# Machine learning speeds up prediction of materials' spectral properties

**URL:** https://community.materialssquare.com/t/machine-learning-speeds-up-prediction-of-materials-spectral-properties/1087
**Category:** News
**Created:** [December 27, 2024, 11:48am UTC](https://community.materialssquare.com/t/machine-learning-speeds-up-prediction-of-materials-spectral-properties/1087 "2024-12-27T11:48:58Z")
**Posts on this page:** 1
**Page:** 1

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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: [December 27, 2024, 11:48am UTC](https://community.materialssquare.com/t/machine-learning-speeds-up-prediction-of-materials-spectral-properties/1087/1 "2024-12-27T11:48:58Z")

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This article highlights a breakthrough in computational materials science, where machine learning significantly accelerates the prediction of materials’ spectral properties. Using Koopmans functionals, which enhance density-functional theory (DFT) to predict light absorption and other spectral characteristics, researchers often face challenges in calculating complex “screening parameters.” These parameters indicate how electrons in a material respond to adding or removing an electron, critical for applications like solar cells. In a study involving liquid water and the halide perovskite CsSnI3, researchers demonstrated that a simple machine learning model, ridge regression, could predict these parameters accurately and efficiently, reducing computational costs. Despite its simplicity, the success was attributed to the careful design of descriptors capturing the system’s physics. This approach opens the door to faster, more efficient exploration of temperature-dependent spectral properties in materials.

For more details, please continue reading the full article under the following link:

> **[Machine learning speeds up prediction of materials' spectral properties](https://phys.org/news/2024-12-machine-materials-spectral-properties.html)**
>
> Many techniques in computational materials science require scientists to identify the right set of parameters that capture the physics of the specific material they are studying. Calculating these parameters from scratch is sometimes possible but...

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In general, if you enjoy reading this kind of scientific news articles, I am always keen to connect with fellow researchers in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( [www.matsq.com](http://www.matsq.com) ), designed for streamlining the execution of materials and molecular modelling simulations!

The Materials Square platform in fact provides extensive web-based functionalities for computational chemistry/materials research and training/education purposes, as detailed also in the following PDF brochure: [https://www.materialssquare.com/wp-content/uploads/Materials\_Square\_Brochure\_2022-compressed\_1674181754.pdf](https://www.materialssquare.com/wp-content/uploads/Materials_Square_Brochure_2022-compressed_1674181754.pdf)

Many thanks for your interest and consideration,

Dr. Gabriele Mogni  
Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform  
Website: [Home | Virtual Lab Inc.](http://www.virtuallab.co.kr/en/)  
Email: gabriele@simulation.re.kr

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