In this talk, I will discuss a coarse-grained, statistical (probabilistic) representation of the potential energy surface (PES) of solid-state systems we have recently developed. It is constructed using a combination of the first-principles random structure sampling and structure transformation modeling. The random structure sampling identifies statistically relevant (i.e., more probable)...
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...
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...
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...
The transformative potential of artificial intelligence (AI) extends beyond the laboratory. This plenary address explores the impact of AI in education, touching on how intelligent systems are being deployed in educational contexts and discussing the ethical and pedagogical challenges inherent in integrating these powerful technologies into educational settings. By drawing parallels between...
Liquid electrolytes play a pivotal role in governing the performance, safety, and longevity of lithium and sodium ion batteries. However, designing optimal electrolytes remains a complex challenge due to the need to simultaneously satisfy multiple criteria, including high ionic conductivity, broad electrochemical stability, low viscosity, chemical compatibility with electrodes, and thermal...
This interactive workshop equips middle and high school teachers to demystify Artificial Intelligence (AI) for their students. We'll explore core AI concepts, delve into the critical topic of AI bias, and examine the practical implications of AI's capabilities and limitations within educational settings. Participants will learn effective strategies for discussing AI and Large Language Models...
The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation, capture and management, polymer...
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...
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...
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...
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...
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...
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...
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....
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...
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...
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....
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,...
Accurately predicting the electronic properties of materials under realistic conditions remains a central challenge in materials science, particularly for systems that deviate from ideal crystalline order, such as those with disorder, defects, or thermal fluctuations. Many-body methods based on the GW approximation provide the accuracy needed to capture these effects, but their high...
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...
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...
Dark matter is a mysterious substance that makes up about 25% of the mass-energy of the universe. Its discovery not only helps in understanding the universe itself but also reveals the secrets behind the formation of matter. SuperCDMS SNOLAB, a second-generation physics experiment currently under construction in Sudbury, Ontario, aims to detect dark matter candidate particles that exist beyond...
The objective of our research is to investigate the electrocatalytic properties of novel metal-free quantum dots (QDs) composed of the recently discovered 2D material penta-CN2, with the aim of replacing costly and scarce catalysts such as Pt and Pd. Employing a first-principles density functional theory (DFT) based approach, the geometries of the three penta-CN2 quantum dots (QDs) of...
The pursuit of MeV-scale dark matter detection demands highly sensitive instrumentation capable of resolving energy deposits in the sub-eV range. This study introduces a novel Germanium Internal Charge Amplification (GeICA) detector engineered to internally amplify charge signals, significantly lowering the detection threshold. Leveraging ultra-pure, USD-grown germanium crystals and operating...
Laser-Induced Graphene (LIG) offers a cost-effective, scalable platform for electrochemical sensors, driving advancements in environmental monitoring, agriculture, and health diagnostics. Our studies focus on recent developments in LIG-based ion-selective electrodes for nutrient sensing in soil, supporting precision agriculture, and for non-invasive monitoring of metabolites and electrolytes...
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...
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...
Monitoring pesticide concentration distribution across farm fields is crucial for precise application and minimizing environmental impact. Rapid, on-site detection of pesticide spray is hindered by lack of field-deployable and easy-to-use sensors that circumvent sample transportation to limited laboratories that possess the equipment needed for detection. Laser-induced graphene (LIG) shows...
Many recent efforts have focused on developing accurate interatomic potentials via data driven approaches in the pursuit of accelerating scientific studies of atomistic mechanisms as well as discovery of new materials. However, the accuracy of the interatomic potentials is limited by the data on which these models are trained, often obtained from DFT calculations. Thus, improving the quality...
Nonequilibrium phenomena in quantum materials represent an exciting research frontier, in which theoretical insights are critical for both understanding cutting-edge experiments and guiding the exploration and realization of transient quantum states. I will briefly review the main challenges for practical simulations of realistic condensed matter systems based on the propagation of many-body...
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...
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...
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...
The development of new materials and manufacturing processes is increasingly driven by advances in Artificial Intelligence (AI). In materials science, evaluating the properties of candidate designs often relies on computationally intensive simulations, which can become impractical when exploring vast design spaces. Machine learning offers a powerful alternative: surrogate models can...
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,...
In this session participants will gain foundational understanding of the newly adopted K-8 Computer Science standards. Specifically highlighting and discussing computational thinking and AI-literacy, participants will have an opportunity to learn how schools are approaching these topics with students and staff.
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...
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...
In this talk I will discuss efforts of using AI systems to predict fracture and damage. Being able to predict material degradation, and in particular fracture and failure, has been a challenging endeavor for many decades. Dynamic fracture in brittle materials, in particular, has been shown to exhibit a distinct behavior that differs from what analytical theories predict. For example, Linear...
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...
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...
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...
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...
High purity germanium (HPGe) crystals play a critical role in the production of radiation detectors used in searches for rare events, where impurity concentrations need to be minimized to as low as 10¹⁰ atoms/cm³. In this presentation, I will discuss our latest research on HPGe crystal development, specifically designed for these highly sensitive applications, and investigate how AI tools such...
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...
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...
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...
This talk will review recent applications of quantum machine learning to problems in high energy particle physics motivated by the analysis of data from the Large Hadron Collider at CERN, Geneva. Typical tasks include the classifications of jets as quarks or gluons; the classification of calorimeter clusters as electrons or photons; generative modelling of fragmentation and hadronization in...
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...
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...
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...
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...
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...
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...
