Scholar
Daniel Schwalbe-Koda
Google Scholar ID: yXXLlWAAAAAJ
UCLA
Citations & Impact
Citations
1,416
H-index
19
i10-index
24
Publications
20
Co-authors
0
Publications
Resume
Academic Achievements
- 1. Developed a generative diffusion model for amorphous materials.
- 2. Proposed an atomistic information theory for thermodynamics, UQ, and machine learning.
- 3. Proposed an efficient data generation strategy to control the extrapolation of neural network models and perform fast sampling in different coverage regimes.
Research Experience
- Leads the Digital Synthesis Lab, focusing on developing new ML methods to accelerate materials design, integrating complex physics simulations with high-performance computing, and proposing data-driven models to elucidate materials synthesis.
Background
- Research interests include developing computational methods to enable predictive materials synthesis, thus accelerating their design. Using a range of tools - from databases to machine learning - he proposes solutions in energy, sustainability, and AI.