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Massimo Noro is the Director of Business Development at the Science & Technology Facilities Council (STFC), with a focus on the Daresbury Campus. Massimo joined STFC in February 2018, following a successful industrial R&D career at Unilever. His scientific interests focus on applying atomistic and coarse-grained simulations to study the interaction of nano-objects and surfactants with lipid bilayers for industrial applications (e.g. soaps, detergents, etc.). In this interview, he will talk about his journey from academic research, to work in Unilever and now at STFC, and will share his insights on the use of simulation and modelling in industry and the role of STFC and research in this regard.
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E-CAM has built up a collection of (hopefully) useful information to help our community, other Centres of Excellence, and interested groups, transition to online training. The information originates from community-contributed sources and by directly sharing our experience in capturing and broadcasting E-CAM training events.
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An E-CAM transverse action is the development of a protein based sensor with applications in medical diagnostics, scientific visualisation and therapeutics. At the heart of the sensor is a novel protein based molecular switch which allows extremely sensitive real time measurement of molecular targets, and to turn on or off protein functions and other processes accordingly. Provided that the amino acid sequences of antibody -epitope pairs specific to this coronavirus are known, the sensor can be modified to quickly detect the COVID19.
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Featured Software Modules
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Quantum Monte Carlo (QMC) methods are a class of ab initio, stochastic techniques for the study of quantum systems. While QMC simulations are computationally expensive, they have the advantage of being accurate, fully ab initio and scalable to a large number of cores with limited memory requirements. Trial wave functions for electronic QMC computations commonly require the use of single electrons orbitals, typically computed by DFT. The aim of the E-CAM pilot project described here is to build interfaces between the free package for QMC simulations QMCPack and other softwares for electronic structure computations, e.g. the DFT code Quantum Espresso.
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PANNA is a package for training and validating neural networks to represent atomic potentials. It implements configurable all-to-all connected deep neural network architectures which allow for the exploration of training dynamics. A common way to use PANNA in its current implementation is to train a neural network in order to estimate the total energy of a molecule or crystal, as a sum of atomic contributions, by learning from the data of reference total energy calculations for similar structures (usually ab-initio calculations).
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Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first- principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-thoughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM, see SCDM Wannier Functions) and is implemented in an AiiDA workflow.
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J. Castagna, X. Guo, M. Seaton and A. O’Cais, Computer Physics Communications 2020, 251, 107159
DOI: 10.1016/j.cpc.2020.107159 (open access)
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A multi-GPGPU development for Mesoscale Simulations using the Dissipative Particle Dynamics method is presented. This distributed GPU acceleration development is an extension of the DL_MESO package to MPI+CUDA in order to exploit the computational power of the latest NVIDIA cards on hybrid CPU–GPU architectures. Details about the extensively applicable algorithm implementation and memory coalescing data structures are presented. The key algorithms’ optimizations for the nearest-neighbour list searching of particle pairs for short range forces, exchange of data and overlapping between computation and communications are also given. We have carried out strong and weak scaling performance analyses with up to 4096 GPUs. A two phase mixture separation test case with 1.8 billion particles has been run on the Piz Daint supercomputer from the Swiss National Supercomputer Center. With CUDA aware MPI, proper GPU affinity, communication and computation overlap optimizations for multi-GPU version, the final optimization results demonstrated more than 94% efficiency for weak scaling and more than 80% efficiency for strong scaling. As far as we know, this is the first report in the literature of DPD simulations being run on this large number of GPUs.
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Sara Bonella, Alessandro Coretti, Rodolphe Vuilleumier and Giovanni Ciccotti, Phys. Chem. Chem. Phys. 2020, Advance Article
DOI: 10.1039/D0CP00163E
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In recent work [Coretti et al., J. Chem. Phys., 2018, 149, 191102], a new algorithm to solve numerically the dynamics of the shell model for polarization was presented. The approach, broadly applicable to systems involving adiabatically separated dynamical variables, employs constrained molecular dynamics to strictly enforce the condition that the force on the fast degrees of freedom, modeled as having zero mass, is null at each time step. The algorithm is symplectic and fully time reversible, and results in stable and efficient propagation. In this paper we complete the discussion of the mechanics of mass-zero constrained dynamics by showing how to adapt it to problems where the fast degrees of freedom must satisfy additional conditions. This extension includes, in particular, the important case of first principles molecular dynamics. We then consider the statistical mechanics of the mass-zero constrained dynamical system demonstrating that the marginal probability sampled by the dynamics in the physical phase space recovers the form of the Born–Oppenheimer probability density. The effectiveness of the approach and the favorable scaling of the algorithm with system size are illustrated in test calculations of solid Na via orbital-free density functional dynamics.
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Postponed event
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Fernando Luís Barroso da Silva (USP)
Brian Glennon (UCD & SSPC),
Donal MacKernan (UCD),
Erik Santiso (NC State University)
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We are closely monitoring the situation to assess if more events will be affected by the crisis. Alternative options, such as virtual meetings, are also being considered. Information will be promptly updated on the E-CAM and CECAM websites.
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Submitted deliverables
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E-CAM’s Extended Software Development Workshop (ESDW) programme for 2020/2021, and most recent guidelines for the organisation of these events. Analysis of the profile of the participants to our ESDWs and the results of our satisfaction surveys.
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Report on the updates made to the online services in the E-CAM project, including the E-CAM library of software modules, the end users portal, and the online training portal.
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*This is a draft document delivered to the European Commission but not yet approved
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Follow us on social media:
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676531
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