Simple synthetic molecular dynamics for efficient trajectory generationSynthetic molecular dynamics (synMD) trajectories from learned generative models have been proposed as a useful addition to the biomolecular simulation toolbox. The computational expense of…John D. Russo, Daniel M. Zuckerman·Apr 9, 2022SaveLearn
Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteriesSulfide-based glasses and glass-ceramics showing high ionic conductivities and excellent mechanical properties are considered as promising solid-state electrolytes. Nowadays, the computational…Zhenming Xu, Yongyao Xia·Apr 8, 2022SaveLearn
Parallelized Domain Decomposition for Multi-Dimensional Lagrangian Random Walk, Mass-Transfer Particle Tracking SchemesWe develop a multi-dimensional, parallelized domain decomposition strategy (DDC) for mass-transfer particle tracking (MTPT) methods. These methods are a type of Lagrangian algorithm for simulating…Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl et al.·Apr 7, 2022SaveLearn
Computation of the Time-Dependent Dirac Equation with Physics-Informed Neural NetworksWe propose to compute the time-dependent Dirac equation using physics-informed neural networks (PINNs), a new powerful tool in scientific machine learning avoiding the use of approximate derivatives…Emmanuel Lorin, Xu Yang·Apr 6, 2022SaveLearn
GROMACS Stochastic Dynamics and BAOAB are equivalent configurational sampling algorithmsTwo of the most widely used Langevin integrators for molecular dynamics simulations are the GROMACS Stochastic Dynamics (GSD) integrator and the splitting method BAOAB. In this letter, we show that…Stefanie Kieninger, Bettina G. Keller·Apr 5, 2022SaveLearn
Application of a Spectral Method to Simulate Quasi-Three-Dimensional Underwater Acoustic FieldsThe calculation of a three-dimensional underwater acoustic field has always been a key problem in computational ocean acoustics. Traditionally, this solution is usually obtained by directly solving…Houwang Tu, Yongxian Wang, Wei Liu et al.·Apr 5, 2022SaveLearn
Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver ChalcogenidesWe propose a data-driven approach for constructing machine-learning interatomic potentials (MLIPs) trained under a regularization with the aim of avoiding nonphysical heat flux. Specifically, we…Kohei Shimamura, Koura Akihide, Fuyuki Shimojo·Apr 4, 2022SaveLearn
Evaluation of finite difference based asynchronous partial differential equations solver for reacting flowsNext-generation exascale machines with extreme levels of parallelism will provide massive computing resources for large scale numerical simulations of complex physical systems at unprecedented…Komal Kumari, Emmet Cleary, Swapnil Desai et al.·Apr 3, 2022SaveLearn
A New Correction to the Rytov Approximation for Strongly Scattering Lossy MediaWe propose a correction to the conventional Rytov approximation (RA) and investigate its performance for predicting wave scattering under strong scattering conditions. An important motivation for the…Amartansh Dubey, Xudong Chen, Ross Murch·Apr 2, 2022SaveLearn
Computation of optimal beams in weak turbulenceWhen an optical beam propagates through a turbulent medium such as the atmosphere or ocean, the beam will become distorted. It is then natural to seek the best or optimal beam that is distorted…Qin Li, Anjali Nair, Samuel N Stechmann·Apr 1, 2022SaveLearn
Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dyeAll signals obtained as instrumental response of analytical apparatus are affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an inherently weak process, the noise background can…Nelson H. T. Lemes, Taináh M. R. Santos, Camila A. Tavares et al.·Apr 1, 2022SaveLearn
Machine learning for a finite size correction in periodic coupled cluster theory calculationsWe introduce a straightforward Gaussian process regression (GPR) model for the transition structure factor of metal periodic coupled cluster singles and doubles (CCSD) calculations. This is inspired…Laura Weiler, Tina N. Mihm, James J. Shepherd·Mar 31, 2022SaveLearn
An implicit symplectic solver for high-precision long term integrations of the Solar SystemCompared to other symplectic integrators (the Wisdom and Holman map and its higher order generalizations) that also take advantage of the hierarchical nature of the motion of the planets around the…M. Antoñana, E. Alberdi, J. Makazaga et al.·Mar 31, 2022SaveLearn
Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426Icosahedral Au clusters with three and four shells of atoms are found to deviate significantly from the commonly assumed Mackay structures. By introducing additional atoms in the surface shell and…Jan Kloppenburg, Andreas Pedersen, Kari Laasonen et al.·Mar 31, 2022SaveLearn
Hybrid normal mode and energy flux model for an ideal oceanic wedge environment with radial sound speed frontEnergy flux is an acoustic propagation model that calculates the locally-averaged intensity without computing explicit eigenvalues or tracing rays. The energy flux method has so far only been used…Mark Langhirt, Charles Holland, Sheri Martinelli et al.·Mar 31, 2022SaveLearn
Atomistically-informed continuum modeling and isogeometric analysis of 2D materials over holey substratesThis work develops, discretizes, and validates a continuum model of a molybdenum disulfide (MoS2) monolayer interacting with a periodic holey silicon nitride substrate via van der Waals (vdW)…Moon-ki Choi, Marco Pasetto, Zhaoxiang Shen et al.·Mar 30, 2022SaveLearn
Machine learning based data-driven discovery of nonlinear phase-field dynamicsOne of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models…Elham Kiyani, Steven Silber, Mahdi Kooshkbaghi et al.·Mar 30, 2022SaveLearn
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in PtychographyWe present a method that lowers the dose required for a ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions…Marcel Schloz, Johannes Müller, Thomas C. Pekin et al.·Mar 29, 2022SaveLearn
Accelerating innovation with software abstractions for scalable computational geophysicsWe present the SLIM (https://github.com/slimgroup) open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical…Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi et al.·Mar 28, 2022SaveLearn
Nanocryotron-driven Charge Configuration MemristorCryo-computing - both classical and quantum, is severely limited by the absence of a suitable cryo-memory. The challenge both in terms of energy efficiency and speed have been known for decades, but…Anze Mraz, Viktor V. Kabanov, Rok Venturini et al.·Mar 28, 2022SaveLearn
MeMC: A package for monte-carlo simulations of spherical shellsThe MeMC is an open-source software package for monte-carlo simulation of elastic shells. It is designed as a tool to interpret the force-distance data generated by indentation of biological…Vipin Agrawal, Vikash Pandey, Hanna Kylhammar et al.·Mar 28, 2022SaveLearn
Molecular conformer search with low-energy latent spaceIdentifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and…Xiaomi Guo, Lincan Fang, Yong Xu et al.·Mar 26, 2022SaveLearn
Hybrid Parallelization of Euler-Lagrange Simulations Based on MPI-3 Shared MemoryThe use of Euler-Lagrange methods on unstructured grids extends their application area to more versatile setups. However, the lack of a regular topology limits the scalability of distributed parallel…Patrick Kopper, Stephen Copplestone, Marcel Pfeiffer et al.·Mar 25, 2022SaveLearn
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geologyA general control policy framework based on deep reinforcement learning (DRL) is introduced for closed-loop decision making in subsurface flow settings. Traditional closed-loop modeling workflows in…Yusuf Nasir, Louis J. Durlofsky·Mar 24, 2022SaveLearn
Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via Shifted Operator InferenceSolar wind conditions are predominantly predicted via three-dimensional numerical magnetohydrodynamic (MHD) models. Despite their ability to produce highly accurate predictions, MHD models require…Opal Issan, Boris Kramer·Mar 24, 2022SaveLearn