The upper critical magnetic field of holographic superconductor with conformally invariant power-Maxwell electrodynamics

The properties of $(d-1)$-dimensional $s$-wave holographic superconductor in the presence of power-Maxwell field is explored. We study the probe limit in which the scalar and gauge fields do not backreact on the background geometry. Our study is based on the matching of solutions on the boundary and on the horizon at some intermediate point. At first, the case without external magnetic field is considered, and the critical temperature is obtained in terms of the charge density, the dimensionality, and the power-Maxwell exponent. Then, a magnetic field is turned on in the $d$-dimensional bulk which can influence the $(d-1)$-dimensional holographic superconductor at the boundary. The phase behavior of the corresponding holographic superconductor is obtained by computing the upper critical magnetic field in the presence of power-Maxwell electrodynamics, characterized by the power exponent $q$. Interestingly, it is observed that in the presence of magnetic field, the physically acceptable phase behavior of the holographic superconductor is obtained for $q={d}/{4}$, which guaranties the conformal invariance of the power-Maxwell Lagrangian. The case of physical interest in five spacetime dimensions ($d=5$, and $q=5/4$) is considered in detail, and compared with the results obtained for the usual Maxwell electrodynamics $q=1$ in the same dimensions.Comment: 12 pages, 1 table, 5 figure

REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information