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1,678 claims from 1,032 papers are on the record. 46 have been checked so far; the other 1,632 have no check with a result yet.

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Status: Unchecked ×Keyword: property prediction ×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

   Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

   Agrawal and Choudhary · APL Materials · 2016

   A perspective article describing how data-driven techniques help reveal links between how materials are made, their structure and their properties, with examples of predicting properties and discovering materials.

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   1. UncheckedThe paper states that data analytics can cut the time to gain insight and speed up low-cost discovery of new materials, the aim of the Materials Genome Initiative.“Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.”