Zobrazeno 1 - 10
of 6 361
pro vyhledávání: '"P. Stiles"'
Due to their compositional complexity, refractory multi-principal element alloys (RMPEAs) exhibit a diverse range of material properties, making them highly suitable for several applications. Importantly, single-phase BCC RMPEAs are known for their s
Externí odkaz:
http://arxiv.org/abs/2410.14614
Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of interest, the number of materials for which a specific
Externí odkaz:
http://arxiv.org/abs/2408.17255
We use {\it ab initio} calculations to understand the current-induced optical response and orbital moment accumulation at the surfaces of metallic films. These two quantities are related by a sum rule that equates the circular dichroic absorption int
Externí odkaz:
http://arxiv.org/abs/2408.10081
The tunability of the mechanical properties of refractory multi-principal-element alloys (RMPEAs) make them attractive for numerous high-temperature applications. It is well-established that the phase stability of RMPEAs control their mechanical prop
Externí odkaz:
http://arxiv.org/abs/2408.06237
Autor:
Pocher, Liam A., Adeyeye, Temitayo N., Gibeault, Sidra, Talatchian, Philippe, Ebels, Ursula, Lathrop, Daniel P., McClelland, Jabez J., Stiles, Mark D., Madhavan, Advait, Daniels, Matthew W.
Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing qualitatively accurate analog dynamics of these devices will be importa
Externí odkaz:
http://arxiv.org/abs/2403.11988
Autor:
Gibeault, Sidra, Adeyeye, Temitayo N., Pocher, Liam A., Lathrop, Daniel P., Daniels, Matthew W., Stiles, Mark D., McClelland, Jabez J., Borders, William A., Ryan, Jason T., Talatchian, Philippe, Ebels, Ursula, Madhavan, Advait
Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of probabilistic computing techniques. Augmenting an SMTJ with electronic circuits, to convert the random telegraph fluctu
Externí odkaz:
http://arxiv.org/abs/2312.13171
Autor:
Borders, William A., Madhavan, Advait, Daniels, Matthew W., Georgiou, Vasileia, Lueker-Boden, Martin, Santos, Tiffany S., Braganca, Patrick M., Stiles, Mark D., McClelland, Jabez J., Hoskins, Brian D.
Publikováno v:
Phys. Rev. Applied 22, 014057 (2024)
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann
Externí odkaz:
http://arxiv.org/abs/2312.06446
Autor:
Phan, Nhat-Tan, Prasad, Nitin, Hakam, Abderrazak, Valli, Ahmed Sidi El, Anghel, Lorena, Benetti, Luana, Madhavan, Advait, Jenkins, Alex S., Ferreira, Ricardo, Stiles, Mark D., Ebels, Ursula, Talatchian, Philippe
Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging nanoscale thermal magnetization fluctuations provides an efficient and a
Externí odkaz:
http://arxiv.org/abs/2311.11982
Autor:
Belashchenko, K. D., Flores, G. G. Baez, Fang, W., Kovalev, A. A., van Schilfgaarde, M., Haney, P. M., Stiles, M. D.
Publikováno v:
Phys. Rev. B 108, 144433 (2023)
Spin accumulation and spin current profiles are calculated for a disordered Pt film subjected to an in-plane electric current within the nonequilibrium Green function approach. In the bulklike region of the sample, this approach captures the intrinsi
Externí odkaz:
http://arxiv.org/abs/2309.00183
Autor:
New, Alexander, Pekala, Michael, Pogue, Elizabeth A., Le, Nam Q., Domenico, Janna, Piatko, Christine D., Stiles, Christopher D.
Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art generative model, the physics-guided crystal generation model (PGCGM),
Externí odkaz:
http://arxiv.org/abs/2309.12323