23 papers · ranked by Valyu relevance
Jógvan Magnus Haugaard Olsen, Viacheslav Bolnykh, Simone Meloni, Emiliano Ippoliti + 3 more
We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through the use of MPI intercommunicators. This allows exploiting the…
Alfons G. Hoekstra, Bastien Chopard, David Coster, Simon Portegies Zwart + 1 more
'Simon Portegies Zwart' 'Peter V. Coveney'] In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a…
Andrew Millar-Wilson, Órla Ward, Eolann Duffy, Gary Hardiman
Title: Summary A central tenet of systems biology is that biological systems are greater than the sum of their component parts. Spaceflight is associated with hazards including radiation exposure and microgravity which impact different echelons of biological organizations spanning molecular, cellular, organ, and…
Derek Groen, Jaroslaw Knap, Philipp Neumann, Diana Suleimenova + 2 more
In the last few decades, multiscale modelling has emerged as one of the dominant modelling paradigms in many areas of science and engineering. Its rise to dominance is primarily driven by advancements in computing power and the need to model systems of increasing complexity. The multiscale modelling paradigm is now…
Derek Groen, Stefan J. Zasada, Peter V. Coveney
—Multiscale and multiphysics applications are now commonplace, and many researchers focus on combining existing models to construct combined multiscale models. Here we present a concise review of multiscale applications and their source communities. We investigate the prevalence of multiscale projects in the EU and the…
Pinaki Bhattacharya, Qiao Li, Damien Lacroix, Visakan Kadirkamanathan + 2 more
'Visakan Kadirkamanathan' 'Marco Viceconti' 'Ryan K. Roeder'] Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges…
Pariksheet Nanda, Maral Budak, Christian T. Michael, Kathryn Krupinsky + 1 more
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex…
Phong Nguyen, Joseph B. Choi, H. S. Udaykumar, Stephen Baek
Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either…
Lionel Kusch, Sandra Diaz, Wouter Klijn, Kim Sontheimer + 3 more
Integration of information across heterogeneous sources creates added scientific value. It is, however, a challenge to progress, often a barrier, to interoperate data, tools and models across spatial and temporal scales. Here we present a design template for coupling simulators operating at different scales and…
Huandong Wang, Huan Yan, Can Rong, Yuan Yuan + 5 more
'Zhenyu Han' 'Hongjie Sui' 'Depeng Jin' 'Yong Li'] Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome…
Omar Lakkis, Adrian Muntean, Omar Richardson, Chandrasekhar Venkataraman
'Chandrasekhar Venkataraman'] We propose a two-scale finite element method designed for heterogeneous microstructures. Our approach exploits domain diffeomorphisms between the microscopic structures to gain computational efficiency. By using a conveniently constructed pullback operator, we are able to model the…
Pinyi Lu, Vida Abedi, Yongguo Mei, Raquel Hontecillas + 3 more
'Adria Carbo' 'Josep Bassaganya-Riera'] Background Modeling of the immune system - a highly non-linear and complex system - requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils…
Jean-Louis Palgen, Angélique Perrillat-Mercerot, Nicoletta Ceres, Emmanuel Peyronnet + 6 more
Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data.…
Mario Lino, Chris D. Cantwell, Anil A. Bharath, Stathi Fotiadis
Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance often limits application in practice. Recent modern machine learning approaches have demonstrated their ability to accelerate…
Mario Lino, Stathi Fotiadis, Anil A. Bharath, Chris D. Cantwell
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal predictions, although, with only moderate accuracy in comparison. Here we introduce…
Authors not listed
Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum desription with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding…
Barak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal + 11 more
Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each…
Sonata Kvedaravičiūtė, Andrej Antalík, Olivier Adjoua, Thomas Plé + 4 more
In this work, we present the development of a fully-polarizable KS-DFT/AMOEBA embedding scheme for delocalized basis sets such as plane-waves and real-space grids. The augmented problem of electron spill-out inherent to a polarizable QM/MM implementation with plane-wave basis sets is addressed and the periodicity for…
Authors not listed
We present the next generation of AMP, a neural network potential (NNP) with anisotropic message passing designed to study large biomolecular systems at DFT accuracy in the condensed phase using a multiscale approach similar to quantum-mechanics/molecular-mechanics (QM/MM) with electrostatic embedding. We trained AMPv3…
Christopher Schölzel, Valeria Blesius, Gernot Ernst, Andreas Dominik
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an…
Authors not listed
The interaction of CO2 with Ni catalysts is important for many industrial processes like methanation, which is known to be structure sensitive. Consequently, structure-dependent multiscale modeling is required to accurately capture the interaction of CO2 with the various Ni facets and to provide accurate atomistic…
Zhenghao Wu, Tianhang Zhou
In the realm of multiscale molecular simulations, structure-based coarse graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems such as polymers. This involves optimizing CG interactions by matching static correlation functions of corresponding degrees of…
Authors not listed
Discovery-oriented research is a fundamental pursuit in chemical and materials science, especially when objective-free or purpose-ambiguous exploration can yield unexpected novel compounds or materials. Recently, data-driven objective-free exploration methods have emerged to support such discovery in materials science.…