Vibrational spectroscopy by means of first-principles molecular dynamics simulations

Vibrational spectroscopy is one of the most important experimental techniques for the characterization of molecules and materials. Spectroscopic signatures retrieved in experiments are not always easy to explain in terms of the structure and dynamics of the studied samples. Computational studies are a crucial tool for helping to understand and predict experimental results. Molecular dynamics simulations have emerged as an attractive method for the simulation of vibrational spectra because they explicitly treat the vibrational motion present in the compound under study, in particular in large and condensed systems, subject to complex intramolecular and intermolecular interactions. In this context, first-principles molecular dynamics (FPMD) has been proven to provide an accurate realistic description of many compounds. This review article summarizes the field of vibrational spectroscopy by means of FPDM and highlights recent advances made such as the simulation of Infrared, vibrational circular dichroism, Raman, Raman optical activity, sum frequency generation, and nonlinear spectroscopies

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

Large-scale grid-enabled lattice-Boltzmann simulations of complex fluid flow in porous media and under shear

Well designed lattice-Boltzmann codes exploit the essentially embarrassingly parallel features of the algorithm and so can be run with considerable efficiency on modern supercomputers. Such scalable codes permit us to simulate the behaviour of increasingly large quantities of complex condensed matter systems. In the present paper, we present some preliminary results on the large scale three-dimensional lattice-Boltzmann simulation of binary immiscible fluid flows through a porous medium derived from digitised x-ray microtomographic data of Bentheimer sandstone, and from the study of the same fluids under shear. Simulations on such scales can benefit considerably from the use of computational steering and we describe our implementation of steering within the lattice-Boltzmann code, called LB3D, making use of the RealityGrid steering library. Our large scale simulations benefit from the new concept of capability computing, designed to prioritise the execution of big jobs on major supercomputing resources. The advent of persistent computational grids promises to provide an optimal environment in which to deploy these mesoscale simulation methods, which can exploit the distributed nature of compute, visualisation and storage resources to reach scientific results rapidly; we discuss our work on the grid-enablement of lattice-Boltzmann methods in this context.Comment: 17 pages, 6 figures, accepted for publication in Phil.Trans.R.Soc.Lond.

Towards a Holistic CAD Platform for Nanotechnologies

Silicon-based CMOS technologies are predicted to reach their ultimate limits by the middle of the next decade. Research on nanotechnologies is actively conducted, in a world-wide effort to develop new technologies able to maintain the Moore's law. They promise revolutionizing the computing systems by integrating tremendous numbers of devices at low cost. These trends will have a profound impact on the architectures of computing systems and will require a new paradigm of CAD. The paper presents a work in progress on this direction. It is aimed at fitting requirements and constraints of nanotechnologies, in an effort to achieve efficient use of the huge computing power promised by them. To achieve this goal we are developing CAD tools able to exploit efficiently these huge computing capabilities promised by nanotechnologies in the domain of simulation of complex systems composed by huge numbers of relatively simple elements.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

PRISE: An Integrated Platform for Research and Teaching of Critical Embedded Systems

In this paper, we present PRISE, an integrated workbench for Research and Teaching of critical embedded systems at ISAE, the French Institute for Space and Aeronautics Engineering. PRISE is built around state-of-the-art technologies for the engineering of space and avionics systems used in Space and Avionics domain. It aims at demonstrating key aspects of critical, real-time, embedded systems used in the transport industry, but also validating new scientific contributions for the engineering of software functions. PRISE combines embedded and simulation platforms, and modeling tools. This platform is available for both research and teaching. Being built around widely used commercial and open source software; PRISE aims at being a reference platform for our teaching and research activities at ISAE

Photonic reservoir computing: a new approach to optical information processing

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks and has been successfully used in several pattern classification problems, like speech and image recognition. The implementations have so far been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance

Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed

Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values