Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Ghosh, Ayana"'
Autor:
Fox, Zachary R., Ghosh, Ayana
Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standa
Externí odkaz:
http://arxiv.org/abs/2404.04224
Exploring molecular spaces is crucial for advancing our understanding of chemical properties and reactions, leading to groundbreaking innovations in materials science, medicine, and energy. This paper explores an approach for active learning in molec
Externí odkaz:
http://arxiv.org/abs/2403.01234
Autor:
Boebinger, Matthew G., Ghosh, Ayana, Roccapriore, Kevin M., Misra, Sudhajit, Xiao, Kai, Jesse, Stephen, Ziatdinov, Maxim, Kalinin, Sergei V., Unocic, Raymond R.
Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic str
Externí odkaz:
http://arxiv.org/abs/2310.08378
Autor:
Morozovska, Anna N., Eliseev, Eugene A., Liu, Yongtao, Kelley, Kyle P., Ghosh, Ayana, Liu, Ying, Yao, Jinyuan, Morozovsky, Nicholas V., Kholkin, Andrei L, Vysochanskii, Yulian M., Kalinin, Sergei V.
Using Landau-Ginzburg-Devonshire (LGD) phenomenological approach we analyze the bending-induced re-distribution of electric polarization and field, elastic stresses and strains inside ultrathin layers of van der Waals ferrielectrics. We consider a Cu
Externí odkaz:
http://arxiv.org/abs/2305.15247
Autor:
Liu, Yongtao, Morozovska, Anna N., Ghosh, Ayana, Kelley, Kyle P., Eliseev, Eugene A., Yao, Jinyuan, Liu, Ying, Kalinin, Sergei V.
Nanoscale ferroelectric 2D materials offer unique opportunity to investigate curvature and strain effects on materials functionalities. Among these, CuInP2S6 (CIPS) has attracted tremendous research interest in recent years due to combination of room
Externí odkaz:
http://arxiv.org/abs/2305.14309
Autor:
Morozovska, Anna N., Eliseev, Eugene A., Ghosh, Ayana, Yelisieiev, Mykola E., Vysochanskii, Yulian M., Kalinin, Sergei V.
Strain-induced transitions of polarization reversal in thin films of a ferrielectric CuInP$_2$S$_6$ (CIPS) with ideally-conductive electrodes is explored using the Landau-Ginzburg-Devonshire (LGD) approach with an eighth-order free energy expansion i
Externí odkaz:
http://arxiv.org/abs/2304.04097
Autor:
Kalinin, Sergei V., Mukherjee, Debangshu, Roccapriore, Kevin M., Blaiszik, Ben, Ghosh, Ayana, Ziatdinov, Maxim A., Al-Najjar, A., Doty, Christina, Akers, Sarah, Rao, Nageswara S., Agar, Joshua C., Spurgeon, Steven R.
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operat
Externí odkaz:
http://arxiv.org/abs/2304.02048
Autor:
Kalinin, Sergei V., Ziatdinov, Maxim, Ahmadi, Mahshid, Ghosh, Ayana, Roccapriore, Kevin, Liu, Yongtao, Vasudevan, Rama K.
Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characterization methods aiming to provide feedback for optimization or discover
Externí odkaz:
http://arxiv.org/abs/2302.04397
Publikováno v:
APL Mach. Learn. 1, 046102 (2023)
Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development of machine learning algorithms capable of rapid exploration of the ch
Externí odkaz:
http://arxiv.org/abs/2301.02665
Autor:
Kalinin, Sergei V., Vasudevan, Rama, Liu, Yongtao, Ghosh, Ayana, Roccapriore, Kevin, Ziatdinov, Maxim
We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial int
Externí odkaz:
http://arxiv.org/abs/2210.06526