Efficient screening framework for organic solar cells with deep learning and ensemble learning

Double Asymmetric Core Optimizes Crystal Packing to Enable Selenophene‐based Acceptor with Over 18 % Efficiency in Binary Organic Solar Cells

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SLI-GNN: A Self-Learning-Input Graph Neural Network for Predicting Crystal and Molecular Properties

(2023) Zhihao Dong et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency

(2022) Asif Mahmood et al.   Journal of Materials Chemistry A

High‐Efficiency Non‐Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry

(2022) Qi Zhang et al.   Advanced Science

Machine Learning for Organic Photovoltaic Polymers: A Minireview

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A Synergistic Strategy of Manipulating the Number of Selenophene Units and Dissymmetric Central Core of Small Molecular Acceptors Enables Polymer Solar Cells with 17.5 % Efficiency

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Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials

(2021) Wenbo Sun et al.   Journal of Physical Chemistry Letters

15.71% Efficiency All‐Small‐Molecule Organic Solar Cells Based on Low‐Cost Synthesized Donor Molecules

(2021) Jing Guo et al.   ADVANCED FUNCTIONAL MATERIALS

Developing Efficient Small Molecule Acceptors with sp 2 ‐Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning

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Non-fullerene acceptors with hetero-dihalogenated terminals induce significant difference in single crystallography and enable binary organic solar cells with 17.5% efficiency

(2021) Lai Wang et al.   Energy & Environmental Science

Design Principles and Synergistic Effects of Chlorination on a Conjugated Backbone for Efficient Organic Photovoltaics: A Critical Review

(2020) Gururaj P. Kini et al.   ADVANCED MATERIALS

A Critical Review of Machine Learning of Energy Materials

(2020) Chi Chen et al.   Advanced Energy Materials

Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface–Molecular Adsorbate Interactions

(2020) Xijun Wang et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Nature‐based solutions for urban pluvial flood risk management

(2020) Yijing Huang et al.   Wiley Interdisciplinary Reviews-Water

Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells

(2020) Yao Wu et al.   npj Computational Materials

Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells

(2020) Zhi-Wen Zhao et al.   CHEMISTRY OF MATERIALS

Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning

(2019) Daniele Padula et al.   Advanced Energy Materials

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning

(2019) Harikrishna Sahu et al.   Journal of Physical Chemistry Letters

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

(2019) Wenbo Sun et al.   Science Advances

A high throughput molecular screening for organic electronics via machine learning: present status and perspective

(2019) Akinori Saeki et al.   JAPANESE JOURNAL OF APPLIED PHYSICS

Organic solar cells based on non-fullerene acceptors

(2018) Jianhui Hou et al.   NATURE MATERIALS

Next-generation organic photovoltaics based on non-fullerene acceptors

(2018) Pei Cheng et al.   Nature Photonics

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

(2018) Harikrishna Sahu et al.   Advanced Energy Materials

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Trends in the electronic and geometric structure of non-fullerene based acceptors for organic solar cells

(2017) Alina Kuzmich et al.   Energy & Environmental Science

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Suppressing Subnanosecond Bimolecular Charge Recombination in a High-Performance Organic Photovoltaic Material

(2016) Kyra N. Schwarz et al.   Journal of Physical Chemistry C

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

(2016) Aki Vehtari et al.   STATISTICS AND COMPUTING

The Harvard organic photovoltaic dataset

(2016) Steven A. Lopez et al.   Scientific Data

Computational predictions of energy materials using density functional theory

(2016) Anubhav Jain et al.   Nature Reviews Materials

Small-molecule solar cells with efficiency over 9%

(2014) Qian Zhang et al.   Nature Photonics

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

(2013) Johannes Hachmann et al.   Energy & Environmental Science

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

(2011) Johannes Hachmann et al.   Journal of Physical Chemistry Letters

Solution-processed small-molecule solar cells with 6.7% efficiency

(2011) Yanming Sun et al.   NATURE MATERIALS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Estimation of prediction error by using K-fold cross-validation

(2009) Tadayoshi Fushiki   STATISTICS AND COMPUTING