# Novel Machine Learning Model For Characterization Of Material Surfaces

**URL:** <https://community.materialssquare.com/t/novel-machine-learning-model-for-characterization-of-material-surfaces/120>\
**Category:** News\
**Created:** [April 12, 2024, 1:20pm UTC](https://community.materialssquare.com/t/novel-machine-learning-model-for-characterization-of-material-surfaces/120 "2024-04-12T13:20:18Z")\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [April 12, 2024, 1:20pm UTC](https://community.materialssquare.com/t/novel-machine-learning-model-for-characterization-of-material-surfaces/120/1 "2024-04-12T13:20:19Z")

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Researchers from Tokyo Institute of Technology have developed a novel machine learning model to predict the ionization potential and electron affinity of binary and ternary oxide surfaces. This model employs an artificial neural network that utilizes structural descriptors to perform its calculations efficiently and accurately, expanding the possibility of screening and developing materials with desired electronic properties for use in various technological applications. Please find the full article below:

> **[Novel Machine Learning Model For Characterization Of Material Surfaces](https://www.nationaltribune.com.au/novel-machine-learning-model-for-characterization-of-material-surfaces/)**
>
> Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as