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In recent years, two-dimensional (2D) materials have emerged as an important class of nanomaterials for novel applications in optoelectronic and energy related devices. These potential applications are due to the unusual electronic, optical and magnetic properties that arise from the reduced dimensionality of 2D materials. In addition, various methods have been employed to further engineer the properties of 2D materials such as alloying, chemical functionalization and creating heterostructures. To guide experimentalists in the process of materials discovery and design, accurate computational methodologies must be used. These quantum simulations can achieve a fundamental understanding of the electronic structure of a given material by approximately solving the many-electron Schrodinger equation. Currently, density functional theory (DFT) is the most widely used method due to its relative accuracy and computational efficiency. Despite this advantage, there are significant shortcomings of DFT that can be addressed by using more accurate many-body methodologies such as Quantum Monte Carlo (QMC). In this thesis, we transition from DFT to more sophisticated methods (QMC) to study and engineer the electronic, optical and magnetic properties of 2D materials with a higher degree of accuracy for next generation devices.

Several articles have looked at factors that affect the adjustments of point spreads, based on hot hands or streaks, for smaller durations of time. This study examines these effects for 34 regular seasons in the National Basketball Association (NBA). Estimating a Seemingly Unrelated Regression model using all 34 seasons, all streaks significantly impacted point spreads and difference in actual points. When estimating each season individually, differences emerged particularly examining winning and losing streaks of six games or more. The results indicate both the presence of momentum effects and the gambler's fallacy.

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H\\(_3\\)S and LaH\\(_{10}\\)) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature (\\(T_c\\)) of over 900 hydride materials under a pressure range of (0 to 500) GPa, where we found 122 dynamically stable structures with a \\(T_c\\) above MgB\\(_2\\) (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict \\(T_c\\) and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure.

In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFF models including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB, MatterSim and OMat24, the CHIPS-FF workflow integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate automated high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling.

In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 13 graph-based MLFF models including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB and OMat24, the CHIPS-FF workflow integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate automated high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling.

The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding, resulting in uncontrolled atomic migration into the vdW gaps during cation deintercalation from the bulk structure, ultimately leading to unpredictable structural disorder. We propose a generic framework using neural network potentials (NNPs) to accurately model the widespread nonstoichiometric environments resulting from disordered atomic migrations during exfoliation of non-vdW materials. We apply our framework to investigate the crystal structures and phase transformations occurring during the exfoliation of non-vdW nonstoichiometric Cr\\(_{(1-x)}\\)S systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic applications. The efficacy of the NNP outperforms the conventional cluster expansion, exhibiting superior accuracy and transferability to unexplored crystal structures and compositions. By employing the NNP in simulated annealing optimizations, we predict low-energy Cr\\(_{(1-x)}\\)S structures anticipated to result from experimental synthesis. A notable structural transition is discerned at the Cr\\(_{0.5}\\)S composition, with half of the Cr atoms preferentially migrating to vdW gaps. This aligns with experimental observations in the chemical exfoliation of 2D CrS\\(_2\\), and emphasizes the vital role of excess Cr atoms beyond the Cr/S = \\(1/2\\) composition ratio in stabilizing vdW gaps. Additionally, we utilize the NNP in a vacancy diffusion Monte Carlo simulation to illustrate the impact of lateral compressive strains in catalyzing the formation of vdW gaps within non-vdW CrS\\(_2\\) slabs. This provides a direct pathway for more facile exfoliation of ultrathin nanosheets from non-vdW materials through strain engineering.

Finding new superconductors with a high critical temperature (\\(T_c\\)) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique structures and chemical compositions. Specifically, we used a crystal diffusion variational autoencoder (CDVAE) along with atomistic line graph neural network (ALIGNN) pretrained models and the Joint Automated Repository for Various Integrated Simulations (JARVIS) superconducting database of density functional theory (DFT) calculations to generate new superconductors with a high success rate. We started with a DFT dataset of \\(\\approx\\)1000 superconducting materials to train the diffusion model. We used the model to generate 3000 new structures, which along with pre-trained ALIGNN screening results in 61 candidates. For the top candidates, we performed DFT calculations for validation. Such approaches go beyond the funnel-like materials design approaches and allow for the inverse design of next-generation materials.

Previous works have controversially claimed near-room temperature ferromagnetism in two-dimensional (2D) VSe\\(_2\\), with conflicting results throughout the literature. These discrepancies in magnetic properties between both phases (T and H phase) of 2D VSe\\(_2\\) are most likely due to the structural parameters being coupled to the magnetic properties. Specifically, both phases have a close lattice match and similar total energies, which makes it difficult to determine which phase is being observed experimentally. In this study, we used a combination of density functional theory (DFT), highly accurate diffusion Monte Carlo (DMC) and a surrogate Hessian line-search optimization technique to resolve the previously reported discrepancy in structural parameters and relative phase stability. With DMC accuracy, we determined the freestanding geometry of both phases and constructed a phase diagram. Our findings demonstrate the successes of the DMC method coupled with the surrogate Hessian structural optimization technique when applied to a 2D magnetic system.

Two-dimensional (2D) post-transition metal chalcogenides (PTMC) have attracted attention due to their suitable band gaps and lower exciton binding energies, making them more appropriate for electronic, optical and water-splitting devices than graphene and monolayer transition metal dichalcogenides (TMDs). Of the predicted 2D PTMCs, GaSe has been reliably synthesized and experimentally characterized. Despite this fact, quantities such as lattice parameters and band character vary significantly depending on which density functional theory (DFT) functional is used. Although many-body perturbation theory (GW approximation) has been used to correct the electronic structure and obtain the excited state properties of 2D GaSe, and solving the Bethe-Salpeter equation (BSE) has been used to find the optical gap, we find that the results depend strongly on the starting wavefunction. In attempt to correct these discrepancies, we employed the many-body Diffusion Monte Carlo (DMC) method to calculate the ground and excited state properties of GaSe because DMC has a weaker dependence on the trial wavefunction. We benchmark these results with available experimental data, DFT [local-density approximation, Perdew-Burke-Ernzerhof (PBE), strongly constrained and appropriately normed (SCAN) meta-GGA, and hybrid (HSE06) functionals] and GW-BSE (using PBE and SCAN wavefunctions) results. Our findings confirm monolayer GaSe is an indirect gap semiconductor (Gamma-M) with a quasiparticle electronic gap in close agreement with experiment and low exciton binding energy. We also benchmark the optimal lattice parameter, cohesive energy and ground state charge density with DMC and various DFT methods. We aim to present a terminal theoretical benchmark for pristine monolayer GaSe, which will aide in the further study of 2D PTMCs using DMC methods.