Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Kurchin, Rachel"'
Autor:
Wang, Xiaoxiao, Musielewicz, Joseph, Tran, Richard, Ethirajan, Sudheesh Kumar, Fu, Xiaoyan, Mera, Hilda, Kitchin, John R., Kurchin, Rachel C., Ulissi, Zachary W.
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learn
Externí odkaz:
http://arxiv.org/abs/2311.01987
Autor:
Babar, Mohammad, Zhu, Ziyan, Kurchin, Rachel, Kaxiras, Efthimios, Viswanathan, Venkatasubramanian
Publikováno v:
J. Am. Chem. Soc. 2024, 146, 23, 16105-16111
In this work, we develop a twist-dependent electrochemical activity map, combining a tight-binding electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region span
Externí odkaz:
http://arxiv.org/abs/2306.00028
Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple
Externí odkaz:
http://arxiv.org/abs/2305.12010
Electrochemical kinetics at electrode-electrolyte interfaces are crucial to understand high-rate behavior of energy storage devices. Phase transformation of electrodes is typically treated under equilibrium thermodynamic conditions, while realistic o
Externí odkaz:
http://arxiv.org/abs/2212.06952
Autor:
Timmins, Andrew, Kurchin, Rachel C.
Publikováno v:
Journal of Applied Physics; 9/7/2024, Vol. 136 Issue 9, p1-9, 9p
Autor:
Gnaneshwar, Dwaraknath, Ramsundar, Bharath, Gandhi, Dhairya, Kurchin, Rachel, Viswanathan, Venkatasubramanian
Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial traini
Externí odkaz:
http://arxiv.org/abs/2203.04698
Computational frameworks to enable accelerated development of defect-tolerant photovoltaic materials
Autor:
Kurchin, Rachel Chava.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-112).
Widespread adoption of carbon-free
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-112).
Widespread adoption of carbon-free
Externí odkaz:
https://hdl.handle.net/1721.1/122174
Autor:
Annevelink, Emil, Kurchin, Rachel, Muckley, Eric, Kavalsky, Lance, Hegde, Vinay I., Sulzer, Valentin, Zhu, Shang, Pu, Jiankun, Farina, David, Johnson, Matthew, Gandhi, Dhairya, Dave, Adarsh, Lin, Hongyi, Edelman, Alan, Ramsundar, Bharath, Saal, James, Rackauckas, Christopher, Shah, Viral, Meredig, Bryce, Viswanathan, Venkatasubramanian
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials
Externí odkaz:
http://arxiv.org/abs/2011.04426
Publikováno v:
J. Chem. Phys. 153, 134706 (2020)
Electrochemical kinetics at electrode-electrolyte interfaces limit performance of devices including fuel cells and batteries. While the importance of moving beyond Butler-Volmer kinetics and incorporating the effect of electronic density of states of
Externí odkaz:
http://arxiv.org/abs/2007.15756
Autor:
Kurchin, Rachel C., Poindexter, Jeremy R., Vähänissi, Ville, Savin, Hele, del Cañizo, Carlos, Buonassisi, Tonio
Defect-assisted recombination processes are critical to understand, as they frequently limit photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specializ
Externí odkaz:
http://arxiv.org/abs/1912.00956