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Predicting material properties from chemical formulas

Paper Authors:

Si-Da Xue,

Qi-Jun Hong

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Key Details

MAPP framework predicts material properties from only chemical formulas

Uses graph neural networks to represent materials for property prediction

Models trained on large datasets can rapidly predict properties

Public web application allows open access to material property predictions

Framework aims to accelerate materials discovery and design

AI generated summary

Predicting material properties from chemical formulas

This paper introduces a machine learning framework called MAPP that can predict key properties of materials based only on their chemical formulas. It uses graph neural networks to represent materials as graphs with elements as nodes. Training on large datasets, the models can rapidly predict properties like melting point and bulk modulus for any material formula. The framework is publicly accessible, allowing material scientists to leverage AI for accelerated discovery.

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