Data Science and First Principle Calculations

(2018) Keisuke Takahashi et al.   ACS Photonics

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

(2018) Kamal Choudhary et al.   Scientific Data

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

(2018) Shuaihua Lu et al.   Nature Communications

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

(2018) Asif J. Chowdhury et al.   Journal of Physical Chemistry C

Rationalizing Perovskite Data for Machine Learning and Materials Design

(2018) Qichen Xu et al.   Journal of Physical Chemistry Letters

PubChem 2019 update: improved access to chemical data

(2018) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

Addressing the stability issue of perovskite solar cells for commercial applications

(2018) Lei Meng et al.   Nature Communications

Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools

(2018) Çağla Odabaşı et al.   Nano Energy

Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production

(2018) Elif Can et al.   APPLIED CATALYSIS B-ENVIRONMENTAL

Discovery of Pb-Free Perovskite Solar Cells via High-Throughput Simulation on the K Computer

(2017) Takahito Nakajima et al.   Journal of Physical Chemistry Letters

Use machine learning to find energy materials 

(2017) Phil De Luna et al.   NATURE

Uncovering structure-property relationships of materials by subgroup discovery

(2017) Bryan R Goldsmith et al.   NEW JOURNAL OF PHYSICS

Using data mining technology in screening potential additives to Ni/Al2O3 catalysts for methanation

(2017) Xiaoxia Han et al.   Catalysis Science & Technology

High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites

(2017) Antoine A. Emery et al.   Scientific Data

A hybrid organic-inorganic perovskite dataset

(2017) Chiho Kim et al.   Scientific Data

Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges

(2017) Martín A. Mosquera et al.   ACS Energy Letters

Mesoscale Functional Imaging of Materials for Photovoltaics

(2017) Elizabeth M. Tennyson et al.   ACS Energy Letters

Computational materials design of crystalline solids

(2016) Keith T. Butler et al.   CHEMICAL SOCIETY REVIEWS

AiiDA: automated interactive infrastructure and database for computational science

(2016) Giovanni Pizzi et al.   COMPUTATIONAL MATERIALS SCIENCE

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

(2016) Sumyea Helal   JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

The FAIR Guiding Principles for scientific data management and stewardship

(2016) Mark D. Wilkinson et al.   Scientific Data

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Finding New Perovskite Halides via Machine Learning

(2016) Ghanshyam Pilania et al.   Frontiers in Materials

Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells

(2015) Abraham Yosipof et al.   Molecular Informatics

Big–deep–smart data in imaging for guiding materials design

(2015) Sergei V. Kalinin et al.   NATURE MATERIALS

PubChem Substance and Compound databases

(2015) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

The Computational Materials Repository

(2012) David D. Landis et al.   COMPUTING IN SCIENCE & ENGINEERING

Crystallography Open Database – an open-access collection of crystal structures

(2009) Saulius Gražulis et al.   JOURNAL OF APPLIED CRYSTALLOGRAPHY

Formability ofABX3(X= F, Cl, Br, I) halide perovskites

(2008) Chonghea Li et al.   ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE