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Chrzan, Daryl C

Publications and source records attributed to Chrzan, Daryl C.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2025

Mid-Infrared Photoluminescence from Tellurium Thin Films

Tellurium is an elemental semiconductor with a 0.34 eV optical band gap, showing promise for mid-infrared (MIR) optoelectronics including light-emitting diodes. However, quantitative measurement of the tellurium luminescence efficiency has not yet been investigated, and growing optically active films remain challenging. Here, we demonstrate the low-temperature growth of bright tellurium thin films by using physical vapor transport. The sample morphology is controlled by varying the growth temperature, yielding continuous thin films, microparticles, or nanowires. Additionally, we demonstrated patterned growth by using a seed layer for selective nucleation. Quantitative photoluminescence measurements at room temperature reveal an internal quantum yield of 2.0% at 0.34 eV for the as-grown tellurium films, comparable to the best-reported efficiencies of III-V and II-VI compound semiconductors with similar band gaps. Furthermore, we demonstrate band-gap tunability with Te-Se alloys, where up to 20% selenium incorporation gradually blue-shifts the band gap to 0.55 eV. The work demonstrates the potential use of tellurium for efficient MIR devices.

Wang, Shu↗
DOE OSTI2025

Thermally Stable Ruthenium Contact for Robust p‑Type Tellurium Transistors

Tellurium (Te) is attractive for p-channel transistors due to its high hole mobility. Despite having a low thermal budget suitable for back-end-of-line (BEOL) monolithic integration, the practical realization of Te transistors is hindered by its thermal stability. In this work, we investigate thermal stability for Te thin films grown via scalable thermal evaporation. Our findings identify ruthenium as a more thermally stable contact for p-type Te transistors, capable of withstanding temperatures up to 250 °C. Ruthenium exhibits significantly lower diffusivity in Te compared to other contact metals commonly used such as nickel and palladium. Using the transfer-length method, we measured a contact resistance of 1.25 kΩ·μm at the ruthenium-tellurium interface. Additionally, the incorporation of high-κ ZrO2 encapsulation not only suppresses the sublimation of the Te channel at elevated temperatures but also serves as the gate dielectric in top-gate devices operating at 1 V, achieving an on/off current ratio of 105.

Rahman, I K M Reaz↗
DOE OSTI2025

MP-ALOE: an r2SCAN dataset for universal machine learning interatomic potentials

We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.

Kuner, Matthew C↗