Rare earth–benzene (RE-Bz, RE = La–Lu) complexes have attracted considerable attention due to their catalytic and optical properties. In this presentation, I will discuss our recent findings from a density functional theory(DFT)-based study that explores the geometries, ground-state spin multiplicities, stabilities, and magnetic properties of RE-Bz complexes.
We found that RE–benzene...
The extrusion dynamics in additive manufacturing (AM) processes
such as electrohydrodynamic (EHD) printing are dependent on a large (>15) set of
processing parameters, material properties, and environmental conditions.
In EHD printing, these parameters affect the process stability, printing
behavior (droplet or filament), and quality of the deposited microstructures. In
this work, we have...
The IceCube Neutrino Observatory, located at the geographic South Pole, has opened a new window into the universe through the detection of high-energy astrophysical neutrinos. This contribution presents recent advances in the application of machine learning (ML) techniques to key challenges in astroparticle physics using IceCube data. We highlight the role of deep learning in improving event...
Metal amorphous nanocomposites (MANC) possess properties distinct from crystalline materials due to their characteristic microstructure, which gives them excellent soft magnetic behavior. Recent research has focused on developing novel MANC compositions with superior magnetic and mechanical properties for next-generation lightweight inductor cores in electrified vehicles and rovers. In this...
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...
Harnessing microbial systems for methane bioconversion necessitates a detailed understanding of enzyme architecture and microbial-surface interactions. Integrating microbial biotechnology, materials engineering, and artificial intelligence (AI) offers a transformative strategy for advancing sustainable methane bioconversion. In this study, we computationally remodeled the particulate methane...
Artificial intelligence (AI) is reshaping the future of materials science by enabling faster, smarter, and more targeted discovery and design of new materials. This open talk will introduce key concepts in AI-driven materials research, highlight successful applications across fields such as energy, semiconductors, and biomedical materials, and examine critical challenges including data...
Recent advances of machine learning interatomic potentials (MLIPs) have improved both the accuracy and scalability of energy and force predictions in chemical systems for many practical applications. Here we explore the combination of MLIPs with state-of-the-art ab initio theory of thermal transport, which requires accurate estimations of higher-order derivatives of the potential energy...
Sulfate-reducing bacteria (SRB) thrive in many natural environments, deep environments, and processing facilities in industrial settings and form biofilms. SRB biofilms alter the physiochemical properties of metals, inducing fouling and later biocorrosion, which cost USD 1.1Trillion to global GDP every year. To combat biocorrosion, it is essential to understand the interaction at the bacteria...
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,...
Organic field effect transistors (OFETs) need to be simulated on a large scale for facilitating real time applications. To ensure that transistor-based circuits operate reliably, they need to be objectively modelled for all the physical events that occur during the operation. However, the existing models are yet to be comprehensive enough to address many of the diverse electrical behaviors of...
Nanoplastics (NP) are ubiquitous, and their interactions with agricultural and food systems are shown to be associated with health concerns for plants, animals and humans. However, fast and accurate detection and characterization of NP in biosystems remains a challenge; and what governs the interactions between NP and biosystems are still largely unknown. Data driven techniques utilizing AI/ML...
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...
Sulfate-reducing bacterial (SRB) biofilms are prevalent across natural and engineered environments, mediating biogeochemical sulfur cycling while accelerating biofouling and microbiologically influenced corrosion (MIC). To uncover surface-dependent biofilm adaptations, we performed a comparative transcriptomic analysis of Oleidesulfovibrio alaskensis G20 biofilms formed on copper and...
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...
Moiré superlattices, formed by stacking layered 2D materials with a twist in orientation, have emerged as a new platform for exploration of new physics and exotic quantum phenomena. The twist-angle dependent moiré effects and superlattice potentials offer a new route in materials design and quantum engineering. However, a direct prediction of the superlattice potential remains challenging due...
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...
The application of large language models (LLMs) in scientific domains, particularly materials discovery, remains underexplored compared to general natural language tasks. This study presents a systematic fine-tuning of the DeepSeek-R1 model using Unsloth’s memory-efficient framework and domain-specific supervised instruction techniques. Three publicly available...
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...
Relaxor ferroelectric Lead Magnesium Niobate-Lead Titanate (PMN-PT) crystal has been used in various applications such as medical ultrasound imaging, SONAR, micro-actuation, and energy harvesting due to its high electromechanical coupling coefficient and high piezoelectric coefficient. However, the high cost of PMN-PT stems from low crystal growth yield and limits the wider its application....
Phase change materials (PCMs), which can be reversibly switched between their high-resistance and low-resistance phases, are promising for non-volatile, high-density data storage and research in non-von Neumann computing architectures. Recent discovery and development of novel PCM superlattices consisting of various layers have demonstrated unprecedented low power and high-density at...
One of the fundamental properties of semiconductors is their ability to support electric currents in the presence of electric and magnetic fields. These properties are described by transport coefficients such as drift and Hall electron and hole mobilities. During the past decade, there has been considerable progress in first-principles atomic-scale calculations of these coefficients by...
In this talk, I will present and discuss a coarse-grained, statistical (probabilistic) representation of the potential energy surface (PES) of solid-state systems we have recently developed. This representation is built using a combination of the first-principles random structure sampling and structure transformation modeling. The random structure sampling identifies statistically relevant...
Sulfate-reducing bacteria (SRB) play a pivotal role in the global sulfur cycle and microbial metal transformations, influencing both ecological resilience and industrial challenges such as microbially induced corrosion (MIC). This research focuses on Oleidesulfovibrio alaskensis G20 (OA G20), a genetically tractable SRB model, to elucidate mechanisms underlying stress tolerance, biofilm...
Large language models (LLMs) for biology such as protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein–nucleic acid binding sites, critical for characterizing the interactions between proteins and...
The discovery of the Higgs boson at the Large Hadron Collider (LHC) through proton-proton collisions at CERN marked a major milestone in confirming the Standard Model (SM) of particle physics. While many SM parameters have now been measured with remarkable precision, the measurement of the triple Higgs coupling remains particularly challenging due to its extremely small production...
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...
Multi-component alloy systems based on copper (Cu), silver (Ag), titanium (Ti) and nickel (Ni) are employed in various industrial applications owing to their desirable mechanical, electrical, thermal and antimicrobial properties. Accurate prediction of mechanical properties such as melting points, hardness and modulus of elasticity is crucial for materials discovery, particularly alloy...
Abstract
Identification of materials with two-dimensional properties is crucial for the creation of next-generation technologies, yet remains a computationally intensive task. The study presents an automated pipeline designed to predict if a material has two-dimensional characteristics from its physical and structural characteristics. Two-dimensional (2D) materials are a unique category of...
Silver iodide is extensively studied for its ionic conductivity, in addition to its thermally-driven polymorphism into several phases including wurtzite, zincblende, rocksalt, and body-centered cubic [1,2]. The exact mechanisms and driving forces behind these transformations, however, are not well understood. Recently, molecular dynamics simulations (MD) informed by machine-learned interatomic...
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...
Recent studies have identified titanium carbide (Ti2C) MXenes as promising 2D materials for detecting volatile organic compounds (VOCs) present in human breath. These VOCs reflect physiological changes and can serve as early biomarkers for diseases such as lung cancer. Diagnosing lung cancer through breath analysis offers a non-invasive and rapid alternative to conventional diagnostic...
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...
