Zobrazeno 1 - 10
of 964
pro vyhledávání: '"Bobby G. Sumpter"'
Autor:
Matthew Flynn‐Hepford, John Lasseter, Ivan Kravchenko, Steven Randolph, Jong Keum, Bobby G. Sumpter, Stephen Jesse, Petro Maksymovych, A. Alec Talin, Matthew J. Marinella, Philip D. Rack, Anton V. Ievlev, Olga S. Ovchinnikova
Publikováno v:
Advanced Electronic Materials, Vol 10, Iss 1, Pp n/a-n/a (2024)
Abstract Inspired by biological neuromorphic computing, artificial neural networks based on crossbar arrays of bilayer tantalum oxide memristors have shown to be promising alternatives to conventional complementary metal‐oxide‐semiconductor (CMOS
Externí odkaz:
https://doaj.org/article/e9aecfc4b6894036a6c6ab5d8b3dae66
Autor:
Bobby G. Sumpter, Vincent Meunier
Publikováno v:
Carbon Trends, Vol 13, Iss , Pp 100297- (2023)
Externí odkaz:
https://doaj.org/article/1cdc0a1397fe441baabe7047ae78935d
Publikováno v:
Frontiers in Nanotechnology, Vol 5 (2023)
Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for
Externí odkaz:
https://doaj.org/article/dff7a222a8a84f5eaa83710a77190158
Autor:
Kamal Choudhary, Bobby G. Sumpter
Publikováno v:
AIP Advances, Vol 13, Iss 9, Pp 095109-095109-6 (2023)
The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfec
Externí odkaz:
https://doaj.org/article/a1a80cdd09254218b5c0789d9fb62605
Publikováno v:
APL Machine Learning, Vol 1, Iss 2, Pp 026117-026117-10 (2023)
We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of a
Externí odkaz:
https://doaj.org/article/057f9671de0f4b9e9e07456e9326c131
Publikováno v:
Carbon Trends, Vol 11, Iss , Pp 100264- (2023)
Ferroelectric materials such as BaTiO3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of disc
Externí odkaz:
https://doaj.org/article/5341405420c74b40a1ef0ed3ab62cbb0
Autor:
Chi-Huan Tung, Shou-Yi Chang, Ming-Ching Chang, Jan-Michael Carrillo, Bobby G Sumpter, Changwoo Do, Wei-Ren Chen
Publikováno v:
Carbon Trends, Vol 10, Iss , Pp 100252- (2023)
A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which extracts the targeted static two-
Externí odkaz:
https://doaj.org/article/67725bae8e9a426fa58e64c1e1b6cd97
Publikováno v:
Carbon Trends, Vol 10, Iss , Pp 100234- (2023)
With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. We review cur
Externí odkaz:
https://doaj.org/article/c13e72cc9cf44561a911ee80dd101dba
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022)
Abstract Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for
Externí odkaz:
https://doaj.org/article/a441c8a0309343cba2d12bf3dfa9dbd6
Publikováno v:
Physical Review Research, Vol 5, Iss 4, p 043074 (2023)
Using first-principles calculations we model the out-of-plane switching of local dipoles in CuInP_{2}S_{6} (CIPS) that are largely induced by Cu off-centering. Previously, a coherent switching of polarization via a quadruple-well potential was propos
Externí odkaz:
https://doaj.org/article/f4056b40f0a94cbaa8464c5f4a4353fc