Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsArtificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simulations with almost…Viktor Zaverkin, David Holzmüller, Ingo Steinwart et al.·Sep 20, 2021SaveLearn
Stabilization of S3O4 at High Pressure-Implications for the Sulfur Excess ParadoxThe geological conundrum of sulfur excess refers to the finding that predicted amounts of sulfur, in the form of SO2, discharged in volcanic eruptions much exceeds the sulfur available for degassing…Siyu Liu, Pengyue Gao, Andreas Hermann et al.·Sep 18, 2021SaveLearn
Improving the Deconvolution of Spectrum at Finite Temperature via Neural NetworkIn the study of condensed matter physics, spectral information plays an important role for understand the mechanism of materials. However, it is difficult to obtain the spectrum directly through…Haidong Xie, Xueshuang Xiang, Yuanqing Chen·Sep 18, 2021SaveLearn
A quantum Monte Carlo study of systems with effective core potentials and node nonlinearitiesWe study beryllium dihydride (BeH2) and acetylene (C2H2) molecules using real-space diffusion Monte Carlo (DMC) method. The molecules serve as perhaps the simplest prototypes that illustrate…Haihan Zhou, Anthony Scemama, Guangming Wang et al.·Sep 17, 2021SaveLearn
Chemical bonding theories as guides for self-interaction corrected solutions: multiple local minima and symmetry breakingFermi--L\"owdin orbitals (FLO) are a special set of localized orbitals, which have become commonly used in combination with the Perdew--Zunger self-interaction correction (SIC) in the FLO-SIC method.…Kai Trepte, Sebastian Schwalbe, Simon Liebing et al.·Sep 16, 2021SaveLearn
Numerical Simulations of the Nonlinear Quantum Vacuum in the Heisenberg-Euler Weak-Field ExpansionThe Heisenberg-Euler theory of the quantum vacuum supplements Maxwell's theory of electromagnetism with nonlinear light-light interactions. These originate in vacuum fluctuations, a key prediction of…Andreas Lindner, Baris Ölmez, Hartmut Ruhl·Sep 16, 2021SaveLearn
Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernelWe study numerical methods for the generalized Langevin equation (GLE) with a positive Prony series memory kernel, in which case the GLE can be written in an extended variable Markovian formalism. We…Manh Hong Duong, Xiaocheng Shang·Sep 16, 2021SaveLearn
Second-order perturbative correlation energy functional in the ensemble density-functional theoryWe derive the second-order approximation (PT2) to the ensemble correlation energy functional by applying the G\"orling-Levy perturbation theory on the ensemble density-functional theory (EDFT). Its…Zeng-hui Yang·Sep 16, 2021SaveLearn
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning PotentialsMachine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical…Viktor Zaverkin, Johannes Kästner·Sep 15, 2021SaveLearn
Disentangling Generative Factors of Physical Fields Using Variational AutoencodersThe ability to extract generative parameters from high-dimensional fields of data in an unsupervised manner is a highly desirable yet unrealized goal in computational physics. This work explores the…Christian Jacobsen, Karthik Duraisamy·Sep 15, 2021SaveLearn
Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representationsNumerical solutions of partial differential equations (PDEs) require expensive simulations, limiting their application in design optimization, model-based control, and large-scale inverse problems.…James Duvall, Karthik Duraisamy, Shaowu Pan·Sep 14, 2021SaveLearn
Assessing the Performance of Nonlinear Regression based Machine Learning Models to Solve Coupled Cluster TheoryThe iteration dynamics of the coupled cluster equations exhibits a synergistic relationship among the cluster amplitudes. The iteration scheme may be viewed as a multivariate discrete-time…Valay Agarawal, Samrendra Roy, Kapil K. Shrawankar et al.·Sep 13, 2021SaveLearn
REST-for-Physics, a ROOT-based framework for event oriented data analysis and combined Monte Carlo responseThe REST-for-Physics (Rare Event Searches Toolkit for Physics) framework is a ROOT-based solution providing the means to process and analyze experimental or Monte Carlo event data. Special care has…Konrad Altenmüller, Susana Cebrián, Theopisti Dafni et al.·Sep 13, 2021SaveLearn
Scaling and Acceleration of Three-dimensional Structure Determination for Single-Particle Imaging Experiments with SpiniFELThe Linac Coherent Light Source (LCLS) is an X- ray free electron laser (XFEL) facility enabling the study of the structure and dynamics of single macromolecules. A major upgrade will bring the…Hsing-Yin Chang, Elliott Slaughter, Seema Mirchandaney et al.·Sep 11, 2021SaveLearn
Connecting lattice Boltzmann methods to physical reality by coarse-graining Molecular Dynamics simulationsThe success of lattice Boltzmann methods has been attributed to their mesoscopic nature as a method derivable from a physically consistent microscopic model. Original lattice Boltzmann methods were…Aleksandra Pachalieva, Alexander J. Wagner·Sep 10, 2021SaveLearn
Exploring the Potential of Parallel Biasing in Flat Histogram MethodsMetadynamics, a member of the `flat histogram' class of advanced sampling algorithms, has been widely used in molecular simulations to drive the exploration of states separated by high free energy…Shanghui Huang, Michael J. Quevillon, Ernesto C. Cortés-Morales et al.·Sep 10, 2021SaveLearn
Improvement of Geant4 Neutron-HP package: from methodology to evaluated nuclear data libraryAn accurate description of interactions between thermal neutrons (below 4 eV) and materials is key to simulate the transport of neutrons in a wide range of applications such as criticality-safety,…Loic Thulliez, Cédric Jouanne, Eric Dumonteil·Sep 10, 2021SaveLearn
Tunable band gaps and optical absorption properties of bent MoS2 nanoribbonsThe large tunability of band gaps and optical absorptions of armchair MoS2 nanoribbons of different widths under bending is studied using density functional theory and many-body perturbation GW…Hong Tang, Bimal Neupane, Santosh Neupane et al.·Sep 9, 2021SaveLearn
Analytical and computational study of cascade reaction processes in catalytic fibrous membranesMultistep catalytic reactions use two different catalysts for the A B and the subsequent B C reaction, respectively. Often the employed catalysts are chemically incompatible, such as…Gabriel Sitaru, Stephan Gekle·Sep 9, 2021SaveLearn
Path Integral Monte Carlo Simulations of liquid 3He without Fixed Nodes: Structural Properties and Collective ExcitationsWe present extensive new ab initio path integral Monte Carlo (PIMC) simulations of normal liquid 3He without any nodal constraints. This allows us to study the effects of temperature on…Tobias Dornheim, Zhandos Moldabekov, Jan Vorberger et al.·Sep 9, 2021SaveLearn
Two-dimensional pentagonal material Penta-PdPSe: A first-principle studyLow-symmetry Penta-PdPSe with intrinsic in-plane anisotropy synthesized successfully [(P. Li et al., Adv. Mater., 2102541, (2021)]. Motivated by this experimental discovery, we investigate the…A. Bafekry, M. M. Fadlallah, M. Faraji et al.·Sep 9, 2021SaveLearn
Influence of database noises to machine learning for spatiotemporal chaosA new strategy, namely the "clean numerical simulation" (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of…Yu Yang, Shijie Qin, Shijun Liao·Sep 8, 2021SaveLearn
Neural network approaches for solving Schr\"odinger equation in arbitrary quantum wellsIn this work we approach the Schr\"odinger equation in quantum wells with arbitrary potentials, using the machine learning technique. Two neural networks with different architectures are proposed and…Adrian Radu, Carlos A. Duque·Sep 7, 2021SaveLearn
SymPhas --General purpose software for phase-field, phase-field crystal and reaction-diffusion simulationsThis work develops a new open source API and software package called SymPhas for simulations of phase-field, phase-field crystal and reaction-diffusion models, supporting up to three…Steven A. Silber, Mikko Karttunen·Sep 6, 2021SaveLearn
A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methodsThis paper presents a new data-driven non-intrusive reduced-order model(NIROM) that outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder model. This is achieved by…R. Fu, D. Xiao, I. M. Navon et al.·Sep 5, 2021SaveLearn