Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Chen, Zhantao"'
Autor:
Liu, Fangze, Chen, Zhantao, Liu, Tianyi, Song, Ruyi, Lin, Yu, Turner, Joshua J., Jia, Chunjing
Drawing inspiration from the achievements of natural language processing, we adopt self-supervised learning and utilize an equivariant graph neural network to develop a unified platform designed for training generative models capable of generating cr
Externí odkaz:
http://arxiv.org/abs/2312.14485
Autor:
Chen, Zhantao, Peng, Cheng, Petsch, Alexander N., Chitturi, Sathya R., Okullo, Alana, Chowdhury, Sugata, Yoon, Chun Hong, Turner, Joshua J.
Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To add
Externí odkaz:
http://arxiv.org/abs/2306.02015
Autor:
Chitturi, Sathya, Ji, Zhurun, Petsch, Alexander, Peng, Cheng, Chen, Zhantao, Plumley, Rajan, Dunne, Mike, Mardanya, Sougata, Chowdhury, Sugata, Chen, Hongwei, Bansil, Arun, Feiguin, Adrian, Kolesnikov, Alexander, Prabhakaran, Dharmalingam, Hayden, Stephen, Ratner, Daniel, Jia, Chunjing, Nashed, Youssef, Turner, Joshua
The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor,
Externí odkaz:
http://arxiv.org/abs/2304.03949
Autor:
Thayer, Jana1 jana@slac.stanford.edu, Chen, Zhantao1, Claus, Richard1, Damiani, Daniel1, Ford, Christopher1, Dubrovin, Mikhail1, Elmir, Victor1, Kroeger, Wilko1, Li, Xiang1, Marchesini, Stefano1, Mariani, Valerio1, Melcchiori, Riccardo1, Nelson, Silke1, Peck, Ariana2, Perazzo, Amedeo1, Poitevin, Frederic1, O'Grady, Christopher Paul1, Otero, Julieth1, Quijano, Omar1, Shankar, Murali1
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-12. 12p.
Autor:
Chen, Zhantao, Shen, Xiaozhe, Andrejevic, Nina, Liu, Tongtong, Luo, Duan, Nguyen, Thanh, Drucker, Nathan C., Kozina, Michael E., Song, Qichen, Hua, Chengyun, Chen, Gang, Wang, Xijie, Kong, Jing, Li, Mingda
One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computation lead to mode-based information, it is hindered by unknown defects
Externí odkaz:
http://arxiv.org/abs/2202.06199
Autor:
Liu, Fangze, Chen, Zhantao, Liu, Tianyi, Song, Ruyi, Lin, Yu, Turner, Joshua J., Jia, Chunjing
Publikováno v:
In iScience 20 September 2024 27(9)
Autor:
Andrejevic, Nina, Chen, Zhantao, Nguyen, Thanh, Fan, Leon, Heiberger, Henry, Zhou, Ling-Jie, Zhao, Yi-Fan, Chang, Cui-Zu, Grutter, Alexander, Li, Mingda
Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function l
Externí odkaz:
http://arxiv.org/abs/2109.08005
Autor:
Drucker, Nathan C., Nguyen, Thanh, Han, Fei, Luo, Xi, Andrejevic, Nina, Zhu, Ziming, Bednik, Grigory, Nguyen, Quynh T., Chen, Zhantao, Nguyen, Linh K., Williams, Travis J., Stone, Matthew B., Kolesnikov, Alexander I., Chi, Songxue, Fernandez-Baca, Jaime, Hogan, Tom, Alatas, Ahmet, Puretzky, Alexander A., Geohegan, David B., Huang, Shengxi, Yu, Yue, Li, Mingda
The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. Howeve
Externí odkaz:
http://arxiv.org/abs/2103.08489
Autor:
Chen, Zhantao, Andrejevic, Nina, Drucker, Nathan, Nguyen, Thanh, Xian, R Patrick, Smidt, Tess, Wang, Yao, Ernstorfer, Ralph, Tennant, Alan, Chan, Maria, Li, Mingda
Publikováno v:
Chem. Phys. Rev. 2, 031301 (2021)
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of
Externí odkaz:
http://arxiv.org/abs/2102.03024
Autor:
Chen, Zhantao, Andrejevic, Nina, Smidt, Tess, Ding, Zhiwei, Chi, Yen-Ting, Nguyen, Quynh T., Alatas, Ahmet, Kong, Jing, Li, Mingda
Publikováno v:
Advanced Science 202004214 (2021)
Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The chall
Externí odkaz:
http://arxiv.org/abs/2009.05163