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1,321 claims from 825 papers are on the record. 46 have been checked so far; the other 1,275 have no check with a result yet.

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Keyword: ridge regression ×Clear all

4 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

   Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

   Schmidt, Shi, Borlido, Chen, Botti and Marques · Chemistry of Materials · 2017

   The authors built a dataset of about 250000 DFT-calculated cubic perovskites and benchmarked machine learning methods for predicting their thermodynamic stability.

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   1. UncheckedExtremely randomized trees gave the lowest average error, 121 meV/atom, in predicting distance to the convex hull for 230000 test perovskites after training on 20000.“We find that extremely randomized trees give the smallest mean absolute error of the distance to the convex hull (121 meV/atom) in the test set of 230000 perovskites, after being trained in 20000 samples.”
   2. UncheckedMachine learning could speed up high-throughput DFT screening of solids by at least five times by narrowing the compositions to calculate, without losing accuracy.“Our results suggest that machine learning can be used to speed up considerably (by at least a factor of 5) high-throughput DFT calculations, by restricting the space of relevant chemical compositions without degradation of the accuracy.”
   3. UncheckedDFT calculations on about 250,000 cubic perovskites found around 500 thermodynamically stable systems that are absent from crystal structure databases.“Incidentally, these calculations already reveal a large number of systems (around 500) that are thermodynamically stable but that are not present in crystal structure databases.”

2. Computer Science › Stochastic Gradient Optimization Techniques

   The generalization error of random features regression: Precise asymptotics and double descent curve

   Song and A · arXiv (Cornell University) · 2019

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   1. Unchecked“This provides the first analytically tractable model that captures all the features of the double descent phenomenon without assuming ad hoc misspecification structures.”