A5010732302- Machine Learning in Materials Science
- 2D Materials and Applications
- Heusler alloys: electronic and magnetic properties
- Chalcogenide Semiconductor Thin Films
- Electronic and Structural Properties of Oxides
- Inorganic Chemistry and Materials
- MXene and MAX Phase Materials
- Physics of Superconductivity and Magnetism
- Superconducting Materials and Applications
- Perovskite Materials and Applications
- Superconductivity in MgB2 and Alloys
- Boron and Carbon Nanomaterials Research
- X-ray Diffraction in Crystallography
- Catalysis and Oxidation Reactions
- Advanced Condensed Matter Physics
- Advanced Chemical Physics Studies
- GaN-based semiconductor devices and materials
- Intermetallics and Advanced Alloy Properties
- Crystal Structures and Properties
- Molecular Junctions and Nanostructures
- Magnetic and transport properties of perovskites and related materials
- Iron-based superconductors research
- Computational Drug Discovery Methods
- Sports Analytics and Performance
- Quantum many-body systems
Material Measurement Laboratory
2023-2025
National Institute of Standards and Technology
2023-2025
University of Maryland, Baltimore County
2020-2024
University of Baltimore
2020
Yale University
2020
Louisiana State University
2014
High-throughput density functional theory (DFT) calculations allow for a systematic search conventional superconductors. With the recent interest in two-dimensional (2D) superconductors, we used high-throughput workflow to screen over 1000 2D materials JARVIS-DFT database and performed electron-phonon coupling calculations, using McMillan-Allen-Dynes formula calculate superconducting transition temperature (Tc) 165 of them. Of these materials, identify 34 dynamically stable structures with...
Finding new superconductors with a high critical temperature (Tc) has been challenging task due to computational and experimental costs. We present diffusion model inspired by the computer vision community generate unique structures chemical compositions. Specifically, we used crystal variational autoencoder (CDVAE) along atomistic line graph neural network (ALIGNN) pretrained models Joint Automated Repository for Various Integrated Simulations (JARVIS) superconducting database of density...
Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety experimental theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, comprehensive comparison benchmarking on an integrated platform with multiple data modalities perfect defect materials is still lacking. This work...
The search for two-dimensional (2D) magnetic materials has attracted a great deal of attention because the experimental synthesis 2D CrI$_3$, which measured Curie temperature 45 K. Often times, these monolayers have higher degree electron correlation and require more sophisticated methods beyond density functional theory (DFT). Diffusion Monte Carlo (DMC) is correlated electronic structure method that been demonstrated successful calculating properties wide variety bulk systems, since it...
The joint automated repository for various integrated simulations (JARVIS) infrastructure at the National Institute of Standards and Technology is a large-scale collection curated datasets tools with more than 80 000 materials millions properties. JARVIS uses combination electronic structure, artificial intelligence, advanced computation, experimental methods to accelerate design. Here, we report some new features that were recently included in infrastructure, such as (1) doubling number...
The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference these engage in three-dimensional bonding, resulting uncontrolled atomic migration into vdW gaps during cation deintercalation bulk structure, ultimately leading unpredictable structural disorder. Computational models capable comprehending widespread nonstoichiometric local environments disordered migrations non-vdW are...
Abstract The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H 3 S and LaH 10 ) has fueled the interest a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations predict critical temperature ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mrow><mml:mi...
Two-dimensional (2D) 1T-VSe2 has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states, and interplay between two. We employed a combined Diffusion Monte Carlo (DMC) functional theory (DFT) approach accurately investigate magnetic properties, CDW their responses strain in monolayer 1T-VSe2. Our calculations show delicate competition various phases, revealing critical insights into relationship...
We investigate the effect of molecular oxygen on photoconductivity monolayer MoS2 via broad-band time-resolved terahertz spectroscopy. observe that transitions from negative to positive when environment changes vacuum atmospheric pressure. argue this transition results physically adsorbed depleting excess electrons n-type MoS2. attribute trion formation, in which photoinduced excitons capture formation as well; however, case, defect rather than electrons, have been immobilized by physisorbed...
Recently, 2D tellurene (Te) structures have been experimentally synthesized. These possess high carrier mobility and stability which make them ideal candidates for applications in electronics, optoelectronics energy devices. We performed density functional theory (DFT) molecular dynamics (MD) simulations to investigate the electronic structure of α- β-Te sheets, hydrogen, oxygen, fluorine functionalized counterparts, including spin-orbit coupling effects. Our calculations show that bare α...
Recently, two-dimensional (2D) group-III nitride semiconductors such as h-BN, h-AlN, h-GaN, and h-InN have attracted attention because of their exceptional electronic, optical, thermoelectric properties. It has also been demonstrated, theoretically experimentally, that properties 2D materials can be controlled by alloying. In this study, we performed density functional theory (DFT) calculations to investigate B1–xAlxN, Al1–xGaxN, Ga1–xInxN alloyed structures. We calculated the these...
Two-dimensional (2D) post-transition metal chalcogenides (PTMCs) have attracted attention due to their suitable bandgaps and lower exciton binding energies, making them more appropriate for electronic, optical, water-splitting devices than graphene monolayer transition dichalcogenides. Of the predicted 2D PTMCs, GaSe has been reliably synthesized experimentally characterized. Despite this fact, quantities such as lattice parameters band character vary significantly depending on which density...
Previous works have controversially claimed near-room-temperature ferromagnetism in two-dimensional (2D) VSe2, with conflicting results throughout the literature. These discrepancies magnetic properties between both phases (T and H) of 2D VSe2 are most likely due to structural parameters being coupled properties. Specifically, a close lattice match similar total energies, which makes it difficult determine phase is observed experimentally. In this study, we used combination density...
Low-dimensional organic–inorganic hybrid perovskites have attracted much interest owing to their superior solar conversion performance, environmental stability, and excitonic properties compared three-dimensional (3D) counterparts. Among reduced-dimensional perovskites, guanidinium-based crystallize in layered one-dimensional (1D) two-dimensional (2D). Here, our studies demonstrate how the dimensionality of perovskite influences chemical physical under different pressures (i.e., bond...
Monolayer MnO$_2$ is one of the few predicted two-dimensional (2D) ferromagnets that has been experimentally synthesized and commercially available. The Mermin-Wagner theorem states magnetic order in a 2D material cannot persist unless anisotropy (MA) present perpendicular to plane, which permits finite critical temperature. Previous computational studies have ordering Curie temperature with DFT+U (Density Funtional Theory + Hubbard U correction), results having strong dependence on...
Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and interplay between two. We employed a combined Diffusion Monte Carlo (DMC) functional theory (DFT) approach accurately investigate magnetic properties response of strain monolayer 1T-VSe$_2$. Our calculations show delicate competition various phases, revealing critical insights into relationship their...
The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering different structures experimentally. In order combat this, first-principles have coupled with statistical such as cluster expansion formalism in construct energy hull diagram, which helps determine if an alloyed structure can exist nature. Traditionally, density functional theory (DFT) used workflows. this paper, we propose use chemically accurate...
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety experimental theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, comprehensive comparison benchmarking on an integrated platform with multiple data modalities both perfect defect materials is still lacking. This work introduces...
Several articles have looked at factors that affect the adjustments of point spreads, based on hot hands or streaks, for smaller durations time. This study examines these effects 34 regular seasons in National Basketball Association (NBA). Estimating a Seemingly Unrelated Regression model using all seasons, streaks significantly impacted spreads and difference actual points. When estimating each season individually, differences emerged particularly examining winning losing six games more....
The field of two-dimensional (2D) materials has grown dramatically in the last two decades. 2D can be utilized for a variety next-generation optoelectronic, spintronic, clean energy, and quantum computation applications. These structures, which are often exfoliated from layered van der Waals (vdW) materials, possess highly inhomogeneous electron densities short- long-range correlations. complexities make them challenging to study with standard mean-field electronic structure methods such as...
This study combines Graph Neural Networks (GNNs) and Large Language Models (LLMs) to improve material property predictions. By leveraging both embeddings, this hybrid approach achieves up a 25% improvement over GNN-only model in accuracy.
In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform machine learning force fields (MLFFs). This provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface and interfacial amorphous phase properties. Utilizing 13 graph-based MLFF models ALIGNN-FF,...