Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Gihan Panapitiya"'
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-18 (2023)
Abstract Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structur
Externí odkaz:
https://doaj.org/article/eab988fe3b204333aaa12b69180e87cc
Autor:
Gihan Panapitiya, Emily Saldanha
Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Becau
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df0742f7201e1bb7049421ae60c4cddb
https://doi.org/10.26434/chemrxiv-2023-pmrfw
https://doi.org/10.26434/chemrxiv-2023-pmrfw
Autor:
Gihan Panapitiya, Michael Girard, Aaron Hollas, Jonathan Sepulveda, Vijayakumar Murugesan, Wei Wang, Emily Saldanha
Publikováno v:
ACS Omega. 7:15695-15710
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction m
Autor:
Gihan Panapitiya, Emily Saldanha
Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Becau
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1a1f0957d522612b4a9ee1cd76947f5
http://arxiv.org/abs/2302.06454
http://arxiv.org/abs/2302.06454
Autor:
Yong-Wang Li, Xiaodong Wen, Gihan Panapitiya, James P. Lewis, Guillermo Avendaño-Franco, Pengju Ren
Publikováno v:
Journal of the American Chemical Society. 140:17508-17514
We propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au25 nanocluster, are utilized in our model. One
Publikováno v:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
Publikováno v:
Physical Chemistry Chemical Physics. 20:13747-13756
In this study, we explore the structural, electronic and catalytic properties of bimetallic nanoparticles of the form Au25−xAgx(SR)18 (for x = 6, 7, 8). Due to the combinatorial enormity of the number of different alloyed structures, we choose 500
Autor:
Renxi Jin, Yan Xing, James P. Lewis, Gihan Panapitiya, Meng Zhou, Rongchao Jin, Shuo Zhao, Chong Liu, Nathaniel L. Rosi
Publikováno v:
Nanoscale. 9:19183-19190
Doping metal nanoclusters with a second type of metal is a powerful method for tuning the physicochemical properties of nanoclusters at the atomic level and it also provides opportunities for a fundamental understanding of alloying rules as well as n
Publikováno v:
Computational Materials Science. 170:109173
Delafossite materials have been studied for a long time for photovoltaic and catalytic applications due to their wide band gaps and bipolar conductivities. These systems have forbidden fundamental band gaps which are much smaller than their apparent
Autor:
James P. Lewis, Pavel Jelínek, Oleg V. Prezhdo, Hong Wang, Oshadha Ranasingha, Gihan Panapitiya, Vladimír Zobač, Amanda Neukirch
Publikováno v:
The journal of physical chemistry letters. 7(8)
Gold nanoparticles distinguish themselves from other nanoparticles due to their unique surface plasmon resonance properties that can be exploited for a multiplicity of applications. The promise of plasmonic heating in systems of Au nanoparticles on t