On-the-fly machine learning for parametrization of the effective Hamiltonian

The first-principles-based effective Hamiltonian is widely used to predict and simulate the properties of ferroelectrics and relaxor ferroelectrics. However, the parametrization method of the effective Hamiltonian is complicated and hardly can resolve the systems with complex interactions and/or complex components. Here, we developed an on-the-fly machine learning approach to parametrize the effective Hamiltonian based on Bayesian linear regression. The parametrization is completed in molecular dynamics simulations, with the energy, forces and stress predicted at each step along with their uncertainties. First-principles calculations are executed when the uncertainties are large to retrain the parameters. This approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered systems including complex systems which previous methods can not handle. BaTiO3 and Pb(Sc,Ta)O3 are taken as examples to show the accurateness of this approach comparing with conventional first-principles parametrization method.Comment: 11 pages, 2 figure

Structural phase transition and material properties of few-layer monochalcogenides

GeSe and SnSe monochalcogenide monolayers and bilayers undergo a two-dimensional phase transition from a rectangular unit cell to a square unit cell at a temperature $T_c$ well below the melting point. Its consequences on material properties are studied within the framework of Car-Parrinello molecular dynamics and density-functional theory. No in-gap states develop as the structural transition takes place, so that these phase-change materials remain semiconducting below and above $T_c$. As the in-plane lattice transforms from a rectangle onto a square at $T_c$, the electronic, spin, optical, and piezo-electric properties dramatically depart from earlier predictions. Indeed, the $Y-$ and $X-$points in the Brillouin zone become effectively equivalent at $T_c$, leading to a symmetric electronic structure. The spin polarization at the conduction valley edge vanishes, and the hole conductivity must display an anomalous thermal increase at $T_c$. The linear optical absorption band edge must change its polarization as well, making this structural and electronic evolution verifiable by optical means. Much excitement has been drawn by theoretical predictions of giant piezo-electricity and ferroelectricity in these materials, and we estimate a pyroelectric response of about $3\times 10^{-12}$ $C/K m$ here. These results uncover the fundamental role of temperature as a control knob for the physical properties of few-layer group-IV monochalcogenidesComment: Supplementary information included. Published versio

The development of ovary in quail’s embryo

The experiment was conducted to study the development of ovary in quails’ embryos which were incubated for 4 to 17 days and incubated out for 1 day. The quails’ embryos or gonads were cut out and HE staining was carried out. The results showed that when embryo was hatched for 4 days, lots of primordial germ cells (PGCs) clustered in the region where gonad would be formed. On the 5th day of hatching, the gonad of the embryo began to be formed and exhibited the feature of ovary or testis. On the 7th hatching day, the right ovary began to degenerate, just a few PGCs began to differentiate into oogonia. On the 10th day, there were many oogonia in the ovary, some of which were surrounded by some other cells distributed like circles. On the 11th day, there were more oogonia, the skinniness became thicker while the medulla was thinner. On the 13th day, the division between skinniness and medulla was obvious and the ovary formed the early original ovum. On the 14th day, more original ovums were seen in the skinniness. On the 17th hatching day and on the 1st day of hatching out, the shape of ovary tended to be mature, also the ovum was clear and more; the medulla was full of vessels. On the 5th hatching day, gonad began to differentiate. On the 7th hatching day and later, thedifferentiation of gonad was obvious; the right ovary began to degenerate. On the 13th hatching day, early original ovum began to be formed in the skinniness of ovary. The results established groundwork for the research of the development of gonads of quail and other poultry.Key words: Quail, embryo, gonad, ovary

Aggregatibacter Actinomycetemcomitans Leukotoxin Cytotoxicity Occurs Through Bilayer Destabilization

The Gram-negative bacterium, Aggregatibacter actinomycetemcomitans, is a common inhabitant of the human upper aerodigestive tract. The organism produces an RTX (Repeats in ToXin) toxin (LtxA) that kills human white blood cells. LtxA is believed to be a membrane-damaging toxin, but details of the cell surface interaction for this and several other RTX toxins have yet to be elucidated. Initial morphological studies suggested that LtxA was bending the target cell membrane. Because the ability of a membrane to bend is a function of its lipid composition, we assessed the proficiency of LtxA to release of a fluorescent dye from a panel of liposomes composed of various lipids. Liposomes composed of lipids that form nonlamellar phases were susceptible to LtxA-induced damage while liposomes composed of lipids that do not form non-bilayer structures were not. Differential scanning calorimetry demonstrated that the toxin decreased the temperature at which the lipid transitions from a bilayer to a nonlamellar phase, while 31P nuclear magnetic resonance studies showed that the LtxA-induced transition from a bilayer to an inverted hexagonal phase occurs through the formation of an isotropic intermediate phase. These results indicate that LtxA cytotoxicity occurs through a process of membrane destabilization. © 2012 Blackwell Publishing Ltd

State estimation for discrete-time neural networks with Markov-mode-dependent lower and upper bounds on the distributed delays

Copyright @ 2012 Springer VerlagThis paper is concerned with the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters and mixed time-delays. The parameters of the neural networks under consideration switch over time subject to a Markov chain. The networks involve both the discrete-time-varying delay and the mode-dependent distributed time-delay characterized by the upper and lower boundaries dependent on the Markov chain. By constructing novel Lyapunov-Krasovskii functionals, sufficient conditions are firstly established to guarantee the exponential stability in mean square for the addressed discrete-time neural networks with Markovian jumping parameters and mixed time-delays. Then, the state estimation problem is coped with for the same neural network where the goal is to design a desired state estimator such that the estimation error approaches zero exponentially in mean square. The derived conditions for both the stability and the existence of desired estimators are expressed in the form of matrix inequalities that can be solved by the semi-definite programme method. A numerical simulation example is exploited to demonstrate the usefulness of the main results obtained.This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60774073 and 61074129, and the Natural Science Foundation of Jiangsu Province of China under Grant BK2010313