Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Samuel M Blau"'
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:
Evan Walter Clark Spotte-Smith, Samuel M. Blau, Xiaowei Xie, Hetal D. Patel, Mingjian Wen, Brandon Wood, Shyam Dwaraknath, Kristin Aslaug Persson
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-15 (2021)
Measurement(s) molecule • solid electrolyte interphase Technology Type(s) density functional theory • computational modeling technique Factor Type(s) bond type • charge • spin multiplicity Machine-accessible metadata file describing the repor
Externí odkaz:
https://doaj.org/article/0136d1d9e8fe43d9a7f0f4665c6b38c1
Autor:
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Aniruddh Khanwale, Shyam Dwaraknath, Kristin A. Persson, Samuel M. Blau
Publikováno v:
Digital Discovery. 2:123-137
Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ in domains such as electrochemistry where reaction mechanisms and outcomes are not well understood. To o
Autor:
Evan Walter Clark Spotte-Smith, Thea Bee Petrocelli, Hetal D. Patel, Samuel M. Blau, Kristin A. Persson
Publikováno v:
ACS Energy Letters. 8:347-355
Autor:
Evan Walter Clark Spotte-Smith, Samuel M. Blau, Daniel Barter, Noel J. Leon, Nathan T. Hahn, Nikita S. Redkar, Kevin R. Zavadil, Chen Liao, Kristin A. Persson
Publikováno v:
Journal of the American Chemical Society, vol 145, iss 22
Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::441ab985211d17743be7f7d695bdbdbb
https://doi.org/10.26434/chemrxiv-2023-tntkg-v2
https://doi.org/10.26434/chemrxiv-2023-tntkg-v2
Autor:
Evan Walter Clark Spotte-Smith, Ronald L. Kam, Daniel Barter, Xiaowei Xie, Tingzheng Hou, Shyam Dwaraknath, Samuel M. Blau, Kristin A. Persson
Publikováno v:
ACS Energy Letters, vol 7, iss 4
The formation of passivation films by interfacial reactions, though critical for applications ranging from advanced alloys to electrochemical energy storage, is often poorly understood. In this work, we explore the formation of an exemplar passivatio
Autor:
Kristin A. Persson, Srikanth Allu, Samuel M. Blau, Lorena Alzate-Vargas, Evan Walter Clark Spotte-Smith, Jean-Luc Fattebert
Publikováno v:
The Journal of Physical Chemistry C. 125:18588-18596
Autor:
Shyam Dwaraknath, Xiaowei Xie, Hetal Patel, Samuel M. Blau, Evan Walter Clark Spotte-Smith, Kristin A. Persson
Publikováno v:
Chemical Science
Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks hav
Autor:
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath, Kristin A. Persson, Samuel M. Blau
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b43478107b917185699a462ff56f020
https://doi.org/10.26434/chemrxiv-2021-c2gp3-v2
https://doi.org/10.26434/chemrxiv-2021-c2gp3-v2
Publikováno v:
Chemical science, vol 13, iss 5
Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77e530fd6e531f42bc8400b6a996e7b0
https://doi.org/10.26434/chemrxiv-2021-xr8tf-v2
https://doi.org/10.26434/chemrxiv-2021-xr8tf-v2