Zobrazeno 1 - 10
of 251
pro vyhledávání: '"Apurva, Mehta"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase
Externí odkaz:
https://doaj.org/article/6db33227cdc646a98b8f2c058c5a9929
Publikováno v:
Materials, Vol 17, Iss 16, p 4038 (2024)
The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human opera
Externí odkaz:
https://doaj.org/article/7121230b42b546bb9d6699bd363d0dff
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4836 (2024)
Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in envir
Externí odkaz:
https://doaj.org/article/882ebc31e7244cbf938c5a68731c8b53
Autor:
Jihyun Baek, Qiu Jin, Nathan Scott Johnson, Yue Jiang, Rui Ning, Apurva Mehta, Samira Siahrostami, Xiaolin Zheng
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Hydrogen peroxide production from water electrochemical oxidation is a challenging process. Here the authors report discovery of LaAlO3 after screening a series of perovskites as active, stable, and selective catalyst for electrochemical H2O2 product
Externí odkaz:
https://doaj.org/article/4ce094e8435348968570d988010ff395
Autor:
Andrew Lee, Suchismita Sarker, James E. Saal, Logan Ward, Christopher Borg, Apurva Mehta, Christopher Wolverton
Publikováno v:
Communications Materials, Vol 3, Iss 1, Pp 1-11 (2022)
In data-driven approaches for materials discovery, it is essential to account for phase stability when predicting synthesizability. Here, by combining density functional theory calculations and machine learning, the authors predict the synthesizabili
Externí odkaz:
https://doaj.org/article/14ddfccf1d0a44e79072bc8f2728f813
Autor:
Sanghoon Kim, Sachin Pathak, Sonny H. Rhim, Jongin Cha, Soyoung Jekal, Soon Cheol Hong, Hyun Hwi Lee, Sung‐Hun Park, Han‐Koo Lee, Jae‐Hoon Park, Soogil Lee, Hans‐Georg Steinrück, Apurva Mehta, Shan X. Wang, Jongill Hong
Publikováno v:
Advanced Science, Vol 9, Iss 24, Pp n/a-n/a (2022)
Abstract Orbital anisotropy at interfaces in magnetic heterostructures has been key to pioneering spin–orbit‐related phenomena. However, modulating the interface's electronic structure to make it abnormally asymmetric has been challenging because
Externí odkaz:
https://doaj.org/article/26db35397dee4b4eaba840864e7ea2f1
Autor:
Margaret M. Kane, Arturas Vailionis, Lauren J. Riddiford, Apurva Mehta, Alpha T. N’Diaye, Christoph Klewe, Padraic Shafer, Elke Arenholz, Yuri Suzuki
Publikováno v:
npj Quantum Materials, Vol 6, Iss 1, Pp 1-6 (2021)
Abstract The emergence of ferromagnetism in materials where the bulk phase does not show any magnetic order demonstrates that atomically precise films can stabilize distinct ground states and expands the phase space for the discovery of materials. He
Externí odkaz:
https://doaj.org/article/3c0d4c3edd244811bf204bc7f92c4362
Autor:
A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new
Externí odkaz:
https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
Autor:
A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has
Externí odkaz:
https://doaj.org/article/8cc99020ad8b4a0b9c2303f1b463ade6
Autor:
Apurva, Mehta *, Mazumdar, Himanshu
Publikováno v:
In Computational Biology and Chemistry February 2020 84