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Machine Learning for Material Property Prediction and Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning for Material Property Prediction and Analysis./
Author:	Magar, Rishikesh.
Description:	1 online resource (153 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Mechanical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380092876

Machine Learning for Material Property Prediction and Analysis.
Magar, Rishikesh.

Machine Learning for Material Property Prediction and Analysis. - 1 online resource (153 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

Includes bibliographical references

The discovery of novel materials can significantly impact the quality of human life on the planet and make it more sustainable. Furthermore, novel materials can play a crucial role in technological advancements and enable innovations in industries such as healthcare and energy. However, the quest for new material discovery involves parsing the vast chemical space of materials. Navigating this vast chemical landscape to identify and synthesize materials with desirable properties is a big challenge. Considering the large scale of chemical space, developing advanced computational methods that can access this space effectively is very important. Computational methods that can accurately predict material properties can especially be leveraged for new material development. Accurate predictions can lead to the discovery of new materials with desirable properties for specific applications and reduce the time and cost associated with new material development. This dissertation investigates the potential of leveraging machine learning (ML) models as a more efficient computational tool for material property prediction and discovery.Traditionally, computational methods like Density Functional Theory (DFT) simulations have been used for high throughput screening and rapid prediction of material properties. Advancements in computational capabilities have enabled the large-scale usage of DFT simulations, yet parsing the entire chemical space and predicting the properties for all the possible materials is practically impossible due to the computational demands of these methods. Data-driven methods such as Machine Learning(ML) can learn patterns in the data and can be a viable alternative for DFT simulations. In general, ML models learn the material representations which can be leveraged to predict material properties at a fractional computational cost compared to DFT. Consequently, these models can be used as a screening tool to identify promising novel materials in the enormous chemical space which can then be further validated through experiments or DFT simulations.This dissertation explores various ML paradigms for material property prediction, encompassing Graph Neural Networks, Self-Supervised Representation Learning, Pretraining Strategies for Structure Agnostic Representation Learning, and Large Language Models. The primary aim of this work is to demonstrate the potential of ML models in accurate material property prediction and, as a result, catalyze novel material discovery through the application of machine learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380092876Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Machine LearningIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning for Material Property Prediction and Analysis.
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520 $a The discovery of novel materials can significantly impact the quality of human life on the planet and make it more sustainable. Furthermore, novel materials can play a crucial role in technological advancements and enable innovations in industries such as healthcare and energy. However, the quest for new material discovery involves parsing the vast chemical space of materials. Navigating this vast chemical landscape to identify and synthesize materials with desirable properties is a big challenge. Considering the large scale of chemical space, developing advanced computational methods that can access this space effectively is very important. Computational methods that can accurately predict material properties can especially be leveraged for new material development. Accurate predictions can lead to the discovery of new materials with desirable properties for specific applications and reduce the time and cost associated with new material development. This dissertation investigates the potential of leveraging machine learning (ML) models as a more efficient computational tool for material property prediction and discovery.Traditionally, computational methods like Density Functional Theory (DFT) simulations have been used for high throughput screening and rapid prediction of material properties. Advancements in computational capabilities have enabled the large-scale usage of DFT simulations, yet parsing the entire chemical space and predicting the properties for all the possible materials is practically impossible due to the computational demands of these methods. Data-driven methods such as Machine Learning(ML) can learn patterns in the data and can be a viable alternative for DFT simulations. In general, ML models learn the material representations which can be leveraged to predict material properties at a fractional computational cost compared to DFT. Consequently, these models can be used as a screening tool to identify promising novel materials in the enormous chemical space which can then be further validated through experiments or DFT simulations.This dissertation explores various ML paradigms for material property prediction, encompassing Graph Neural Networks, Self-Supervised Representation Learning, Pretraining Strategies for Structure Agnostic Representation Learning, and Large Language Models. The primary aim of this work is to demonstrate the potential of ML models in accurate material property prediction and, as a result, catalyze novel material discovery through the application of machine learning.
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