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James Paolo Rili

Publications and source records attributed to James Paolo Rili.

2 recordsLinked to original sources

Characterization of Semiconducting Materials Using the Van der Pauw Method

Semiconductors are currently an active topic of study due to the endless range of applications in electronic hardware and computer engineering. In this experiment, the material properties (i.e. resistivity $ρ$, Hall coefficient $R_{H}$, and mobility $μ$) of a doped GaAs sheet is described by utilizing Hall Effect and the Van der Pauw method with varying temperature $T$ and magnetic field values $B$. It is determined that the sample is an $n$-type semiconductor using the sign of $R_H$, which is measured to be $R_H = -2.9 \times 10^{-12} \pm 0.1 \times 10^{-14} ~ \text{m}^{3} \text{C}^{-1}$, at $T = 303 ~\text{K}$ and $B = 3.3 ~\text{kGs}$. Furthermore, the rate of change for the slope $R_H$ and $T$ is increasing along $B$ at the rate of $Δk/ ΔB = \left(3.6 \pm 0.5 \right) \times 10^{-16} ~\text{m}^3 (\text{CK} \cdot \text{kGs})^{-1}$, meaning the charge accumulation caused by the current and Lorentz force is quadratic in $B$. It is also discovered that $μ$, and therefore the electron drift velocity is reduced proportionally at higher $T$-values. This method provides a potential analogue in quantum scales with the Quantum Hall Effect and characterisation of quantum dots.

cond-mat.mtrl-sci↗

Machine Learning Interatomic Potentials with Keras API

A neural network is used to train, predict, and evaluate a model to calculate the energies of 3-dimensional systems composed of Ti and O atoms. Python classes are implemented to quantify atomic interactions through symmetry functions and to specify prediction algorithms. The hyperparameters of the model are optimised by minimising validation RMSE, which then produced a model that is accurate to within 100 eV. The model could be improved by proper testing of symmetry function calculations and addressing properties of features and targets.

physics.comp-ph↗