Shuaihua Lu, Qionghua Zhou, Yixin Ouyang, Yilv Guo et al.
Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs)...
Bing Wang, Qisheng Wu, Yehui Zhang, Yilv Guo et al.
monolayer (45 K) and the boiling point of liquid nitrogen (77 K). Moreover, a small amount of hole doping can induce a transition from a ferromagnetic metal to a half-metal. Furthermore, the ScCl monolayer possesses excellent thermal and dynamical stabilities as well as feasibility of experimental e...
Yilei Wu, Changfeng Wang, Ming‐Gang Ju, Qiang‐Qiang Jia et al.
The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally mu...
Xinyu Chen, Shuaihua Lu, Qian Chen, Qionghua Zhou et al.
Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend ...
Zhilong Song, Linfeng Fan, Shuaihua Lu, Chongyi Ling et al.
Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation...
Qiang Li, Xiaowan Bai, Chongyi Ling, Qionghua Zhou et al.
Abstract Atomically dispersed supported catalysts show superior catalytic activity and selectivity in diverse reactions, while the challenging part is identifying the active sites and revealing the reaction mechanisms, which play essential roles in rational design of efficient catalysts to massive e...
Zhilong Song, Shuaihua Lu, Ming‐Gang Ju, Qionghua Zhou et al.
Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herei...
Qionghua Zhou, Qiang Li, Shijun Yuan, Qian Chen et al.
and He into BP. Moreover, the molecule intercalated BP maintains high hole mobility, which makes it a better two-dimensional semiconductor for practical applications.
Xinyu Chen, Zhilong Song, Shuaihua Lu, Qian Chen et al.
Generative artificial intelligence is transforming materials discovery by creating unexplored candidates. However, such models are trapped in historical data bias, particularly for data-scarce systems like two-dimensional (2D) materials, leading to repetitive outputs rather than genuine discoveries....
Qionghua Su, Dongqi Wu, Huanyi Zhou, Ye Yang et al.
ABSTRACT Ultraviolet (UV) radiation greatly affects the stability of perovskite solar cells (PSCs), limiting their application in extreme environments such as plateaus and deserts. Herein, the antioxidant [triethylene glycol bis (3‐tert‐butyl‐4‐hydroxy‐5‐methylphenyl) propionate] (AO‐245) is introdu...
Xiaoshu Gong, Xinyang Li, Jie Ji, Qionghua Zhou et al.
ABSTRACT The emerging 2D semiconductors are promising candidates for beyond‐silicon electronics because of their atomic‐scale thickness, high carrier mobility at the 2D limit, and so on. Achieving semiconductor–metal (S–M) ohmic contact with low resistance is crucial for high‐performance electronics...