<\/p>\n\n\n\n
<\/p>\n\n\n\n
Dynamic topological materials<\/h3>\n\n\n\n\n\n![\"\"](\"\/wp-content\/uploads\/2020\/12\/\u5c4f\u5e55\u622a\u56fe-2020-12-25-231055.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nOptical driving is a good way to excite new state of matter and tune the properties of materials in non-equilibrium. Based on the TDDFT and master equations, we explore the transport properties of massless and massive Dirac fermion systems under the photon fields. We argue the non-trivial Hall current have both contributions from optical field driven Berry curvature and charge imbalances.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
\n- Nature 614, 75-80 (2023).<\/a><\/em><\/li>\n\n\n\n
- Nature Reviews Physics 4, 33-48 (2022).<\/a><\/em><\/li>\n\n\n\n
- New Journal of Physics 21, 093005 (2019).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 99, 214302 (2019).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
Topological Magnetic materials<\/h3>\n\n\n\n\n\n\n \n \n \n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/4-1-700x300.jpg\" "\\"4\\"")
<\/li>\n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/5-700x300.jpg\" "\\"5\\"")
<\/li>\n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/3-1-700x300.jpg\" "\\"3\\"")
<\/li>\n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/1-1-700x300.jpg\" "\\"1\\"")
<\/li>\n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/2-1-700x300.jpg\" "\\"2\\"")
<\/li>\n ![\"\"](\"\/wp-content\/uploads\/2020\/12\/6-700x300.jpg\" "\\"6\\"")
<\/li>\n <\/ul>\n <\/div>\n \n <\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n\nThe Dancing between topological materials and magnetism will bring new concepts and phenomena to exotic materials, which are key for the new applications in spintronic devices. Based on theoretical tools (DFT and kp theory), our group mainly focus on the following topics.<\/p>\n<\/div>\n<\/div>\n\n\n\n
\u00b7 AFM topological systems<\/h5>\n\n\n\n\n\n![\"\"](\"\/wp-content\/uploads\/2020\/12\/AFM.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nAntiferromagnetic topological materials have showed great possibilities in not only research of the interplay of Dirac fermion physics and magnetism, but new-generation low-power memory design. We predicted AFM topological fermions in CuMnAs<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
\n- Physical Review Research 2 (2), 022025 (2020).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 96, 224405 (2017).<\/a><\/em><\/li>\n\n\n\n
- Nature Physics 12, 1100 (2016).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
\u00b7 Topological Material Engineering<\/h5>\n\n\n\n\n\n![\"\"](\"\/wp-content\/uploads\/2020\/12\/\u5c4f\u5e55\u622a\u56fe-2020-12-25-225413.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nDue to the strong SOC in topological materials, the degrees of freedom of spin and momentum could couple together. Via using the symmetry arguments, we design ways to engineer magnetic and topological properties.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
\n- Physical Review B 102, 201405 (2020).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 98, 245108 (2018).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 115, 136801 (2015).<\/a><\/em><\/li>\n\n\n\n
- Nano Letters 15, 2031 (2015).<\/a><\/em><\/li>\n\n\n\n
- Science 339, 1582 (2013).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 111, 116601 (2013).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
\u00b7 New Topological Materials<\/h5>\n\n\n\n\n\n![\"\"](\"\/wp-content\/uploads\/2020\/12\/NTM-1.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nWe predicted the new fermions in CoSi and many new topological materials, including 2D quantum spin Hall insulator in dumbbell stanene, the Dirac fermion in hexagonal ABC compound LiZnBi, weak TI in BiTeI-Bi2-BiTeI sandwiched structure and so on. Those topological materials are equipped with many exotic quantum properties and provide material candidates for novel physics and future applications.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
\n- Physical Review Letters 119, 206402 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 96, 115203 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 93, 241117(R) (2016).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B (R) 90, 121408 (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 89, 041409(R) (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 112, 056801 (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 111, 136804 (2013).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
Machine learning for material sciences<\/h3>\n\n\n\n\n\n![\"\"](\"\/wp-content\/uploads\/2020\/12\/\u5c4f\u5e55\u622a\u56fe-2020-12-25-231814.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nExciting advances have been made in artificial intelligence (AI) during recent decades. Among them, machine learning and deep learning techniques are widely used in almost all parts of science. Our group engages in applying machine learning to material science, makes full use of neural networks and other machine learning models, and expects to find machines to learn the basic properties of atoms by themselves from the extensive database of known compounds and materials.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
<\/figure>\n<\/div>\n\n\n\nOptical driving is a good way to excite new state of matter and tune the properties of materials in non-equilibrium. Based on the TDDFT and master equations, we explore the transport properties of massless and massive Dirac fermion systems under the photon fields. We argue the non-trivial Hall current have both contributions from optical field driven Berry curvature and charge imbalances.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
- \n
- Nature 614, 75-80 (2023).<\/a><\/em><\/li>\n\n\n\n
- Nature Reviews Physics 4, 33-48 (2022).<\/a><\/em><\/li>\n\n\n\n
- New Journal of Physics 21, 093005 (2019).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 99, 214302 (2019).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
Topological Magnetic materials<\/h3>\n\n\n\n
\n\n\n\n\n- \n
- <\/li>\n
- <\/li>\n
- <\/li>\n
- <\/li>\n
- <\/li>\n
- <\/li>\n <\/ul>\n <\/div>\n \n <\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n\n
The Dancing between topological materials and magnetism will bring new concepts and phenomena to exotic materials, which are key for the new applications in spintronic devices. Based on theoretical tools (DFT and kp theory), our group mainly focus on the following topics.<\/p>\n<\/div>\n<\/div>\n\n\n\n
\u00b7 AFM topological systems<\/h5>\n\n\n\n
\n\n<\/figure>\n<\/div>\n\n\n\n\nAntiferromagnetic topological materials have showed great possibilities in not only research of the interplay of Dirac fermion physics and magnetism, but new-generation low-power memory design. We predicted AFM topological fermions in CuMnAs<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
- \n
- Physical Review Research 2 (2), 022025 (2020).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 96, 224405 (2017).<\/a><\/em><\/li>\n\n\n\n
- Nature Physics 12, 1100 (2016).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
\u00b7 Topological Material Engineering<\/h5>\n\n\n\n
\n\n<\/figure>\n<\/div>\n\n\n\n\nDue to the strong SOC in topological materials, the degrees of freedom of spin and momentum could couple together. Via using the symmetry arguments, we design ways to engineer magnetic and topological properties.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
- \n
- Physical Review B 102, 201405 (2020).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 98, 245108 (2018).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 115, 136801 (2015).<\/a><\/em><\/li>\n\n\n\n
- Nano Letters 15, 2031 (2015).<\/a><\/em><\/li>\n\n\n\n
- Science 339, 1582 (2013).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 111, 116601 (2013).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
\u00b7 New Topological Materials<\/h5>\n\n\n\n
\n\n<\/figure>\n<\/div>\n\n\n\n\nWe predicted the new fermions in CoSi and many new topological materials, including 2D quantum spin Hall insulator in dumbbell stanene, the Dirac fermion in hexagonal ABC compound LiZnBi, weak TI in BiTeI-Bi2-BiTeI sandwiched structure and so on. Those topological materials are equipped with many exotic quantum properties and provide material candidates for novel physics and future applications.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
- \n
- Physical Review Letters 119, 206402 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 96, 115203 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 93, 241117(R) (2016).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B (R) 90, 121408 (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 89, 041409(R) (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 112, 056801 (2014).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Letters 111, 136804 (2013).<\/a><\/em><\/li>\n<\/ul>\n\n\n\n
Machine learning for material sciences<\/h3>\n\n\n\n
\n\n<\/figure>\n<\/div>\n\n\n\n\nExciting advances have been made in artificial intelligence (AI) during recent decades. Among them, machine learning and deep learning techniques are widely used in almost all parts of science. Our group engages in applying machine learning to material science, makes full use of neural networks and other machine learning models, and expects to find machines to learn the basic properties of atoms by themselves from the extensive database of known compounds and materials.<\/p>\n<\/div>\n<\/div>\n\n\n\n
References:<\/p>\n\n\n\n
- Physical Review B 96, 115203 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 98, 245108 (2018).<\/a><\/em><\/li>\n\n\n\n
- Physical Review B 96, 224405 (2017).<\/a><\/em><\/li>\n\n\n\n
- Physical Review Research 2 (2), 022025 (2020).<\/a><\/em><\/li>\n\n\n\n
- Nature Reviews Physics 4, 33-48 (2022).<\/a><\/em><\/li>\n\n\n\n