Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917813004795904 |
|---|---|
| author | Price, Christopher C. Li, Yansong Zhou, Guanyu Younas, Rehan Zeng, Spencer S. Scanlon, Tim H. Munro, Jason M. Hinkle, Christopher L. |
| author_facet | Price, Christopher C. Li, Yansong Zhou, Guanyu Younas, Rehan Zeng, Spencer S. Scanlon, Tim H. Munro, Jason M. Hinkle, Christopher L. |
| contents | Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization Price, Christopher C. Li, Yansong Zhou, Guanyu Younas, Rehan Zeng, Spencer S. Scanlon, Tim H. Munro, Jason M. Hinkle, Christopher L. Materials Science Machine Learning Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis. |
| title | Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2409.08054 |