Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Christopher Kuenneth"'
Autor:
Rishi Gurnani, Stuti Shukla, Deepak Kamal, Chao Wu, Jing Hao, Christopher Kuenneth, Pritish Aklujkar, Ashish Khomane, Robert Daniels, Ajinkya A. Deshmukh, Yang Cao, Gregory Sotzing, Rampi Ramprasad
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industr
Externí odkaz:
https://doaj.org/article/02af7782e66e49df95225e4b67d0ed64
Autor:
Rishi Gurnani, Stuti Shukla, Deepak Kamal, Chao Wu, Jing Hao, Christopher Kuenneth, Pritish Aklujkar, Ashish Khomane, Robert Daniels, Ajinkya A. Deshmukh, Yang Cao, Gregory Sotzing, Rampi Ramprasad
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/dcd4f8b1e5fd4d58ba71f0921b8d27da
Autor:
Christopher Kuenneth, Rampi Ramprasad
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to
Externí odkaz:
https://doaj.org/article/cf08bcaaa38b496ca1dd9f3c137c723a
Autor:
Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone, Carl N. Iverson, Rampi Ramprasad, Ghanshyam Pilania
Publikováno v:
Communications Materials, Vol 3, Iss 1, Pp 1-10 (2022)
Biodegradable polyhydroxyalkanoates are promising replacements for non-degradable plastics. Here, neural network property predictors are applied to a search space of approximately 1.4 million candidates, identifying 14 polyhydroxyalkanoates that coul
Externí odkaz:
https://doaj.org/article/3c9921783df34dd3ab1b366c1b850d84
Publikováno v:
Chemistry of Materials. 35:1560-1567
Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fb88c862e1b41e201e2c43ee5bd5348
https://doi.org/10.1021/acs.chemmater.2c02991
https://doi.org/10.1021/acs.chemmater.2c02991
Autor:
Christopher Kuenneth, Rampi Ramprasad
Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::922745a7039efdc3a6aef160fcb3ad55
https://doi.org/10.21203/rs.3.rs-2116998/v1
https://doi.org/10.21203/rs.3.rs-2116998/v1
Publikováno v:
The Journal of Physical Chemistry A. 124:9496-9502
Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn-Sham equation, which remains a major obstac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f475ae1874bdb310dc59fe3a2cb9cdb8
https://doi.org/10.1021/acs.jpca.0c07458
https://doi.org/10.1021/acs.jpca.0c07458
Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::396d54980810faec14d822afeb65c607
Autor:
Lihua Chen, Huan Tran, Rampi Ramprasad, Chiho Kim, Arunkumar Chitteth Rajan, Christopher Kuenneth
Publikováno v:
Patterns
Summary Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full bre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7df994a17621c5883ecf5e19cb48cecf
http://arxiv.org/abs/2010.15166
http://arxiv.org/abs/2010.15166
Publikováno v:
Macromolecules. 54:7321-7321
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24410813188e9ec9157649b009fad1d6
https://doi.org/10.1021/acs.macromol.1c01539
https://doi.org/10.1021/acs.macromol.1c01539