arXiv subjects

Alex Ganose

Publications and source records attributed to Alex Ganose.

5 recordsLinked to original sources

arXiv2026

On the possibility of hybrid chalcogenide perovskite photovoltaics

Chalcogenide perovskites are an emerging class of photovoltaic absorbers offering stable, lead-free structures and promising optoelectronic properties. To date, the literature on chalcogenide perovskites has focused primarily on fully inorganic systems such as \ce{BaZrS3}. This contrasts with the halide perovskites, for which hybrid organic-inorganic systems exhibit record performance. In this work, we assess the viability of hybrid chalcogenide perovskite absorbers using first-principles calculations. We screen a wide range of monovalent and divalent organic cations within the A-site to evaluate their electronic, optical, and thermodynamic properties. Our analysis reveals that the majority of candidates are structurally unstable; however, we identify the hydrazinium cation (\ce{N2H6^{2+}}) as a unique candidate that maintains a stable perovskite structure. Specifically, we identify \ce{N2H6ZrSe3} as the most promising candidate, exhibiting a quasi-direct band gap of \SI{1.31}{eV} and a theoretical maximum efficiency of \SI{24.5}{\percent} for a \SI{200}{\nm} thin film. This study represents the first comprehensive computational report on hybrid chalcogenide perovskites, opening new avenues for the development of Earth-abundant photovoltaic materials.

cond-mat.mtrl-sci↗

arXiv2021

Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development

Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale HPC platforms. Workflows will play a crucial role in the data-oriented and post-Moore's computing landscape as they democratize the application of cutting-edge research techniques, computationally intensive methods, and use of new computing platforms. As workflows continue to be adopted by scientific projects and user communities, they are becoming more complex. Workflows are increasingly composed of tasks that perform computations such as short machine learning inference, multi-node simulations, long-running machine learning model training, amongst others, and thus increasingly rely on heterogeneous architectures that include CPUs but also GPUs and accelerators. The workflow management system (WMS) technology landscape is currently segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. Another fundamental problem is that there are conflicting theoretical bases and abstractions for a WMS. Systems that use the same underlying abstractions can likely be translated between, which is not the case for systems that use different abstractions. More information: https://workflowsri.org/summits/technical

cs.DC↗

arXiv2021

Workflows Community Summit: Bringing the Scientific Workflows Community Together

Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale high-performance computing (HPC) platforms. These executions must be managed using some software infrastructure. Due to the popularity of workflows, workflow management systems (WMSs) have been developed to provide abstractions for creating and executing workflows conveniently, efficiently, and portably. While these efforts are all worthwhile, there are now hundreds of independent WMSs, many of which are moribund. As a result, the WMS landscape is segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. As a result, many teams, small and large, still elect to build their own custom workflow solution rather than adopt, or build upon, existing WMSs. This current state of the WMS landscape negatively impacts workflow users, developers, and researchers. The "Workflows Community Summit" was held online on January 13, 2021. The overarching goal of the summit was to develop a view of the state of the art and identify crucial research challenges in the workflow community. Prior to the summit, a survey sent to stakeholders in the workflow community (including both developers of WMSs and users of workflows) helped to identify key challenges in this community that were translated into 6 broad themes for the summit, each of them being the object of a focused discussion led by a volunteer member of the community. This report documents and organizes the wealth of information provided by the participants before, during, and after the summit.

cs.DC↗

arXiv2020

High Thermoelectric Performance and Defect Energetics of Multi-pocketed Full-Heusler Compounds

We report first-principles density-functional study of electron-phonon interactions and thermoelectric transport properties of full-Heusler compounds Sr$_{2}$BiAu and Sr$_{2}$SbAu. Our results show that ultrahigh intrinsic bulk thermoelectric performance across a wide range of temperatures is physically possible and point to the presence of multiply degenerate and highly dispersive carrier pockets as the key factor for achieving it. Sr$_{2}$BiAu, which features ten energy-aligned low effective mass pockets (six along $Γ-X$ and four at $L$), is predicted to deliver $n$-type $zT=0.4-4.9$ at $T=100-700$~K. Comparison with the previously investigated Ba$_{2}$BiAu compound shows that the additional $L$-pockets in Sr$_{2}$BiAu significantly increase its low-temperature power factor to a maximum value of $12$~mW~m$^{-1}$~K$^{-2}$ near $T=300$~K. However, at high temperatures the power factor of Sr$_{2}$BiAu drops below that of Ba$_{2}$BiAu because the $L$ states are heavier and subject to strong scattering by phonon deformation as opposed to the lighter $Γ-X$ states that are limited by polar-optical scattering. Sr$_{2}$SbAu is predicted to deliver lower $n$-type of $zT=3.4$ at $T=750$~K due to appreciable misalignment between the $L$ and $Γ-X$ carrier pockets, generally heavier scattering, and slightly higher lattice thermal conductivity. Soft acoustic modes, responsible for low lattice thermal conductivity, also increase vibrational entropies and high-temperature stability of the Heusler compounds, suggesting that their experimental synthesis may be feasible. The dominant intrinsic defects are found to be Au vacancies, which drive the Fermi level towards the conduction band and work in favor of $n$-doping.

cond-mat.mtrl-sci↗

arXiv2020

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.

cond-mat.mtrl-sci↗