Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72963
Title: Machine learning driven synthesis of two-dimensional materials
Authors: Chia, Jia Jun
Keywords: DRNTU::Engineering::Materials
Issue Date: 2017
Abstract: No humans are born perfect and as such people tend to make mistakes or have limited capabilities in certain aspect as compared to machines. And with the technological advances made, machine learning was created. With the aid of machine learning, certain tasks like finding algorithm or pattern and calculations were made faster to achieve as compared humans doing so. And in recent times, researches are being conducted for the use of artificial intelligence not only in other industries but also in the materials industry where machines could aid in the discovery of new materials, analysis of materials and even creation or production of materials. In this study, we aim to make use of machine learning, specifically the Decision Tree Learning approach to train on a set of data of Molybdenum Disulfide (MoS2) obtained from Chemical Vapor Deposition (CVD) to predict the optimum conditions for the growth of such material. Also, the possible future applications of machine learning, not only on two-dimensional materials in this study, but materials as a whole will be mentioned.
URI: http://hdl.handle.net/10356/72963
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MSE Student Reports (FYP/IA/PA/PI)

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