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 …

I and i

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.

Fast parallel algorithms for short-range molecular dynamics

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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.

/pdf/highly-accurate-protein-structure-prediction-with-alphafold-2xjbzfwu3n.pdf

Highly accurate protein structure prediction with AlphaFold

Organic light-emitting diodes (OLED) have attracted increasing attention due to their excellent properties, such as self-luminosity, high color gamut and flexibility, and potential applications in display, wearable devices and lighting. The emitters are the most important composition in OLEDs, mainly classified into fluorescent compounds (first generation), metal phosphorescent complexes (second generation), and thermally activated delayed fluorescence (TADF) materials (third generation). In this review, we summarize the advances of novel emitters of organic metal complexes in the last decade, focusing on coinage metals (Cu, Ag, and Au) and non-precious metals (Al, Zn, W, and alkali metal). Also, the design strategy of d10 and Au(III) complexes was discussed. We aim to provide guidance for exploring efficient metal complexes beyond traditional phosphorescent complexes.

Diversity of Luminescent Metal Complexes in OLEDs: Beyond Traditional Precious Metals.

A thermal environmental analysis method for data centers

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/sstr.202000059

Zhen Li

Papers

Diversity of Luminescent Metal Complexes in OLEDs: Beyond Traditional Precious Metals.

A thermal environmental analysis method for data centers

Methods and Cost Estimation for the Synthesis of Nanosized Lithium Sulfide

One-pot preparation of cross-linked amphiphilic fluorescent polymer based on aggregation induced emission dyes

Controllable synthesis and photocatalytic activities of water-soluble TiO2 nanoparticles