Predicting and Optimizing Nanomaterial Synthesis Outcomes While Modelling Defects Using AI and ML
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866901343718866944 |
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| author | Jaiswal, Lakshya |
| author_facet | Jaiswal, Lakshya |
| contents | <p><span>With regard to their distinctive properties and versatility, nanomaterials have attracted considerable interest in catalysis, energy storage, and biomedical engineering. The intrinsic or extrinsic defects will determine the final properties and performances of these materials. Traditional methods of synthesis may be effective, but they generally lack the desired precision to control defects, causing problems in terms of reproducibility and scalability. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has proven to be a disruptive approach toward predictive modelling and optimization of the synthesis of nanomaterials. This article will discuss the synergistic potential that may exist in AI/ML in understanding defect formation and predicting material properties while improving synthesis methodologies. Advanced computational tools, case studies, and applications help articulate the importance of interdisciplinary strategies for defect-aware nanomaterial design. This integration of data-driven intelligence and traditional techniques improves the performance of the material while also opening doors for innovative solutions that can satisfy the demands of modern technology.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14583748 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predicting and Optimizing Nanomaterial Synthesis Outcomes While Modelling Defects Using AI and ML Jaiswal, Lakshya Nanoparticles Synthesis Optimization AI ML ICME <p><span>With regard to their distinctive properties and versatility, nanomaterials have attracted considerable interest in catalysis, energy storage, and biomedical engineering. The intrinsic or extrinsic defects will determine the final properties and performances of these materials. Traditional methods of synthesis may be effective, but they generally lack the desired precision to control defects, causing problems in terms of reproducibility and scalability. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has proven to be a disruptive approach toward predictive modelling and optimization of the synthesis of nanomaterials. This article will discuss the synergistic potential that may exist in AI/ML in understanding defect formation and predicting material properties while improving synthesis methodologies. Advanced computational tools, case studies, and applications help articulate the importance of interdisciplinary strategies for defect-aware nanomaterial design. This integration of data-driven intelligence and traditional techniques improves the performance of the material while also opening doors for innovative solutions that can satisfy the demands of modern technology.</span></p> |
| title | Predicting and Optimizing Nanomaterial Synthesis Outcomes While Modelling Defects Using AI and ML |
| topic | Nanoparticles Synthesis Optimization AI ML ICME |
| url | https://doi.org/10.5281/zenodo.14583748 |