Zobrazeno 1 - 10
of 242
pro vyhledávání: '"C A, Goldsmith"'
Publikováno v:
ACS Physical Chemistry Au, Vol 4, Iss 3, Pp 247-258 (2024)
Externí odkaz:
https://doaj.org/article/f6cab58f443b4c68bf9f7022d4131947
Autor:
Bjarne Kreitz, Khachik Sargsyan, Katrín Blöndal, Emily J. Mazeau, Richard H. West, Gregor D. Wehinger, Thomas Turek, C. Franklin Goldsmith
Publikováno v:
JACS Au, Vol 1, Iss 10, Pp 1656-1673 (2021)
Externí odkaz:
https://doaj.org/article/a3c3cada500e41d7a777f823316a5322
Publikováno v:
Neuromorphic Computing and Engineering, Vol 3, Iss 2, p 023001 (2023)
This article is a historical perspective on how the study of the neuromechanics of insects and other arthropods has inspired the construction, and especially the control, of hexapod robots. Many hexapod robots’ control systems share common features
Externí odkaz:
https://doaj.org/article/6964f685196648698a58fd8a3aec4bf2
Publikováno v:
The Journal of Physical Chemistry A. 127:1499-1511
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41372ea469d3ace91399e6695f4d2146
https://doi.org/10.1021/acs.jpca.2c06623
https://doi.org/10.1021/acs.jpca.2c06623
Publikováno v:
ACS Catalysis. 13:19-32
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1793e96668226d4bb6c83aee26342280
https://doi.org/10.1021/acscatal.2c04246
https://doi.org/10.1021/acscatal.2c04246
Autor:
Matthew S. Johnson, Xiaorui Dong, Alon Grinberg Dana, Yunsie Chung, David Farina, Ryan J. Gillis, Mengjie Liu, Nathan W. Yee, Katrin Blondal, Emily Mazeau, Colin A. Grambow, A. Mark Payne, Kevin A. Spiekermann, Hao-Wei Pang, C. Franklin Goldsmith, Richard H. West, William H. Green
Publikováno v:
Journal of Chemical Information and Modeling. 62:4906-4915
The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7bc6321c63b96b9e056610a26ea32104
https://doi.org/10.1021/acs.jcim.2c00965
https://doi.org/10.1021/acs.jcim.2c00965
Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::713e6b9b7814b7c0664bfe9814dc8765
https://doi.org/10.26434/chemrxiv-2023-rskz3
https://doi.org/10.26434/chemrxiv-2023-rskz3
Autor:
Bjarne Kreitz, Patrick Lott, Andrew J. Medford, Felix Studt, Olaf Deutschmann, C. Franklin Goldsmith
The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. Two thousand unique microkinetic models were generated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d430f00252e0ecf5eb895970a099cbe
https://doi.org/10.26434/chemrxiv-2023-hnb4l
https://doi.org/10.26434/chemrxiv-2023-hnb4l
Autor:
Jongyoon Bae, Javad Hashemi, Dongmin Yun, Do Kyoung Kim, Dae Hyun Choo, C. Franklin Goldsmith, Andrew A. Peterson
Publikováno v:
Catalysis Science & Technology. 12:6903-6919
A combined heterogeneous and homogeneous model of methane conversion reveals the temperature limitations of methane to olefins, aromatics, and hydrogen (MTOAH).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7063f5cb8e262636ce04685665b95e7
https://doi.org/10.1039/d2cy00907b
https://doi.org/10.1039/d2cy00907b
Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions, since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using densit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::00f944d8bc540c9be3d236210ae5ed25
https://doi.org/10.26434/chemrxiv-2023-kfwd0
https://doi.org/10.26434/chemrxiv-2023-kfwd0