Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Andrew S. Rosen"'
Autor:
Eric C.-Y. Yuan, Anup Kumar, Xingyi Guan, Eric D. Hermes, Andrew S. Rosen, Judit Zádor, Teresa Head-Gordon, Samuel M. Blau
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully d
Externí odkaz:
https://doaj.org/article/f4975b34f01c443e85ecb32c6c0675d8
Autor:
John Dagdelen, Alexander Dunn, Sanghoon Lee, Nicholas Walker, Andrew S. Rosen, Gerbrand Ceder, Kristin A. Persson, Anubhav Jain
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large la
Externí odkaz:
https://doaj.org/article/e142a6e499644255824966cf6a0c02ee
Autor:
Ryan S. Kingsbury, Andrew S. Rosen, Ayush S. Gupta, Jason M. Munro, Shyue Ping Ong, Anubhav Jain, Shyam Dwaraknath, Matthew K. Horton, Kristin A. Persson
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022)
Abstract Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) l
Externí odkaz:
https://doaj.org/article/c100114fdcc549dcae7c5898c4c8fb3f
Autor:
Andrew S. Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G. Truhlar, Kristin A. Persson, Justin M. Notestein, Randall Q. Snurr
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-10 (2022)
Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of M
Externí odkaz:
https://doaj.org/article/e0c539f7b8f34ab1bf3579bc903a0157
Publikováno v:
Chemical science, vol 14, iss 6
Through a data-mining and high-throughput density functional theory approach, we identify a diverse range of metallic compounds that are predicted to have transition metals with "free-atom-like" d states that are highly localized in terms of their en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a067572920b015332a3224a421041efd
https://doi.org/10.1039/d2sc05772g
https://doi.org/10.1039/d2sc05772g
Publikováno v:
The Journal of Physical Chemistry C. 126:19705-19714
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cdf62e9e2bb9ae5581f47fa5d003e16
https://doi.org/10.1021/acs.jpcc.2c05799
https://doi.org/10.1021/acs.jpcc.2c05799
Publikováno v:
Physical Chemistry Chemical Physics. 24:8129-8141
An iron–triazolate metal–organic framework (MOF) is computationally investigated for the catalytic oxidation of strong C–H bonds. The MOF is predicted to form reactive iron-oxo active sites, and design rules to guide future experiments are disc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d218b10dcfefd96b877a45905e4606e
https://doi.org/10.1039/d2cp00963c
https://doi.org/10.1039/d2cp00963c
Publikováno v:
ACS central science, vol 9, iss 4
The space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. This is, in particular, the case for reticular chemistry which provides materials designers with a practically infinite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8556235d0b8570943dfd7bbb7236f09
https://doi.org/10.26434/chemrxiv-2022-4g7rx
https://doi.org/10.26434/chemrxiv-2022-4g7rx
Autor:
Randall Q. Snurr, Andrew S. Rosen, Laura Gagliardi, Shaelyn M. Iyer, Alán Aspuru-Guzik, Debmalya Ray, Zhenpeng Yao, Justin M. Notestein
Publikováno v:
Matter. 4:1578-1597
Summary The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d89cb0b45c3ba86eb22eb1b0380b31e0
https://doi.org/10.1016/j.matt.2021.02.015
https://doi.org/10.1016/j.matt.2021.02.015
Autor:
Kenton E. Hicks, Justin M. Notestein, Zoha H. Syed, Andrew S. Rosen, Omar K. Farha, Randall Q. Snurr
Publikováno v:
ACS Catalysis. 10:14959-14970
Zirconium-based metal–organic frameworks (Zr-MOFs) have been increasingly studied over the past two decades as heterogeneous catalysts due to their synthetic tunability, well-defined nature, and ch...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac8e04cde7804f5eda93d6ff848de91d
https://doi.org/10.1021/acscatal.0c03579
https://doi.org/10.1021/acscatal.0c03579