Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Lu, Deyu"'
Autor:
Kwon, Hyuna, Hsu, Tim, Sun, Wenyu, Jeong, Wonseok, Aydin, Fikret, Chapman, James, Chen, Xiao, Carbone, Matthew R., Lu, Deyu, Zhou, Fei, Pham, Tuan Anh
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning meth
Externí odkaz:
http://arxiv.org/abs/2312.05472
Heterostructures of topological insulator Bi$_2$Se$_3$ on transition metal dichalcogenides (TMDCs) offer a new materials platform for studying novel quantum states by exploiting the interplay among topological orders, charge orders and magnetic order
Externí odkaz:
http://arxiv.org/abs/2312.05112
Autor:
Cao, Chuntian, Carbone, Matthew R., Komurcuoglu, Cem, Shekhawat, Jagriti S., Sun, Kerry, Guo, Haoyue, Liu, Sizhan, Chen, Ke, Bak, Seong-Min, Du, Yonghua, Weiland, Conan, Tong, Xiao, Steingart, Dan, Yoo, Shinjae, Artrith, Nongnuch, Urban, Alexander, Lu, Deyu, Wang, Feng
Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in the performance of solid-state batteries. In this study, by combining
Externí odkaz:
http://arxiv.org/abs/2310.00794
Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data
Externí odkaz:
http://arxiv.org/abs/2309.04067
Autor:
Chen, Wei, Ren, Yihui, Kagawa, Ai, Carbone, Matthew R., Chen, Samuel Yen-Chi, Qu, Xiaohui, Yoo, Shinjae, Clyde, Austin, Ramanathan, Arvind, Stevens, Rick L., van Dam, Hubertus J. J., Lu, Deyu
Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidel
Externí odkaz:
http://arxiv.org/abs/2308.01921
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack
Externí odkaz:
http://arxiv.org/abs/2306.16349
Autor:
Meng, Fanchen, Maurer, Benedikt, Peschel, Fabian, Selcuk, Sencer, Hybertsen, Mark, Qu, Xiaohui, Vorwerk, Christian, Draxl, Claudia, Vinson, John, Lu, Deyu
Publikováno v:
Phys. Rev. Materials 8, 013801 (2024)
X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimenta
Externí odkaz:
http://arxiv.org/abs/2303.17089
Autor:
Guo, Haoyue, Carbone, Matthew R., Cao, Chuntian, Qu, Jianzhou, Du, Yonghua, Bak, Seong-Min, Weiland, Conan, Wang, Feng, Yoo, Shinjae, Artrith, Nongnuch, Urban, Alexander, Lu, Deyu
X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crysta
Externí odkaz:
http://arxiv.org/abs/2302.00126
Autor:
Liang, Zhu, Carbone, Matthew R., Chen, Wei, Meng, Fanchen, Stavitski, Eli, Lu, Deyu, Hybertsen, Mark S., Qu, Xiaohui
Publikováno v:
Pyhys. Rev. Materials 7, (2023) 053802
A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra. This method constructs
Externí odkaz:
http://arxiv.org/abs/2301.04724
Autor:
Carbone, Matthew R., Meng, Fanchen, Vorwerk, Christian, Maurer, Benedikt, Peschel, Fabian, Qu, Xiaohui, Stavitski, Eli, Draxl, Claudia, Vinson, John, Lu, Deyu
First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse
Externí odkaz:
http://arxiv.org/abs/2211.04452