Machine learning driven prediction of lattice constants in transition metal dichalcogenides

Aug 4, 2026

Machine learning serves as an emerging branch of artificial intelligence that strengthens algorithms by leveraging data and accumulated research knowledge. In materials science, AI is increasingly required to accelerate the discovery of high-performance materials at scale. This report applies a gradient boosting regression tree model to predict lattice constants for cubic and trigonal MX2 systems, compares predicted and theoretical/experimental values, and evaluates errors using RMSE and MAE. Input features include ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density, and oxidation states, with contribution visualized via a bar plot to clarify parameter influence.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

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Article information

1 Introduction

1.1 Process of Machine Learning

1.2 Density Functional Theory to Machine Learning

BIBECHANA Vol. 20, No. 3, December 2023, 267-274 ISSN 2091-0762 (Print), 2382-5340 (Online) Journal homepage: [http://nepjol.info/index.php/BIBECHANA](http://nepjol.info/index.php/BIBECHANA)[ ](http://nepjol.info/index.php/BIBECHANA)Publisher: Dept. of Phys., Mahendra Morang A. M. Campus (Tribhuvan University) Biratnagar Machine learning driven prediction of lattice constantsin transition metal dichalcogenides Bhupendra Sharma 1 , Laxman Chaudhary 1 , Rajendra Adhikari2 Madhav Prasad Ghimire 1 ,∗ 1 Central Department of Physics, Tribhuvan University, Kirtipur, 44613, Kathmandu, Nepal. 2 Department of Physics, Kathmandu University, Dhulikhel, Kavre, Nepal. ∗ Corresponding author. Email: [madhav. ghimire@cdp. tu. edu. np](madhav. ghimire@cdp. tu. edu. np) Abstract Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms) . The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error) . The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported. Keywords Machine learning, Artificial Intelligence, Gradient Boosting Regression, Gradient Descent, RMSE, MAE. Article information Manuscript received: August 18, 2023; Accepted: September 23, 2023 DOI [https://doi.org/10.3126/bibechana.v20i3.57732](https://doi.org/10.3126/bibechana.v20i3.57732) This work is licensed under the Creative Commons CC BY-NC License. [https://creativecommons](https://creativecommons). org/licenses/by-nc/4.0/ 1 Introduction The crucial advancement of human history has been manifested by the development of new materials. So a vast amount of data has been collected throughout the centuries in the discipline of material science. The database of the previous experiments can be used to expedite innovations. The rise of artificial intelligence (AI) ushers a new genesis in the development of materials. AI works on the basis of complex multilayer neural networks with magnificent data mining ability. The fusion of material science and AI methods are used to find the complex relationship between different parameters, predict the particular properties of materials and improve the material characterization techniques. The most favorable branch of AI in material science is Machine learning (ML) [1] . Recently, ML is turning out into a robust approach to study the materials with extremely fast and economical means. It works on the basis of neural networks (NN) which performs the prediction of required variables on the basis of training data. The first principle calculation of materials could now be enhanced by ML. We specifically design ML models to generate predictions based on the correlations of the database through statistical and probabilistic methods [2] . On the basis of nature of data labeling in material science, ML can be divided into two categories: Supervised learning and Unsupervised learning. Supervised learning is a ML approach which

FAQs

What machine learning approach is used to predict lattice constants?

The study uses a gradient boosting regression tree model to predict lattice constants for cubic and trigonal MX2 systems.

How are prediction errors evaluated in the report?

Predicted values are compared with theoretical/experimental values, and standard errors are computed using RMSE and MAE.

Which features are used as inputs for the lattice-constant prediction?

The model uses ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density, and oxidation states as predictive features.

Aug 4, 2026

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