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1,545 claims from 958 papers are on the record. 46 have been checked so far; the other 1,499 have no check with a result yet.

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Status: Unchecked ×Keyword: amorphous materials ×Clear all

3 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

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

   Ward, Agrawal, Choudhary and Wolverton · npj Computational Materials · 2016

   The authors built a general machine learning framework for predicting properties of inorganic materials, and show it on crystalline and amorphous materials, including band gap energy and glass-forming ability.

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   1. UncheckedThe authors' method predicts material properties using a chemically diverse set of attributes plus a new way of grouping similar materials to improve accuracy.“Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials in order to boost the predictive accuracy.”
   2. UncheckedThe authors show their machine learning method can predict varied properties of crystalline and amorphous materials, including band gap energy and glass-forming ability.“In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.”

2. Materials Science › Machine Learning in Materials Science

   Atom-centered symmetry functions for constructing high-dimensional neural network potentials

   Behler · The Journal of Chemical Physics · 2011

   The paper discusses several types of atom-centred symmetry functions for building neural network potential-energy surfaces, using simple benchmark systems to examine their properties.

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   1. UncheckedThe paper states that its symmetry functions are general and can describe molecules, crystalline and amorphous solids, and liquids.“The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.”