Category:Forces: Difference between revisions
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=== Introduction === | === Introduction === | ||
Forces on particles are a fundamental concept in condensed matter physics and chemistry. These forces describe the interactions that cause particles, such as atoms and molecules, to move and behave in specific ways. In VASP forces result from electromagnetic interactions which can be computed from DFT by the use of the [[Hellmann-Feynman forces|Hellmann-Feynman theorem]], the [[ACFDT/RPA calculations|random-phase approximation]] or by the use of [[Machine learning force field: Theory|machine learning force fields]]. Understanding these forces is crucial for predicting | Forces on particles are a fundamental concept in condensed matter physics and chemistry. These forces describe the interactions that cause particles, such as atoms and molecules, to move and behave in specific ways. In VASP forces result from electromagnetic interactions which can be computed from DFT by the use of the [[Hellmann-Feynman forces|Hellmann-Feynman theorem]], the [[ACFDT/RPA calculations|random-phase approximation]] or by the use of [[Machine learning force field: Theory|machine learning force fields]]. Understanding these forces is crucial in many aspects, as for example: | ||
* predicting the atomic structure of solids and molecules | |||
* to engineer and design new materials | |||
* predicting and optimizing chemical reactions | |||
* improving and understanding catalysis | |||
* predicting and understanding thermodynamic proerties | |||
Formally forces can be obtained in the following way | |||
=== Theory === | === Theory === | ||
Revision as of 09:58, 18 October 2023
Introduction
Forces on particles are a fundamental concept in condensed matter physics and chemistry. These forces describe the interactions that cause particles, such as atoms and molecules, to move and behave in specific ways. In VASP forces result from electromagnetic interactions which can be computed from DFT by the use of the Hellmann-Feynman theorem, the random-phase approximation or by the use of machine learning force fields. Understanding these forces is crucial in many aspects, as for example:
- predicting the atomic structure of solids and molecules
- to engineer and design new materials
- predicting and optimizing chemical reactions
- improving and understanding catalysis
- predicting and understanding thermodynamic proerties
Formally forces can be obtained in the following way
Theory
How To
Pages in category "Forces"
The following 8 pages are in this category, out of 8 total.