Descriptor based adsorption-energy prediction on nano-clusters for catalyst selection
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Journal Title
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Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2018-11-06
Department
Major/Subject
Physics of Advanced Materials
Mcode
SCI3057
Degree programme
Master’s Programme in Engineering Physics
Language
en
Pages
6+51
Series
Abstract
Nano-catalyst design, supplanting critical/rare metals with earth-abundant elements, for hydrogen evolution reactions (HERs) is a significant material-science and economic challenge. These design challenges can be significantly overcome by extensive first principle simulations. However, these simulations are computationally costly. With an introduction of descriptor, machine-learning methods afford significant advantages in such scenario. Descriptors: Smooth Overlap of Atomic Positions (SOAP) based on charge density, BLEACH, and Local Many- Body Tensor Representation (LMBTR) are proposed, and developed. These are evaluated on database of AuCu, and MoS2 nano-clusters. A learning error of 0.05 eV in adsorption energy, and 1.7 me in charge on hydrogen are realised for LMBTR; while 0.08 eV and 13.4 me, respectively for BLEACH on the AuCu dataset. Although not as accurate as state-of-the-art SOAP-lite (3.36 meV and 0.077 me, respectively), these descriptors have their own benefits. While LMBTR allows for bi-directional operability, BLEACH provides element-agnosticism; both of which missing are from SOAP.Description
Supervisor
Foster, AdamThesis advisor
Jäger, MarcKeywords
catalyst, descriptor, energy prediction, machine learning, material science