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.

Molecular Uniting Set Identified Characteristic ( MUSIC ) of Organic Optoelectronic Materials

Abstract: Researchers investigated the organic optoelectronic materials and facilitated their development in organic light-emitting diodes (OLEDs), chemo- and biosensors, organic solar cells, data storage, and anticounterfeiting devices. Atoms make up molecules through chemical bonds, and molecular aggregates are formed through weak intermolecular interactions. The opto-electronic performance of these materials depends on not only the properties of the well-designed molecules with specific function groups, but also their aggregate states. The molecular aggregates in the form of nanoparticles can be applied in biological imaging, and films can be applied to photovoltaic and photodeformable devices, in which, the alignment of optoelectronic molecules can be either an ordered crystalline or an amorphous state. Generally, the crystalline materials could be deeply investigated by single crystal/powder X-ray diffraction analysis, which could provide the accurate information about molecular conformations, interactions and packing characteristics. It afforded a convenient way to investigate the possible relationship between molecular aggregates and opto-electronic properties. Among various opto-electronic materials, organic room temperature phosphorescence (RTP) materials exhibit the extremely sensitive luminescence property to molecular aggregates, even the dynamic properties can be detected by the tiny change of molecular aggregates. Thus, we selected the organic RTP emission as the output information of molecular aggregates, and afforded typical examples to find the possible relation between RTP effect and molecular packing. Accordingly, molecular packing can be adjusted by the external force as light, mechanical force, temperature, electric field, and so on, as well as the molecular structures as the building blocks, and the systematic investigation in the dynamic and static aggregation structures is of great value to the design of various optoelectronic materials. This review discusses the relationship among molecular structures, aggregation behaviors and corresponding optoelectronic properties by a comprehensive summary of recent research in our group, and the concept of molecular uniting set identified characteristic (MUSIC) is afforded. What is the most favorite and original chemistry developed in your research group? The concept of “Suitable Isolation Group” for molecular design of organic second-order nonlinear optical (NLO) materials. Molecular packing is highlighted as the key point to opto-electronic materials, which are partially summarized in this mini review to present “MUSIC” in the molecular world. How do you supervise your students? Discussions. We do discussions for science, interests and other topics related to research together, through which to solve the encountered problems. What is the most important personality for scientific research? Curiosity, desire to advance, persistence, sense of urgency, and team spirit. What are your hobbies? What's your favorite book(s)?? Listening to music and reading books, and my favorite book is “Tao Te Ching (道德经)”. Could you please give us some advices on improving Chinese Journal of Chemistry? There are many different approaches to improve CJC, perhaps, to attract and publish good papers is the key, especially those from Chinese authors since the chemistry in China develops rapidly. If you have anything else to tell our readers, please feel free to do so? While pursuing new published exciting papers, it is a very good habit to read related old excellent literatures and the classics.

Dendrimers with large nonlinear optical performance by introducing isolation chromophore, utilizing the Ar/ArF self-assembly effect, and modifying the topological structure.

Abstract: By the combination of divergent and convergent approach, a new series of NLO dendrimers (G1-PFPh-NS-GL to G3-PFPh-NS-GL) was conveniently prepared with satisfied yields through the powerful “click chemistry” reaction, in which perfluoroaromatic rings were introduced in the periphery, two types of chromophores were arranged with regular AB structure, and their topological structure was improved to a more spherical shape. All the dendrimers demonstrated good processability, and G1-PFPh-NS-GL exhibited the highest NLO effect of 221 pm/V among the three dendrimers.

I and i

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 …

Fast parallel algorithms for short-range molecular dynamics

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.

Highly accurate protein structure prediction with AlphaFold

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.