learning-notes.md

layout	splash
title	Learning Notes
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Phonon Dynamics in Disordered Systems

View on GitHub

  <div class="project-details">
    <div class="detail-item">
      <span class="detail-label">Methodology:</span>
      <span>Developed a high-performance numerical engine to calculate vibrational properties from <strong>Reverse Monte Carlo (RMC)</strong> structural models.</span>
    </div>
    <div class="detail-item">
      <span class="detail-label">Algorithm:</span>
      <span>Implemented <strong>Force Constant Matrix (Hessian)</strong> construction for large-scale disordered supercells, using finite-displacement methods, sparse numerical workflows, and Python/C++ kernels.</span>
    </div>
    <div class="detail-item">
      <span class="detail-label">Challenge:</span>
      <span>Optimized the computation of the <strong>Dynamic Structure Factor</strong> $S(Q, \omega)$ by parallelizing the Fourier transform of atomic trajectories across $10^5+$ coordinates.</span>
    </div>
    <div class="detail-item">
      <span class="detail-label">Impact:</span>
      <span>Enables direct comparison between theoretical atomic models and experimental Inelastic Neutron Scattering (INS) data for non-crystalline materials.</span>
    </div>
  </div>

  <div class="stack-tags">
    <span class="tech-tag">Python</span>
    <span class="tech-tag">NumPy</span>
    <span class="tech-tag">SciPy (Sparse)</span>
    <span class="tech-tag">C++ Kernels</span>
  </div>
</div>

<div class="project-figure">
  <img src="/assets/images/phonon-concept.svg" alt="Phonon Dynamics Visualization">
</div>

Materials Data & Scattering Workflows

Research

  <div class="project-details">
    <div class="detail-item">
      <span class="detail-label">Overview:</span>
      <span>A learning and project track for building reproducible Python workflows around neutron scattering, PDF analysis, and atomistic model validation.</span>
    </div>
     <div class="detail-item">
      <span class="detail-label">Features:</span>
      <span>Includes fit diagnostics, model-comparison plots, local-structure summaries, and reusable Python analysis patterns for scattering-informed materials research.</span>
    </div>
  </div>

  <div class="stack-tags">
    <span class="tech-tag">Python</span>
    <span class="tech-tag">Neutron Scattering</span>
    <span class="tech-tag">Matplotlib</span>
    <span class="tech-tag">Model Validation</span>
    <span class="tech-tag">Materials Modeling</span>
  </div>
</div>

<div class="project-figure">
   <img src="/assets/images/rmc-fit-dashboard.svg" alt="Materials data workflow dashboard">
</div>

Physics-Informed Neural Networks

Related Notes

  <div class="project-details">
    <div class="detail-item">
      <span class="detail-label">Overview:</span>
      <span>A learning track for solving differential-equation and inverse-problem examples with neural networks constrained by physical residuals.</span>
    </div>
     <div class="detail-item">
      <span class="detail-label">Focus:</span>
      <span>Connects scientific modeling habits with modern ML: loss design, parameter inference, uncertainty checks, and physically meaningful validation.</span>
    </div>
  </div>

  <div class="stack-tags">
    <span class="tech-tag">PyTorch</span>
    <span class="tech-tag">Inverse Problems</span>
    <span class="tech-tag">Scientific ML</span>
    <span class="tech-tag">Optimization</span>
  </div>
</div>

<div class="project-figure">
   <img src="/assets/images/pinn_concept.svg" alt="Physics-informed neural network concept">
</div>