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:
Authors
- Théodoros Dimopoulos (ORCID: https://orcid.org/0000-0002-3620-9645)
- Selina Goetz (ORCID: https://orcid.org/0000-0003-4962-153X)
- Maximilian Wolf (ORCID: https://orcid.org/0000-0003-4917-7547)
- Georg K. H. Madsen (ORCID: https://orcid.org/0000-0001-9844-9145)
Institutions
- AIT Austrian Institute of Technology GmbH (AT)
- TU Wien (AT)
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