Predicting the band gap of ZnO quantum dots via supervised machine learning models

Scikit-learn: Machine Learning in Python

Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text

Computing, Computer Science, Artificial Intelligence +10

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Gavin C. Cawley, Nicola L. C. Talbot · 2010 · 2K citations

Computing, Data Science, Machine Learning +16

Machine learning in materials informatics: recent applications and prospects

Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania et al. · npj Computational Materials · 2017 · 1.8K citations · Full text

Natural Sciences, Materials Engineering, Materials Science +17

Mean Absolute Percentage Error for regression models

Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand et al. · Neurocomputing · 2016 · 1.4K citations · Full text

Business, Biostatistics, Psychometrics +13

Quantum-size effects of interacting electrons and holes in semiconductor microcrystals with spherical shape

Yōsuke Kayanuma · Physical review. B, Condensed matter · 1988 · 1.3K citations

Natural Sciences, Physics, Condensed Matter Physics +15