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@article{192085,
        author = {Dr. Ritesh Kumar},
        title = {Accelerating 2D Device Discovery via Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {193-198},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192085},
        abstract = {Two-dimensional materials are about to change the world of next-generation electronics by making it possible to make smaller, more energy-efficient devices with new features. Because they are so thin and have great electrical, mechanical, and optical properties, they are perfect for making ultra-scaled transistors, as well as neuromorphic and quantum technologies. But getting these materials ready for production is hard because there are so many different factors that go into making and combining them. This problem can be solved very well with machine learning. ML speeds up the development cycle by finding hidden patterns in large experimental datasets and automating high-throughput testing. This paper examines that pivotal intersection, elucidating how machine learning improves material characterisation, refines growth processes to regulate morphology, and optimises fabrication parameters for enhanced device performance.
#Machine #Learning #2D #ML #Quantum #Technology},
        keywords = {},
        month = {January},
        }