Theory for trivial trajectory parallelization of multicanonical molecular dynamics and application to a polypeptide in water

Trivial trajectory parallelization of multicanonical molecular dynamics (TTP-McMD) explores the conformational space of a biological system with multiple short runs of McMD starting from various initial structures. This method simply connects (i.e., trivially parallelizes) the short trajectories and generates a long trajectory. First, we theoretically prove that the simple trajectory connection satisfies a detailed balance automatically. Thus, the resultant long trajectory is regarded as a single multicanonical trajectory. Second, we applied TTP-McMD to an alanine decapeptide with an all-atom model in explicit water to compute a free-energy landscape. The theory imposes two requirements on the multiple trajectories. We have demonstrated that TTP-McMD naturally satisfies the requirements. The TTP-McMD produces the free-energy landscape considerably faster than a single-run McMD does. We quantitatively showed that the accuracy of the computed landscape increases with increasing the number of multiple runs. Generally, the free-energy landscape of a large biological system is unknown a priori. The current method is suitable for conformational sampling of such a large system to reduce the waiting time to obtain a canonical ensemble statistically reliable.     

二氧化碳/甲烷/氮气二元混合物在有序介孔碳材料CMK-3 中的吸附和分离

采用分子模拟与吸附理论研究了天然气成分在有序介孔碳材料CMK-3上的吸附和分离.巨正则系综蒙特卡罗(GCMC)模拟表明,CH4和CO2气体的较优存储条件分别为208 K、4 MPa和298 K、6 MPa,其最大超额吸附量分别为10.07和14.85 mmol· g-1.基于双位Langmuir-Freundlich (DSLF)模型,使用理想吸附溶液理论(IAST)预测了不同二元混合物在CMK-3中的分离行为,发现吸附选择性Sco2/CH4与ScH4/N2比较接近,在298 K和4 MPa下约等于3,而N2-CO2体系中的CO2吸附选择性较高,可达到7.5,说明CMK-3是一种适合吸附和分离天然气组分的碳材料.     

Structural properties of effective potential model by liquid state theories

This paper investigates the structural properties of a model fluid dictated by an effective inter-particle oscillatory potential by grand canonical ensemble Monte Carlo (GCEMC) simulation and classical liquid state theories.The chosen oscillatory potential incorporates basic interaction terms used in modeling of various complex fluids which is composed of mesoscopic particles dispersed in a solvent bath,the studied structural properties include radial distribution function in bulk and inhomogeneous density distribution profile due to influence of several external fields.The GCEMC results are employed to test the validity of two recently proposed theoretical approaches in the field of atomic fluids.One is an Ornstein-Zernike integral equation theory approach;the other is a third order + second order perturbation density functional theory.Satisfactory agreement between the GCEMC simulation and the pure theories fully indicates the ready adaptability of the atomic fluid theories to effective model potentials in complex fluids,and classifies the proposed theoretical approaches as convenient tools for the investigation of complex fluids under the single component macro-fluid approximation.     

Thermodynamic Properties of the 1D Robin Quantum Well

The thermodynamic properties of the Robin quantum well with extrapolation length Λ are analyzed theoretically both for the canonical and two grand canonical ensembles, with special attention being paid to the situation when the energies of one or two lowest‐lying states are split off from the rest of the spectrum by the large gap that is controlled by the varying Λ. For the single split‐off level, which exists for the geometry with the equal magnitudes but opposite signs of the Robin distances on the confining interfaces, the heat capacity of the canonical averaging is a nonmonotonic function of the temperature T with its salient maximum growing to infinity as for the decreasing to zero extrapolation length and its position being proportional to . The specific heat per particle of the Fermi–Dirac ensemble depends nonmonotonically on the temperature too, with its pronounced extremum being foregone on the T axis by the plateau, whose value at the dying Λ is , with N being the number of fermions. The maximum of , similar to the canonical averaging, unrestrictedly increases as Λ goes to zero and is largest for one particle. The most essential property of the Bose–Einstein ensemble is the formation, for a growing number of bosons, of the sharp asymmetric shape on the characteristics, which is more protrusive at the smaller Robin distances. This cusp‐like structure is a manifestation of the phase transition to the condensate state. For two split‐off orbitals, one additional maximum emerges whose position is shifted to colder temperatures with the increase of the energy gap between these two states and their higher‐lying counterparts and whose magnitude approaches a Λ‐independent value. All these physical phenomena are qualitatively and quantitatively explained by the variation of the energy spectrum by the Robin distance. Parallels with other structures are drawn and similarities and differences between them are highlighted. Generalization to higher dimensions is also provided.     

基于Lorenz模型的集合预报与单一预报的比较研究

根据非线性局部Lyapunov向量方法和增长模繁殖方法,选取Lorenz63模型和Lorenz96模型的不同状态为例,对集合预报与单一预报的预报技巧开展了对比研究.结果表明:与单一预报比较,集合预报的均方根误差和型异常相关有明显改善,随预报时间推移,改善效果越显著,且集合平均优于单一预报的实验个例数逐渐增多.就概率分布(f)而言,单一预报状态的f与真实状态基本一致,不随时间变化;而集合平均预报状态的f则随时间呈现出值域变窄、峰值变大的特点.表明随预报时间的延长,单一预报状态为混沌吸引子上的随机状态,而集合平均预报状态为吸引子子集上的随机状态,这可能是集合平均误差小于单一预报的原因.     

The forces on a single interacting Bose–Einstein condensate

Using double parabola approximation for a single Bose–Einstein condensate confined between double slabs we proved that in grand canonical ensemble (GCE) the ground state with Robin boundary condition (BC) is favored, whereas in canonical ensemble (CE) our system undergoes from ground state with Robin BC to the one with Dirichlet BC in small-L region and vice versa for large-L region and phase transition in space of the ground state is the first order. The surface tension force and Casimir force are also considered in both CE and GCE in detail.     

Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F₂-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.     

一种获取三肽构象系综的可靠量子化学方法

三肽是蛋白质的基本组成模块, 具有重要生理功能. 解析其结构对于更大的肽及蛋白质研究具有重要意义. 基于二面角组合规则, 结合键旋转手段, 提出一种获取三肽构象系综的从头算量子化学方法. 使用该方法对八个目标三肽的势能面进行彻底搜索, 将所得结果与以前的预测方法及蛋白质数据库(PDB) 提取的结构进行比较.结果表明, 新方法搜索到了最完整的三肽构象系综. 此外证明了 PDB 结构存在缺陷, 遗漏了大部分中低能区的重要构象. 将新获取的三肽结构用于红外光谱研究, 理论结果与实验数据符合得更精准.