COHESIVM: Combinatorial h+/e- Sample Investigation using Voltaic Measurements

Accelerating materials discovery and optimization is crucial for transitioning to sustainable energy conversion and storage.In this regard, materials acceleration platforms (MAPs) can significantly shorten the discovery process, cutting material and labor costs (Aspuru-Guzik & Persson, 2018).Combinatorial and high-throughput methods are instrumental in developing said MAPs, enabling autonomous operation and the generation of large datasets (Maier et al., 2007).Therefore, in a previous work, we developed combinatorial deposition and analysis techniques for the discovery of new semiconductor materials (Wolf et al., 2023).To drive further innovation in the field, COHESIVM was created, which facilitates combinatorial analysis of material and device properties through the following key features:

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Publication Details

Journal
The Journal of Open Source Software
Published
2025-02-05
DOI
https://doi.org/10.21105/joss.07291
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52
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article

COHESIVM: Combinatorial h+/e- Sample Investigation using Voltaic Measurements

Théodoros Dimopoulos, Selina Goetz, Maximilian Wolf, Georg K. H. Madsen
1 citations
The Journal of Open Source Software
Machine Learning in Materials Science
0.52
article

COHESIVM: Combinatorial h+/e- Sample Investigation using Voltaic Measurements

Théodoros Dimopoulos, Selina Goetz, Maximilian Wolf, Georg K. H. Madsen
article en
1 citations

Abstract

Accelerating materials discovery and optimization is crucial for transitioning to sustainable energy conversion and storage.In this regard, materials acceleration platforms (MAPs) can significantly shorten the discovery process, cutting material and labor costs (Aspuru-Guzik & Persson, 2018).Combinatorial and high-throughput methods are instrumental in developing said MAPs, enabling autonomous operation and the generation of large datasets (Maier et al., 2007).Therefore, in a previous work, we developed combinatorial deposition and analysis techniques for the discovery of new semiconductor materials (Wolf et al., 2023).To drive further innovation in the field, COHESIVM was created, which facilitates combinatorial analysis of material and device properties through the following key features:

The Journal of Open Source SoftwareVol. 10(106)
AIT Austrian Institute of Technology GmbH (AT), TU Wien (AT)
Affordable and clean energy
Openalex Percentile: Top 42%
Machine Learning in Materials Science
0.52
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COHESIVM: Combinatorial h+/e- Sample Investigation using Voltaic Measurements — Théodoros Dimopoulos, Selina Goetz, et al. · The Journal of Open Source Software (2025) | TGRS Research Map | TGRS