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High Throughput Computational Materials Discovery

Description:

The field of materials discovery is undergoing an unprecedented transition from laboratory tocomputer. Behind this transition is the new ability to accurately compute material properties, especially energetic stability, from first principles with density functional theory (DFT). However, DFT remains computationally expensive, and DFT-based materials discovery is intractable, especially in high...

Keyword:

High throughput, Inorganic compounds, Big data, Density functional theory, Materials discovery, and Machine learning

Subject:

Materials Science, Condensed matter physics, and Computational chemistry

Creator:

Griesemer, Sean Darius

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16606 and etdadmin_upload_986816
Materials Discovery for Water Splitting Applications Using First-Principles Calculations and Machine Learning

Description:

Moving away from fossil fuels requires environmentally friendly and economically viable alternative energy sources. A wide adoption of new technologies for energy production and storage depends on better performing materials. Computational methods, such as electronic structure calculations and machine learning, hold the promise to work in conjunction with traditional experimentation...

Keyword:

Perovskites, Hydrogen production, Water splitting, Density functional theory, Materials discovery, and Machine learning

Subject:

Materials Science and Engineering

Creator:

Antoine Alexandre Emery

Owner:

Scholarly Digital Publishing

Date Uploaded:

10/22/2018

Date Modified:

10/22/2018

Date Created:

2017-01-01

Resource Type:

Dissertation

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Materials Discovery for Water Splitting Applications Using First-Principles Calculations and Machine Learning

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