Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Alexander J, Norquist"'
Autor:
Noor Titan Putri Hartono, Mansoor Ani Najeeb, Zhi Li, Philip W. Nega, Clare A. Fleming, Xiaohe Sun, Emory M. Chan, Antonio Abate, Alexander J. Norquist, Joshua Schrier, Tonio Buonassisi
Publikováno v:
Crystal Growth & Design. 22:5424-5431
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d5d32064f07a0549bbc892357e7882a
https://doi.org/10.1021/acs.cgd.2c00522
https://doi.org/10.1021/acs.cgd.2c00522
Autor:
Mansoor Ani Najeeb Nellikkal, Rodolfo Keesey, Margaret Zeile, Venkateswaran Shekar, Zhi Li, Nicholas Leiby, Matthias Zeller, Emory M. Chan, Joshua Schrier, Alexander J. Norquist
Publikováno v:
Chemistry of Materials. 34:5386-5396
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f65a5971aeaa69fe67b53b04ffeb1b5
https://doi.org/10.1021/acs.chemmater.2c00247
https://doi.org/10.1021/acs.chemmater.2c00247
Autor:
Matthias Zeller, Zhi Li, Chaochao Dun, Wissam A. Saidi, Alexander J. Norquist, Jeffrey J. Urban, Mansoor Ani Najeeb, Joshua Schrier, Philip Nega, Emory M. Chan
Publikováno v:
Chemistry of Materials, vol 34, iss 2
Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-drive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::797126aa844e735bf46cdd7c2aa448ff
https://doi.org/10.1021/acs.chemmater.1c03564
https://doi.org/10.1021/acs.chemmater.1c03564
Autor:
Rodolfo Keesey, Armi Tiihonen, Alexander E. Siemenn, Thomas W. Colburn, Shijing Sun, Noor Titan Putri Hartono, James Serdy, Margaret Zeile, Keqing He, Cole A. Gurtner, Austin C. Flick, Clio Batali, Alex Encinas, Richa R. Naik, Zhe Liu, Felipe Oviedo, I. Marius Peters, Janak Thapa, Siyu Isaac Parker Tian, Reinhold H. Dauskardt, Alexander J. Norquist, Tonio Buonassisi
This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ff8efb4880a3c06c374706e9a2af112
https://doi.org/10.26434/chemrxiv-2022-wp18w-v2
https://doi.org/10.26434/chemrxiv-2022-wp18w-v2
Autor:
Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loïc M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle Friedler, Joshua Schrier
Machine learning is a useful tool for accelerating materials discovery, however it is a challenge to develop accurate methods that successfully transfer between domains while also broadening the scope of reaction conditions considered. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9b7d8eb73803574e204f28241acb281
https://doi.org/10.26434/chemrxiv-2022-l1wpf-v2
https://doi.org/10.26434/chemrxiv-2022-l1wpf-v2
Autor:
Liana Alves, Mansoor Ani Najeeb, Peter Cruz Parrilla, Matthias Zeller, Zhi Li, Alexander J. Norquist, Joshua Schrier, Venkateswaran Shekar, Philip Nega, Ian M. Pendleton, Emory M. Chan, Wesley Wang, Alyssa Z. Sherman
Publikováno v:
Chemistry of Materials. 32:5650-5663
Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da18a717ce37bcc8db8eaecffa13ebd7
https://doi.org/10.1021/acs.chemmater.0c01153
https://doi.org/10.1021/acs.chemmater.0c01153
Autor:
Michael Tynes, Alexander J. Norquist, Joshua Schrier, Zhi Li, Emory M. Chan, Mary Kathleen Caucci, Ian M. Pendleton, Mansoor Ani Najeeb Nellikkal, Aaron Dharna
Publikováno v:
The Journal of Physical Chemistry C. 124:13982-13992
Discovery of new perovskite materials is motivated by a broad range of materials applications and accelerated by recent advances in machine learning (ML). We herein report dataset augmentation, ben...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e9821c2866d7ab8549768d3b15215b2
https://doi.org/10.1021/acs.jpcc.0c01726
https://doi.org/10.1021/acs.jpcc.0c01726
Autor:
Joshua Schrier, Jordi Cabana, Ian M. Pendleton, Gene M. Nolis, Matthew L. Nisbet, Alexander J. Norquist, Kenneth R. Poeppelmeier, Kent J. Griffith
Publikováno v:
Journal of the American Chemical Society. 142:7555-7566
Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fbdae9596efefb54fad6469953fa5d7
https://doi.org/10.1021/jacs.0c01239
https://doi.org/10.1021/jacs.0c01239
Autor:
Mansoor Ani Najeeb, Sorelle A. Friedler, Vincent Yu, Emory M. Chan, Zhi Li, Alexander J. Norquist, Dylan Slack, Venkateswaran Shekar, Joshua Schrier, Philip Nega, Gareth Nicholas, Xiaorong Wang, Margaret Zeile
Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bb496e9061d2b4c98e8d3ea86eda1d1
https://doi.org/10.26434/chemrxiv-2021-tfdmc
https://doi.org/10.26434/chemrxiv-2021-tfdmc
Autor:
Aaron E Ruby, Hao Wang, Joshua Schrier, Immaculate Lang'at, Alexander Milder, Sorelle A. Friedler, Allyson Lynch, Alexander J. Norquist, Yuheng Huang, Matthew Danielson, Xiwen Jia
Publikováno v:
Nature. 573:251-255
Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases1, heuristics2 and social influences3. These anthropogenic chemical reaction data are widely used to train machine-learning mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82aa27e4d6799edf1deee80090486c62
https://doi.org/10.1038/s41586-019-1540-5
https://doi.org/10.1038/s41586-019-1540-5