A universal machine learning model for the electronic density of states

How Wei Bin; Pol Febrer; Sanggyu Chong; Arslan Mazitov; Filippo Bigi; Matthias Kellner; Sergey Pozdnyakov; Michele Ceriotti

doi:10.1039/d5dd00557d

January 1, 2025Digital DiscoveryOpen Access

A universal machine learning model for the electronic density of states

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Key Points

The aim is to develop a universal machine learning model to accurately predict the electronic density of states in various atomic configurations.
Utilized machine learning techniques to create a model for predicting interatomic potentials.
Focused on energy and force predictions across different compositions and structures.
Analyzed model accuracy by comparing predicted values with experimental data.
Achieved high accuracy in predicting electronic properties of diverse atomic configurations.
Demonstrated the model's effectiveness across various material compositions.
Revealed significant improvements in predictive capabilities compared to traditional methods.

Implication

Machine learning predicts electronic density of states, indicating improved accuracy for atomic configurations.

Abstract

In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy...

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Cite This Study

Bin et al. (2025) studied this question.

synapsesocial.com/papers/69aa7066531e4c4a9ff5a2dd https://doi.org/10.1039/d5dd00557d

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A universal machine learning model for the electronic density of states

Key Points

Implication

Abstract

Discussion

Cite This Study

A universal machine learning model for the electronic density of states

Key Points

Implication

Abstract

Discussion

Cite This Study

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