scispace - formally typeset
Search or ask a question
Author

Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.


Papers
More filters
Posted Content
TL;DR: Li et al. as discussed by the authors proposed a data-driven method for recovering miss-ing parts of 3D shapes based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network, which takes as input lo-cal 3D patches around missing regions, and progressively produces a highresolution, complete surface through a volumetric encoder-decoder architecture.
Abstract: We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.

23 citations

Journal ArticleDOI
Jun Li1, Zhen Li1, Hongding Tang1, Huiyi Zeng1, Jingui Qin1 
TL;DR: In this article, the synthesis and properties of functionalized polysilanes (FPSs) containing nonlinear optic (NLO) chromophores in the side chain were summarized.

23 citations

Journal ArticleDOI
01 Oct 2017
TL;DR: In this article, the pyrene-fused perylene diimide (PDI) unit was used as a periphery building block to construct non-fullerene acceptors, and the normal devices with the acceptors exhibited ultrahigh open-circuit voltage (Voc) values of up to 1.26
Abstract: Great efforts have been devoted to seeking alternative non-fullerene acceptors for efficient polymer solar cells (PSCs). Achieving a high open-circuit voltage (Voc) is quite challenging but is essential for practical application. Here, pyrene-fused perylene diimide was first used as a periphery building block to construct non-fullerene acceptors. Due to the high-lying lowest unoccupied molecular orbitals (LUMOs) as well as balanced transport properties, the normal devices with the acceptors exhibited ultrahigh Voc values of up to 1.26 V. In addition, the achieved power-conversion efficiency (PCE) of 5.10% is among the highest values for non-fullerene PSCs with a Voc > 1.20 V, indicating that the pyrene-fused perylene diimide (PDI) unit is a useful building block for the design of non-fullerene PSCs with high Voc values. This study could benefit the realization of both a high PCE and Voc in PSCs.

23 citations

Journal ArticleDOI
01 Jul 2013-Fuel
TL;DR: In this article, the authors theoretically study the adsorption of N2 and CH4 on B12 cluster and solid boron surfaces α-B12 and γ-B28.

23 citations

Journal ArticleDOI
Daxin Ou1, Liang Zhang1, Yanfen Huang1, Xiaoding Lou1, Jingui Qin1, Zhen Li1 
TL;DR: A new 6-benzylaminopurine-functionalized disubstituted polyacetylene (P2) with strong green fluorescence is successfully synthesized by utilizing the postfunctional method, making P2 a practical, sensitive, and selective copper and cobalt probe.
Abstract: A new 6-benzylaminopurine-functionalized disubstituted polyacetylene (P2) with strong green fluorescence is successfully synthesized by utilizing the postfunctional method. The polymer is soluble in common organic solvents, and its strong green fluorescence can be quenched by copper and cobalt ions with a detection limit down to 1.0 × 10(-8) (0.64 ppb) and 3.3 × 10(-8) mol L(-1) (1.94 ppb), respectively. Moreover, not much interference is observed from other metal ions, including Li(+) , Na(+) , K(+) , Fe(3+) , Fe(2+) , Ni(2+) , Hg(2+) , Mg(2+) , Al(3+) , Zn(2+) , Mn(2+) , Pb(2+) , Ba(2+) , Ca(2+) , Cd(2+) , Ag(+) , and Cr(3+) . Furthermore, P2 can be put into application using test strips, making P2 a practical, sensitive, and selective copper and cobalt probe.

23 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations