Conveners
Artificial Intelligence in Physics
- Anton Rudakovskyi (Bogolyubov Institute for Theoretical Physics)
The recent surge in machine learning (ML) capabilities, most widely known through the ascent of large language models, mirrors the transformative impact of microprocessors in the 1970s, indicating a potential paradigm shift in the computational toolset used in scientific research workflows, including physics. This talk aims to ignite interest in investigating the multifaceted relationship...
The development of new molecules and the optimization of chemical synthesis depend on the ability to accurately estimate molecular energies and properties from structural information. In recent years, machine learning (ML) methods have emerged as powerful tools for addressing this task [1]. However, a persistent challenge is the illusion of improved accuracy that arises due to error...
We investigated classical machine learning algorithms for categorizing galaxy morphology using the Galaxy Zoo DECaLS dataset. It contains more than 300,000 photos of galaxies, from which we selected 50,000 images with the highest coincidence of human classification choice. Our methodology combined dimensionality reduction with subsequent classification. We evaluated five reduction techniques...
The integrated HydroKinetic Model (iHKM) is a key tool for simulating the complex dynamics of relativistic heavy-ion collisions. However, full-scale iHKM simulations are computationally demanding. This work presents a novel approach combining machine learning with iHKM to both infer optimal model parameters (such as viscosity and relaxation time) from experimental data and to approximate full...
This talk is dedicated to the development and implementation of a methodology for crystal structure prediction using genetic algorithms integrated into the Python ASE library.
The application of genetic algorithms for crystal structure prediction holds great potential in various fields, particularly in materials science and nanotechnology.
The main objective is to develop an efficient tool...
The intriguing phenomenon of superconductivity has been extensively studied for over a century. Yet, a key challenge remains unresolved in practice: understanding and predicting the critical temperature $T_c$ of a superconductor. This issue is especially challenging for high-temperature superconductors (HTSC), which comprise diverse material classes and probably involve different electron...
A third to a half of solar radiation is reflected from a silicon surface of a bare solar cell. That is why a problem of creation additional anti-reflective coatings gathers lots of attention even today. In our submission, we demonstrate that a single patterned polycrystalline silicon layer can suppress average reflectance to $\approx 2\%$ at normal incidence and below 5 % up to $60{}^\circ$...
