Synthesizability indexing of machine learning algorithm suggested novel compounds

The creation of new materials is a critical aspect of scientific research and development, as it plays a significant role in improving the quality of life for people all over the world. From advanced medical technologies to the development of sustainable energy sources, new materials have a va...

Full description

Saved in:
Bibliographic Details
Main Author: Koh, Joel Bo Jun
Other Authors: Kedar Hippalgaonkar
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166928
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-166928
record_format dspace
spelling sg-ntu-dr.10356-1669282023-05-20T16:46:11Z Synthesizability indexing of machine learning algorithm suggested novel compounds Koh, Joel Bo Jun Kedar Hippalgaonkar School of Materials Science and Engineering kedar@ntu.edu.sg Engineering::Materials The creation of new materials is a critical aspect of scientific research and development, as it plays a significant role in improving the quality of life for people all over the world. From advanced medical technologies to the development of sustainable energy sources, new materials have a vast range of applications that have the potential to revolutionize multiple industries. New materials are essential for technological advancements, as they are often designed to be stronger, more durable, and more versatile than existing materials. For example, carbon fiber composites have replaced metal alloys in the construction of high-performance sports cars and aerospace components, resulting in lighter and more fuel-efficient vehicles [1]. Similarly, the development of new materials such as perovskite solar cells has led to significant improvements in solar panel efficiency, making renewable energy more cost-effective and accessible. Furthermore, the creation of new materials can also address specific challenges facing society, such as climate change and disease prevention. For example, researchers are currently developing nanomaterials that can selectively remove carbon dioxide from the atmosphere, potentially mitigating the effects of global warming [2]. In the field of medicine, researchers are exploring the use of biomaterials to develop advanced drug delivery systems that can more effectively target specific diseases and reduce the risk of side effects [3]. While significant progress has been made in the development of new materials, there is a need for continuous innovation to address emerging challenges and improve existing technologies. As technologies evolve and become more complex, the materials used to create them must also become more advanced to meet the demands of these new applications. For example, the emergence of electric vehicles has created a need for new materials with high energy storage capacities to develop better and more efficient batteries (Zhang et al., 2020). 3 Furthermore, materials science is a rapidly evolving field, with new discoveries and advancements being made all the time. Continuously creating new materials allows researchers to explore new possibilities and overcome limitations that may have previously hindered progress. Additionally, advancements in materials science can often have significant economic benefits, as new materials can open up new markets and create new job opportunities [4]. The purpose of this report is to document the research, development, and evaluation of a machine learning-based synthesizability indexing system for 3-element compounds. The project aims to explore the correlation between the physical and chemical properties of the constituent elements in 3-element compounds and their ability to be synthesized. The report will discuss the use of machine learning techniques and formulas to create a model that accurately predicts the synthesizability of 3-element compounds based on the properties of the constituent elements Bachelor of Engineering (Materials Engineering) 2023-05-18T12:42:19Z 2023-05-18T12:42:19Z 2023 Final Year Project (FYP) Koh, J. B. J. (2023). Synthesizability indexing of machine learning algorithm suggested novel compounds. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166928 https://hdl.handle.net/10356/166928 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Materials
spellingShingle Engineering::Materials
Koh, Joel Bo Jun
Synthesizability indexing of machine learning algorithm suggested novel compounds
description The creation of new materials is a critical aspect of scientific research and development, as it plays a significant role in improving the quality of life for people all over the world. From advanced medical technologies to the development of sustainable energy sources, new materials have a vast range of applications that have the potential to revolutionize multiple industries. New materials are essential for technological advancements, as they are often designed to be stronger, more durable, and more versatile than existing materials. For example, carbon fiber composites have replaced metal alloys in the construction of high-performance sports cars and aerospace components, resulting in lighter and more fuel-efficient vehicles [1]. Similarly, the development of new materials such as perovskite solar cells has led to significant improvements in solar panel efficiency, making renewable energy more cost-effective and accessible. Furthermore, the creation of new materials can also address specific challenges facing society, such as climate change and disease prevention. For example, researchers are currently developing nanomaterials that can selectively remove carbon dioxide from the atmosphere, potentially mitigating the effects of global warming [2]. In the field of medicine, researchers are exploring the use of biomaterials to develop advanced drug delivery systems that can more effectively target specific diseases and reduce the risk of side effects [3]. While significant progress has been made in the development of new materials, there is a need for continuous innovation to address emerging challenges and improve existing technologies. As technologies evolve and become more complex, the materials used to create them must also become more advanced to meet the demands of these new applications. For example, the emergence of electric vehicles has created a need for new materials with high energy storage capacities to develop better and more efficient batteries (Zhang et al., 2020). 3 Furthermore, materials science is a rapidly evolving field, with new discoveries and advancements being made all the time. Continuously creating new materials allows researchers to explore new possibilities and overcome limitations that may have previously hindered progress. Additionally, advancements in materials science can often have significant economic benefits, as new materials can open up new markets and create new job opportunities [4]. The purpose of this report is to document the research, development, and evaluation of a machine learning-based synthesizability indexing system for 3-element compounds. The project aims to explore the correlation between the physical and chemical properties of the constituent elements in 3-element compounds and their ability to be synthesized. The report will discuss the use of machine learning techniques and formulas to create a model that accurately predicts the synthesizability of 3-element compounds based on the properties of the constituent elements
author2 Kedar Hippalgaonkar
author_facet Kedar Hippalgaonkar
Koh, Joel Bo Jun
format Final Year Project
author Koh, Joel Bo Jun
author_sort Koh, Joel Bo Jun
title Synthesizability indexing of machine learning algorithm suggested novel compounds
title_short Synthesizability indexing of machine learning algorithm suggested novel compounds
title_full Synthesizability indexing of machine learning algorithm suggested novel compounds
title_fullStr Synthesizability indexing of machine learning algorithm suggested novel compounds
title_full_unstemmed Synthesizability indexing of machine learning algorithm suggested novel compounds
title_sort synthesizability indexing of machine learning algorithm suggested novel compounds
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166928
_version_ 1772827422252597248