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1,634 claims from 1,009 papers are on the record. 46 have been checked so far; the other 1,588 have no check with a result yet.

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Keyword: materials informatics ×Clear all

5 claims from 4 papers

1. 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.”

2. Materials Science › Machine Learning in Materials Science

   Accelerated search for materials with targeted properties by adaptive design

   Xue, Balachandran, Hogden, Theiler, Xue and Lookman · Nature Communications · 2016

   The authors used an adaptive design strategy, coupled with experiments, to find NiTi-based shape memory alloys with very low thermal hysteresis, testing 36 compositions from about 800,000 possible ones.

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   1. UncheckedOf 36 NiTi-based alloy compositions the method predicted and the authors made, 14 had lower thermal hysteresis than any of the 22 starting alloys.“Of these, 14 had smaller Δ T than any of the 22 in the original data set.”

3. Materials Science › Machine Learning in Materials Science

   Combinatorial screening for new materials in unconstrained composition space with machine learning

   Meredig, Agrawal, Kirklin et al. · Physical Review B · 2014

   The authors trained a machine learning model on thousands of DFT calculations, used it to scan about 1.6 million ternary compositions, and predicted 4500 new stable materials.

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   1. UncheckedA machine learning model trained on DFT data can predict the thermodynamic stability of any composition, using about a millionth of the computer time DFT needs.“The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT.”
   2. UncheckedA machine learning model screened about 1.6 million candidate compositions and predicted 4500 new stable ternary compounds.“We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (${A}_{x}{B}_{y}{C}_{z}$), and predict 4500 new stable materials.”

4. Materials Science › Machine Learning in Materials Science

   ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

   Jha, Ward, Paul et al. · Scientific Reports · 2018

   The paper presents ElemNet, a deep neural network that predicts material properties from elemental composition alone, and uses it to screen a huge space of possible chemical systems for undiscovered compounds.

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   1. UncheckedA deep learning model can skip hand-designed, knowledge-based inputs and predict material properties better, even with only a few thousand training samples.“Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples.”