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.

Organic Dyes based on Tetraaryl-1,4-dihydropyrrolo-[3,2-b]pyrroles for Photovoltaic and Photocatalysis Applications with the Suppressed Electron Recombination.

Abstract: A tetraaryl-1,4-dihydropyrrolo-[3,2-b]pyrroles (TAPP) moiety with the combination of two pyrrole rings and four phenyl moieties demonstrated strong electron-donating ability and nonplanar configuration simultaneously. Once incorporated into the organic dyes as a novel electron donor, it was beneficial for the enhancement of light-harvesting ability and suppression of electron recombination in the photovoltaic and photocatalysis systems. With the linkage of tunable conjugated bridges and electron acceptor, the corresponding organic dyes exhibited improved photovoltaic performance in dye-sensitized solar cells and facilitated photocatalytic hydrogen generation with a highest turnover number (TON) of 4337. Through the detailed investigation of relationship between molecular structures and photovoltaic/photocatalysis property, the connection and difference in molecular design for these two systems are well explained, with the aim to promote the application of dye-sensitized technology in various fields.

A computational study of carbon dioxide adsorption on solid boron

Abstract: Capturing and sequestering carbon dioxide (CO2) can provide a route to partial mitigation of climate change associated with anthropogenic CO2 emissions. Here we report a comprehensive theoretical study of CO2 adsorption on two phases of boron, α-B12 and γ-B28. The theoretical results demonstrate that the electron deficient boron materials, such as α-B12 and γ-B28, can bond strongly with CO2 due to Lewis acid-base interactions because the electron density is higher on their surfaces. In order to evaluate the capacity of these boron materials for CO2 capture, we also performed calculations with various degrees of CO2 coverage. The computational results indicate CO2 capture on the boron phases is a kinetically and thermodynamically feasible process, and therefore from this perspective these boron materials are predicted to be good candidates for CO2 capture.

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.