What question did this study set out to answer?

To develop a general statistical learning formalism that maps basic chemical attributes to diverse material properties using quantum mechanical reference data.

September 30, 2013Scientific ReportsOpen Access

Accelerating materials property predictions using machine learning

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Authors

GPGhanshyam PilaniaLos Alamos National Laboratory CWChenchen WangZhoukou Normal University XJXun JiangTongji University

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Implication

Computational study demonstrates rapid and accurate material property prediction in one-dimensional chain systems, highlighting machine learning for accelerated materials discovery.

Key Points

To develop a general statistical learning formalism that maps basic chemical attributes to diverse material properties using quantum mechanical reference data.
Trained machine learning and statistical learning models on quantum mechanical calculations using a model family of one-dimensional chain systems.
Formulated decision rules establishing mappings between easily accessible physical descriptors and complex material properties.
Constructed predictive fingerprints based on chemo-structural features (composition and atomic configuration) and electronic charge density distributions.
Decision rules successfully mapped accessible system attributes to diverse material properties.
Fingerprints derived from chemo-structural information and charge density distributions enabled ultra-fast and accurate property predictions.

Cite This Study

Pilania et al. (2013) studied this question.

synapsesocial.com/papers/69d6c579733a2b54c8aa830a https://doi.org/10.1038/srep02810

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Implication

Computational study demonstrates rapid and accurate material property prediction in one-dimensional chain systems, highlighting machine learning for accelerated materials discovery.

Accelerating materials property predictions using machine learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Accelerating materials property predictions using machine learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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