Data centers will consume 9% of U.S. electricity generation annually by 2030 according to the Electric Power Research Institute. There are two energy-related challenges for building AI data centers with a greater capacity: a shortage of electricity and the need to reduce carbon emissions. Small modular reactors (SMRs) may solve both issues by co-location with data center campuses. Compared to...
MXenes, a rapidly growing class of two-dimensional materials, are known for their diverse electronic, mechanical, and optoelectronic properties. Among these, double-transition-metal MXenes, such as Cr₂TiC₂Tx, offer even greater tunability in their properties due to the presence of two distinct transition metals. Cr₂TiC₂Tx stands out as a unique material among the MXenes experimentally tested,...
Two-dimensional (2D) materials have garnered significant attention due to their unique properties which result from their reduced dimensionality and quantum confinement. In this talk, I will present our recent research on the data-driven discovery of various architectures of atomically thin materials for photocatalytic splitting of water for generating clean hydrogen. One of the most daunting...
Germanium and tin both belong to group IV of the periodic table, but exhibit different properties when it comes to electrical conductivity. Ge is a semiconductor, with a direct and indirect bandgap of 0.8 eV and 0.67 eV respectively, while 𝛼-Sn is a (semi)metal. When combined as an alloy, the bandgap of GeSn can be tuned from 0.67 eV to 0 eV by varying the Sn content making it a useful...
Reduction of defects such as cracking, porosity, lack of fusion, distortion, and surface roughness and control of grain structure and phase formation are needed to improve part quality, reduce cost, and increase the market penetration of 3D printed or additively manufactured components. Reduction in defects and control of microstructure cannot be done by time-consuming and expensive...
We present the quantum simulation of the frustrated quantum spin-$1/2$ antiferromagnetic Heisenberg spin chain with competing nearest-neighbor ($J_{1}$) and next-nearest-neighbor ($J_{2}$) exchange interactions in the real superconducting quantum computer with qubits ranging up to 100. In particular, we implement the Hamiltonian with the next-nearest neighbor exchange interaction in...
Two-dimensional (2D) perovskites are promising materials for nonlinear optics, photonics, and optoelectronics due to their strong excitonic behavior, quantum confinement, and structural tunability. We investigate second-order nonlinear optical properties of mono- and few-layered (Benzylammonium)2PbX4 (where X = Br, Cl, and I), with our current focus on (Benzylammonium)2PbBr4. Bulk crystals...
Two-dimensional (2D) materials placed on top of bulk substrates form complex heterostructures with rich interfacial physics with potential use in various applications such as nanoelectronics, sensing, and energy conversion. While numerous prior works have studied 2D-2D heterostructures, there have been relatively fewer studies that explore interfaces of 2D-bulk material heterostructures. In...
Fouling release performance is a critical factor in marine coatings, influencing their effectiveness in preventing biofouling. In this study, Gradient Boosting Regressor (GBM) models were developed to predict fouling release properties based on experimental data for assays performed for C. lytica at 10 psi and 20 psi and N. incerta at 20 psi. The coating systems analyzed consisted of SBMA...
The increasing demand for refractory materials that can withstand high temperature environments necessitates the discovery of advanced alloys for aeronautic and energy applications. This study introduces a computational approach leveraging AFLOW's machine learning frameworks—specifically the Property Labeled Material Fragments (PLMF) and Molar Fragment Descriptor (MFD) methodologies—to...
Achieving high-purity, detector-grade germanium (HPGe) crystals is essential for rare-event physics experiments such as dark matter detection and neutrinoless double-beta decay searches. We present a predictive machine learning framework that leverages Long Short-Term Memory (LSTM) networks to forecast the detector-grade yield fraction in HPGe crystal growth, based on experimentally obtained...
MXenes, owing to their unique morphology, high surface-to-volume ratio, and metallic conductivity, have gained significant attention as promising materials for gas sensing. Conventional MXene-based sensors primarily utilize electrical conductivity for signal transduction, but alternative mechanisms, such as mass-sensitive detection, can further enhance their selectivity and stability....
Prediction processing-microstructure-property (PMP) link is critical for material processing, characterization, and discovery. We demonstrate GAN-based machine learning models that can accurately predict PMP relationships, specifically in the prediction of (1) the microstructure of alumina under arbitrary laser power, (2) the expected microstructure from the desired hardness, (3) real-time,...
Recent progress in addressing the system size and accuracy limitations of ab-initio materials simulations will be presented. In particular, the development of computational methods and numerical algorithms for conducting fast and accurate large-scale DFT calculations using adaptive finite-element discretization will be presented, which form the basis for the recently released DFT-FE...
Tuning Soft Magnetic Properties in Fe-Based Nanocrystalline Alloys via Ge Substitution
Paul White, Department of Physics
Tula R. Paudel, Department of Physics
Soft magnetic materials play a crucial role in modern electrical and electronic devices, with ongoing research focused on enhancing their performance through compositional and structural modifications. In this study, we...
The Haber–Bosch (HB) process is the foundation of industrial ammonia (NH₃) production, essential for manufacturing nitrate-based fertilizers and offering potential as a hydrogen carrier. However, the HB process consumes over 2% of global energy annually to produce more than 160 million tons of NH₃, primarily due to the high temperatures and pressures required by iron-based catalysts. In this...
