EMPLOYING MACHINE LEARNING POTENTIAL TO ELUCIDATE NON-STOICHIOMETRIC FES STRUCTURE
Iron (II) sulfide (FeS) is of interest due to their potential usage in a variety of industries, including energy storage, catalysis, and environmental remediation. FeS is a Metal-Insulator Transition (MIT) material in which its electronic properties can be modified as initial temperature treatmen...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/71731 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Iron (II) sulfide (FeS) is of interest due to their potential usage in a variety of
industries, including energy storage, catalysis, and environmental remediation. FeS
is a Metal-Insulator Transition (MIT) material in which its electronic properties can
be modified as initial temperature treatment. FeS has P62c space group at below
400 K and P63/mmc at above 590 K. Nevertheless, since a stoichiometric ratio is
challenging to attain, multistep methods are needed to synthesize FeS. In this study,
we employ artificial neural network (ANN) based on Behler-Parinello approach to
investigate the phase changes of FeS at 300 – 600 K. Currently, we try to elucidate
the best multilayer perceptron (MLP) ANN structure to construct adequate atomic
interaction potential of FeS. The potentials are constructed from more than 4000
database structures, which include pristine and defected structures. We obtain that
2 hidden layers MLP with 20 nodes in each show fine training and testing set error.
We do force minimization of particular FeS structure using ANN atomic potential.
We obtain excellent results as no overlapping Fe-S bond length in the final
structure. The resulting structure in the finite temperature simulation shows a
changed structure but remains within reasonable limits. |
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