25 papers · ranked by Valyu relevance
Anna Corti, Monika Colombo, Francesco Migliavacca, Jose Felix Rodriguez Matas + 2 more
'Jose Felix Rodriguez Matas' 'Stefano Casarin' 'Claudio Chiastra'] The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and…
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…
Matteo Capone, Marco Romanelli, Davide Castaldo, Giovanni Parolin + 3 more
The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first…
Andrew F. Ilersich, Prasanth B. Nair
The physical sciences are replete with dynamical systems that require the resolution of a wide range of length and time scales. This presents significant computational challenges since direct numerical simulation requires discretization at the finest relevant scales, leading to a high-dimensional state space. In this…
Ondrej Krejcar, Hamidreza Namazi
The brain’s complex organization spans from molecular-level processes within neurons to large-scale networks, making it essential to understand this multiscale structure to uncover brain functions and address neurological disorders. Multiscale brain modeling has emerged as a transformative approach, integrating…
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.…
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…
Mark Ashworth, Ahmed H. Elsheikh, Florian Doster
In multiscale modelling, multiple models are used simultaneously to describe scale-dependent phenomena in a system of interest. Here we introduce a machine learning (ML)-based multiscale modelling framework for modelling hierarchical multiscale problems. In these problems, closure relations are required for the…
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…
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…
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…
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…
Yin-Jui Chang, Yuan-I Chen, Hannah M. Stealey, Yi Zhao + 5 more
Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective…
Junfeng Wang, Guohui Li
Computational chemistry methods are playing an increasingly pivotal role in chemical experiments. From quantum chemistry simulations to finite element simulations, researchers can always find an appropriate simulation method to elucidate the specific mechanisms at a certain resolution scale. However, in organic or…
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…
Wei Xie, Viet Ha Hoang, Yin Yang, Yunqing Huang
A recently developed upscaling technique, the multicontinuum homogenization method, has gained significant attention for its effectiveness in modeling complex multiscale systems. This method defines multiple continua based on distinct physical properties and solves a series of constrained cell problems to capture…
T.J. Sego, James P. Sluka, Herbert M. Sauro, James A. Glazier
Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular…
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…
Authors not listed
Self-organizing tissues, such as organoids, offer transformative potential beyond healthcare by enabling the sustainable production of advanced materials. Resource scarcity and global warming drive the need for innovative fabrication solutions. This prospective review explores developmental biology as a manufacturing…
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.…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…