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Machine Learning for Atomistic Modeling: Representations and Thermal Transport

appsOther research productkeyboard_double_arrow_right Other ORP type 12 Sep 2023Publisher:Zenodo
Authors: Langer, Marcel F.;

doi: 10.5281/zenodo.7963151 , 10.5281/zenodo.7963152

Machine Learning for Atomistic Modeling: Representations and Thermal Transport

Abstract

This repository contains code and data for the dissertation "Machine Learning for Atomistic Modeling: Representations and Thermal Transport" by Marcel F. Langer. This repository is available at https://github.com/sirmarcel/doctor-archive. Selected versions are archived on Zenodo, under doi:10.5281/zenodo.7963152. The official version of the thesis can be found at doi:10.14279/depositonce-18647, the website for the thesis at marcel.science/doctor.

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Keywords

machine learning, materials science, automatic differentiation, molecular dynamics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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