Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Austin D. Sendek"'
Autor:
Yinxing Ma, Jiayu Wan, Xin Xu, Austin D. Sendek, Sarah E. Holmes, Brandi Ransom, Zhelong Jiang, Pu Zhang, Xin Xiao, Wenbo Zhang, Rong Xu, Fang Liu, Yusheng Ye, Emma Kaeli, Evan J. Reed, William C. Chueh, Yi Cui
Publikováno v:
ACS Energy Letters. :2762-2771
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2449a1ce40187c51ec594eeada87ae0f
https://doi.org/10.1021/acsenergylett.3c00560
https://doi.org/10.1021/acsenergylett.3c00560
Publikováno v:
Transition Metal Oxides for Electrochemical Energy Storage. :393-409
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5acced0b5e37f94db817e15e2d66d408
https://doi.org/10.1002/9783527817252.ch16
https://doi.org/10.1002/9783527817252.ch16
Publikováno v:
MRS Bulletin. 46:1116-1129
We have identified CrOF4 and NbFe3(PO4)6 as candidate cathodes with density functional theory (DFT) calculations which suggest a useful balance of low chemical expansion and gravimetric energy density. Low chemical expansion is a likely requirement f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c2a20d8a474ab5c774b01009b768fc4
https://doi.org/10.1557/s43577-021-00154-9
https://doi.org/10.1557/s43577-021-00154-9
Autor:
Vincent Dufour-Décieux, Brandi Ransom, Austin D. Sendek, Rodrigo Freitas, Jose Blanchet, Evan J. Reed
Publikováno v:
Journal of chemical theory and computation.
We develop a method to construct temperature-dependent kinetic models of hydrocarbon pyrolysis, based on information from molecular dynamics (MD) simulations of pyrolyzing systems in the high-temperature regime. MD simulations are currently a key too
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::970b5cd6e70e0892e9d87d506aa93fe2
https://pubmed.ncbi.nlm.nih.gov/36399110
https://pubmed.ncbi.nlm.nih.gov/36399110
Autor:
Austin D. Sendek, Brandi Ransom, Ekin D. Cubuk, Lenson A. Pellouchoud, Jagjit Nanda, Evan J. Reed
Publikováno v:
Advanced Energy Materials. 12:2200553
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3af02ac6024137cdced2dd5f34b611cb
https://doi.org/10.1002/aenm.202200553
https://doi.org/10.1002/aenm.202200553
Autor:
Josh Buettner-Garrett, Yi Cui, Evan J. Reed, Evan R. Antoniuk, Brian E. Francisco, Austin D. Sendek, Ekin D. Cubuk, Brandi Ransom
Publikováno v:
ACS applied materialsinterfaces. 12(34)
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature singl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a312df1d92cbc6165270ab165b22ec28
https://pubmed.ncbi.nlm.nih.gov/32700896
https://pubmed.ncbi.nlm.nih.gov/32700896
Publikováno v:
Chemistry of Materials. 31:342-352
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72c062241b3ca76c2b634819e28fe8ee
https://doi.org/10.1021/acs.chemmater.8b03272
https://doi.org/10.1021/acs.chemmater.8b03272
Publikováno v:
Energy & Environmental Science. 10:306-320
We present a new type of large-scale computational screening approach for identifying promising candidate materials for solid state electrolytes for lithium ion batteries that is capable of screening all known lithium containing solids. To be useful
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a3370d876b2ab62ade1ce3e07b28278
https://doi.org/10.1039/c6ee02697d
https://doi.org/10.1039/c6ee02697d
Publikováno v:
The Journal of chemical physics. 150(21)
Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4f8303874277ea056c658b7dd8357a7
https://pubmed.ncbi.nlm.nih.gov/31176329
https://pubmed.ncbi.nlm.nih.gov/31176329
We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8969ce035b8b0c0db74073e9032dd2b9
http://arxiv.org/abs/1904.08996
http://arxiv.org/abs/1904.08996
