# Machine learning framework for derivation and optimization of Cz-YAG crystal growth recipe

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-framework-for-derivation-and-optimization-of-cz-yag-crystal/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Kunal Meshram,Milena Petković,Martin Holeňa,Natasha Dropka |
| Citations | 2 |
| DOI | 10.1016/j.jcrysgro.2025.128451 |
| Fields | Engineering,Materials Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1016/j.jcrysgro.2025.128451 |
| OpenAlex ID | https://openalex.org/W4417044192 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Kunal Meshram](https://scholariq.org/researchers/kunal-meshram/)

## Paper primary topic

- [Machine Learning in Materials Science](https://scholariq.org/topics/machine-learning-in-materials-science/)

## Paper topics

- [Machine Learning in Materials Science](https://scholariq.org/topics/machine-learning-in-materials-science/)
- [Luminescence Properties of Advanced Materials](https://scholariq.org/topics/luminescence-properties-of-advanced-materials/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.