Overview of the VASP Software and Its Applications in Materials Research

The rapid advancement of computational materials science has significantly accelerated the discovery and design of advanced materials at the atomicscale. Among the various first-principles simulation packages, the Vienna Ab initio Simulation Package (VASP) has become one of the most powerful and widely used computational tools in modern materials research. This review presents a comprehensive overview of the theoretical foundations, main functionalities, and applications of VASP in computational materials science. The fundamental principles underlying VASP, including Density Functional Theory (DFT), plane-wave basis sets, and the Projector Augmented Wave (PAW) method, are discussed in detail. In addition, the major computational capabilities of VASP, such as geometry optimization, electronic structure calculations, magnetic property analysis, optical property simulations, and ab initio molecular dynamics (AIMD), are systematically summarized. Particular attention is devoted to the applications of VASP in the investigation of two-dimensional materials, semiconductors, catalytic systems,gas adsorption, and energy-related materials, including lithium-ion batteries, photocatalysts, and perovskite solarcells. The advantages and current limitations of VASP are also analyzed, together with recent development trends involving machine learning, high-throughput calculations, workflow automation, and AI-assisted materials discovery. Owing to its high computational accuracy, scalability on high-performance computing platforms, and extensive methodological support, VASP continues to play a crucial role in advancing modern computational materials research and the development of next-generation functional materials.

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-29
DOI
https://doi.org/10.5281/zenodo.20444324
Primary Topic
Machine Learning in Materials Science
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article
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article

Overview of the VASP Software and Its Applications in Materials Research

Tung Nguyen Thanh
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Overview of the VASP Software and Its Applications in Materials Research

Tung Nguyen Thanh
article en

Abstract

The rapid advancement of computational materials science has significantly accelerated the discovery and design of advanced materials at the atomicscale. Among the various first-principles simulation packages, the Vienna Ab initio Simulation Package (VASP) has become one of the most powerful and widely used computational tools in modern materials research. This review presents a comprehensive overview of the theoretical foundations, main functionalities, and applications of VASP in computational materials science. The fundamental principles underlying VASP, including Density Functional Theory (DFT), plane-wave basis sets, and the Projector Augmented Wave (PAW) method, are discussed in detail. In addition, the major computational capabilities of VASP, such as geometry optimization, electronic structure calculations, magnetic property analysis, optical property simulations, and ab initio molecular dynamics (AIMD), are systematically summarized. Particular attention is devoted to the applications of VASP in the investigation of two-dimensional materials, semiconductors, catalytic systems,gas adsorption, and energy-related materials, including lithium-ion batteries, photocatalysts, and perovskite solarcells. The advantages and current limitations of VASP are also analyzed, together with recent development trends involving machine learning, high-throughput calculations, workflow automation, and AI-assisted materials discovery. Owing to its high computational accuracy, scalability on high-performance computing platforms, and extensive methodological support, VASP continues to play a crucial role in advancing modern computational materials research and the development of next-generation functional materials.

Zenodo (CERN European Organization for Nuclear Research)
Ho Chi Minh City University of Technology (VN)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Machine Learning in Materials Science
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Overview of the VASP Software and Its Applications in Materials Research — Tung Nguyen Thanh · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS