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DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely…

Covalent Organic Frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry. These traits make COFs a highly promising choice…

Materials Science · Physics 2023-12-21 Felipe Lopes Oliveira , Pierre Mothé Esteves

TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for…

Applied Physics · Physics 2026-05-26 Uttiyoarnab Saha , Ali Hamedani , Miguel A. Caro , Andrea E. Sand

Multiple software packages currently exist for the computation of bulk topological invariants in both idealized tight-binding models and realistic Wannier tight-binding models derived from density functional theory. Currently, only one…

Materials Science · Physics 2024-06-14 Alexander C. Tyner

This paper describes a series of model-checking experiments to verify optimistic replication algorithms based on Operational Transformation (OT) approach used for supporting collaborative edition. We formally define, using tool UPPAAL, the…

Logic in Computer Science · Computer Science 2009-04-20 Hanifa Boucheneb , Abdessamad Imine

In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from multiple domains such as computer vision, natural language…

Machine Learning · Computer Science 2024-06-28 Simon Blauth , Tobias Bürger , Zacharias Häringer , Jörg Franke , Frank Hutter

The parameterization of simulation-based models is a central yet laborious task in computational chemistry and physics, often driven by human intuition and manual iteration. Automating this task necessitates the definition of suitable…

A variety of optimization algorithms have been developed to solve engineering design problems in which the solution space is too large to manually determine the optimal solution. The Modular Optimization Framework (MOF) was developed to…

Neural and Evolutionary Computing · Computer Science 2022-04-04 Brian Andersen , Gregory Delipei , David Kropaczek , Jason Hou

Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the…

BosonSampling is a quantum mechanical task involving Fock basis state preparation and detection and evolution using only linear interactions. A classical algorithm for producing samples from this quantum task cannot be efficient unless the…

Quantum Physics · Physics 2017-08-09 A. P. Lund , S. Rahimi-Keshari , T. C. Ralph

High precision atomic data is indispensable for experiments involving studies of fundamental interactions, astrophysics, atomic clocks, plasma science, and others. We develop new parallel atomic structure codes and explore the difficulties…

Atomic Physics · Physics 2021-03-11 C. Cheung , M. S. Safronova , S. G. Porsev

The software package DIALECT is introduced, which provides the capability of calculating excited-state properties and nonadiabatic dynamics of large molecular systems and can be applied to simulate energy and charge-transfer processes in…

Chemical Physics · Physics 2025-05-15 Richard Einsele , Xincheng Miao , Luca Nils Philipp , Roland Mitric

Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, and this set is screened for candidate materials for specific applications. In contrast, many…

Materials Science · Physics 2023-04-11 Rachel Woods-Robinson , Matthew K. Horton , Kristin A. Persson

RooFit is a library of C++ classes that facilitate data modeling in the ROOT environment. Mathematical concepts such as variables, (probability density) functions and integrals are represented as C++ objects. The package provides a flexible…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Wouter Verkerke , David Kirkby

The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software package (Bayesian Adaptive Trials Simulator Software -…

Correlation functions, such as static and dynamic structure factors, offer a versatile approach to analyzing atomic-scale structure and dynamics. By having access to the full dynamics from atomistic simulations, they serve as valuable tools…

Fitted interatomic potentials are widely used in atomistic simulations thanks to their ability to compute the energy and forces on atoms quickly. However, the simulation results crucially depend on the quality of the potential being used.…

Other Condensed Matter · Physics 2024-05-07 Mingjian Wen , Junhao Li , Peter Brommer , Ryan S. Elliott , James P. Sethna , Ellad B. Tadmor

We have investigated plasma-surface interactions with molecular dynamics (MD) simulations. It, however, is high cost computation and is limited to simulations for materials of nanometer order. In order to overcome the limitation, a…

Materials Science · Physics 2010-05-20 Arimichi Takayama , Seiki Saito , Atsushi M. Ito , Takahiro Kenmotsu , Hiroaki Nakamura

We present the ALPS (Algorithms and Libraries for Physics Simulations) project, an international open source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models…

We introduce MOSAIC, a Python program for machine learning models. Our framework is developed with in mind accelerating machine learning studies through making implementing and testing arbitrary network architectures and data sets simpler,…

Machine Learning · Computer Science 2023-01-31 Mattéo Papin , Yann Beaujeault-Taudière , Frédéric Magniette