• Machine-Learning Accelerated Calculations of Reduced Density Matrices: arXiv:2511.07367  

Artificial intelligence (AI) holds great promise for studying strongly correlated quantum phases of matter, which is otherwise prohibitively expensive in terms of computational resources. Current methods focus on using neural network (NN)-based wavefunction ansätze to capture low-energy many-body states, which nevertheless face a serious issue of expressibility at large system sizes, since the complexity of the wavefunction scales exponentially with system size while NNs scale with a power-law. Hence, there is no guarantee of NN ansätze capturing the low-energy subspace at realistically large system sizes. 

In this work,  we (Awwab, Lexu, and Jiabin) focus on n-particle reduced density matrices (n-RDMs), which exhibit power-law scaling with system size, and they can capture crucial physical behaviors/quantities such as spontaneous symmetry breaking (through 1-RDMs) and many-body ground state energies (through 2-RDMs). We construct a new NN-based approach that can predict the n-RDMs of large systems without heavy computations required. Our NNs demonstrate remarkable precision in predicting n-RDMs for large systems across various physical model benchmarks, which can also help to resolve unphysical convergence in traditional 2-RDM methods, which might open a new avenue for future research on strongly correlated phases.

• THE ASTROPHYSICAL JOURNAL:  https://iopscience.iop.org/article/10.3847/1538-4357/adfb7d 

Our research (https://www.arxiv.org/abs/2508.09370) delivers a practical advance for astrophysics: a AI approach that reliably classifies the coolest stars and brown dwarfs—objects that dominate our Galaxy and often host planets—without slow, expert-by-eye inspection. Using low-resolution near-infrared spectra, our models achieve ~95% accuracy within one spectral subtype and can also flag key physical traits (surface gravity and metallicity), a capability that scales to the millions of spectra expected from surveys like Gaia, SDSS, and SPHEREx. By automating and standardizing this step, the work accelerates cataloging nearby low-mass objects, improves the search for unusual targets, and strengthens population studies that inform planet formation and Galactic structure.

• American Astronomical Society: https://ui.adsabs.harvard.edu/abs/2024AAS...24333006Z/abstract 

We analyzed 219 million low-resolution Gaia DR3 spectra and, after rigorous quality cuts, distilled a high-purity set of 37,945 stellar spectra to build calibrated templates. Anchored to SDSS spectral standards, the library spans 3,360–10,200 Å and includes 201 main-sequence templates across OBAFGKM (subtypes 0–9), plus white dwarfs (DA0.5–DA7.0) and carbon stars (dCG, dCK, dCM). The result is a clean, standardized reference for fast, consistent spectral typing and downstream stellar population studies. Github Repo 

• American Astronomical Society:  https://ui.adsabs.harvard.edu/abs/2025AAS...24546403Z/abstract 

We build AI (machine learning) methods that read low-resolution infrared spectra to identify the coolest stars and brown dwarfs (M, L, T) with about 95% accuracy—no hand-typing required. Beyond labels, the models also flag metallicity and surface gravity clues, helping us quickly map and understand our smallest, coolest galactic neighbors.