Descriptor based adsorption-energy prediction on nano-clusters for catalyst selection

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Jäger, Marc Ranawat, Yashasvi 2018-11-13T13:36:28Z 2018-11-13T13:36:28Z 2018-11-06
dc.description.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. en
dc.format.extent 6+51
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Descriptor based adsorption-energy prediction on nano-clusters for catalyst selection en
dc.type G2 Pro gradu, diplomityö fi Perustieteiden korkeakoulu fi
dc.subject.keyword catalyst en
dc.subject.keyword descriptor en
dc.subject.keyword energy prediction en
dc.subject.keyword machine learning en
dc.subject.keyword material science en
dc.identifier.urn URN:NBN:fi:aalto-201811135764
dc.programme.major Physics of Advanced Materials fi
dc.programme.mcode SCI3057 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Foster, Adam
dc.programme Master’s Progamme in Engineering Physics fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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