Enhancing the stability of organic photovoltaics through machine learning

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

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

Unsupervised word embeddings capture latent knowledge from materials science literature

(2019) Vahe Tshitoyan et al.   NATURE

Virtual Issue on Machine-Learning Discoveries in Materials Science

(2019) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

Using Large Datasets of Organic Photovoltaic Performance Data to Elucidate Trends in Reliability Between 2009 and 2019

(2019) Tudur Wyn David et al.   IEEE Journal of Photovoltaics

Machine learning analysis on stability of perovskite solar cells

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

Perovskite Solar Cells: Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells (Adv. Energy Mater. 46/2019)

(2019) Jinxin Li et al.   Advanced Energy Materials

How to Make over 20% Efficient Perovskite Solar Cells in Regular (n–i–p) and Inverted (p–i–n) Architectures

(2018) Michael Saliba et al.   CHEMISTRY OF MATERIALS

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

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

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

(2018) Çağla Odabaşı et al.   Nano Energy

Using ISOS consensus test protocols for development of quantitative life test models in ageing of organic solar cells

(2017) J. Kettle et al.   SOLAR ENERGY MATERIALS AND SOLAR CELLS

Effective use of Pearson's product–moment correlation coefficient

(2014) Marie-Therese Puth et al.   ANIMAL BEHAVIOUR

Nearest neighbor selection for iteratively kNN imputation

(2012) Shichao Zhang   JOURNAL OF SYSTEMS AND SOFTWARE

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING