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Towards Realizing Computationally Dr...

Carnegie Mellon University., Chemical Engineering.

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Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts // Brook Wander.
Author:	Wander, Brook,
Description:	1 electronic resource (148 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Chemical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31636077
ISBN:	9798346762744

Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts /
Wander, Brook,

Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts /Brook Wander. - 1 electronic resource (148 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

The vast amount of anthropogenic carbon dioxide emissions have pushed our planet into an era of climate instability. As we look to recast our chemical and energy infrastructures to mitigate further problems, there is a need to accelerate the discovery of novel catalysts. Catalysts enable the efficient transformation of energy and chemicals, so as we redesign the methods by which energy and chemicals are accessed, we also need to redesign the catalysts that support these processes. Today, the use of computational approaches to aid in the discovery of catalysts is primarily limited to explaining trends and phenomena observed experimentally. This is because there are many gaps between what is treated computationally and what is realized experimentally. Although large gaps still exist, over the course of this dissertation four projects are described which seek to make computationally driven experimental catalyst discovery a reality. Firstly, we present catlas, an open-source framework that automates the process of performing computational materials screenings using machine learning. Secondly, we present CatTSunami, which allows access to transition states at high accuracy with significantly reduced cost. Transition state energies determine the rate of reaction, which is paramount for catalysis. Next, we explored the usefulness of machine learned potentials for determining the potential energy Hessian. Hessians enable the determination of Gibbs free energies, which are of consequence when considering thermodynamic favorability and reaction rate. Finally, as the culmination of these efforts, we sought to build models capable of predicting experimental production rates using computational features for the electrochemical reduction of CO2 and the hydrogen evolution reaction, important green chemistries.

English

ISBN: 9798346762744Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Carbon dioxide

Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts /
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520 $a The vast amount of anthropogenic carbon dioxide emissions have pushed our planet into an era of climate instability. As we look to recast our chemical and energy infrastructures to mitigate further problems, there is a need to accelerate the discovery of novel catalysts. Catalysts enable the efficient transformation of energy and chemicals, so as we redesign the methods by which energy and chemicals are accessed, we also need to redesign the catalysts that support these processes. Today, the use of computational approaches to aid in the discovery of catalysts is primarily limited to explaining trends and phenomena observed experimentally. This is because there are many gaps between what is treated computationally and what is realized experimentally. Although large gaps still exist, over the course of this dissertation four projects are described which seek to make computationally driven experimental catalyst discovery a reality. Firstly, we present catlas, an open-source framework that automates the process of performing computational materials screenings using machine learning. Secondly, we present CatTSunami, which allows access to transition states at high accuracy with significantly reduced cost. Transition state energies determine the rate of reaction, which is paramount for catalysis. Next, we explored the usefulness of machine learned potentials for determining the potential energy Hessian. Hessians enable the determination of Gibbs free energies, which are of consequence when considering thermodynamic favorability and reaction rate. Finally, as the culmination of these efforts, we sought to build models capable of predicting experimental production rates using computational features for the electrochemical reduction of CO2 and the hydrogen evolution reaction, important green chemistries.
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653 $a Machine learning
653 $a Electrochemical reduction
653 $a Energy Hessian
653 $a Computational materials
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710 2 $a Carnegie Mellon University. $b Chemical Engineering. $e degree granting institution. $3 3817251
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