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Virtual observatories allow the means by which an astronomer is able to discover, access, and process data seamlessly, regardless of its physical location. However, steep learning curves are often required to become proficient in the…

Instrumentation and Methods for Astrophysics · Physics 2019-06-27 K. Fitzgerald , L. -M. Browne , R. F. Butler

AnyMOD.jl is a Julia framework for creating large-scale energy system models with multiple periods of capacity expansion. It applies a novel graphbased approach that was developed to address the challenges in modeling high levels of…

Physics and Society · Physics 2022-12-21 Leonard Göke

Polymerization of C60 molecular crystal under high pressure and high temperature is simulated by using linear scaling tight binding molecular dynamics (TBMD) with Graphic Processing Unit (GPU) as a computational accelerator for…

Materials Science · Physics 2010-05-06 Toshiaki Iitaka

Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture…

Quantum simulation of chemistry and materials is predicted to be an important application for both near-term and fault-tolerant quantum devices. However, at present, developing and studying algorithms for these problems can be difficult due…

The rapid progress of Large Multimodal Models (LMMs) and cloud-based AI agents is transforming human-AI collaboration into bidirectional, multimodal interaction. However, existing codecs remain optimized for unimodal, one-way communication,…

Artificial Intelligence · Computer Science 2025-09-29 Qi Mao , Tinghan Yang , Jiahao Li , Bin Li , Libiao Jin , Yan Lu

We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environments, as well as interaction potentials and neural networks…

Computational Physics · Physics 2020-12-04 Samuel S. Schoenholz , Ekin D. Cubuk

PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code…

Machine Learning · Computer Science 2023-12-21 Agus Sudjianto , Aijun Zhang , Zebin Yang , Yu Su , Ningzhou Zeng

We introduce bindings that enable the convenient, efficient, and reliable use of software modules of CGAL (Computational Geometry Algorithm Library), which are written in C++, from within code written in Python. There are different tools…

Computational Geometry · Computer Science 2023-06-08 Nir Goren , Efi Fogel , Dan Halperin

In this demo, we present PackageBuilder, a system that extends database systems to support package queries. A package is a collection of tuples that individually satisfy base constraints and collectively satisfy global constraints. The need…

Databases · Computer Science 2015-07-06 Matteo Brucato , Rahul Ramakrishna , Azza Abouzied , Alexandra Meliou

Recently proposed modifications of the standard particle-in-cell (PIC) method resolve long-standing limitations such as exact preservation of physically conserved quantities and unbiased ensemble down-sampling. Such advances pave the way…

Plasma Physics · Physics 2026-03-13 Frida Brogren , Christoffer Olofsson , Joel Magnusson , Arkady Gonoskov

Direct manipulation interfaces provide intuitive and interactive features to a broad range of users, but they often exhibit two limitations: the built-in features cannot possibly cover all use cases, and the internal representation of the…

Human-Computer Interaction · Computer Science 2019-07-26 Brian Hempel , Ravi Chugh

{\sc ParMooN} is a program package for the numerical solution of elliptic and parabolic partial differential equations. It inherits the distinct features of its predecessor {\sc MooNMD} \cite{JM04}: strict decoupling of geometry and finite…

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-01-21 Davide Gadioli , Gianluca Palermo , Stefano Cherubin , Emanuele Vitali , Giovanni Agosta , Candida Manelfi , Andrea R. Beccari , Carlo Cavazzoni , Nico Sanna , Cristina Silvano

There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular…

Chemical Physics · Physics 2026-01-21 Xinyu Lu , Hao Ma , Hui Li , Jia Li , Yi Rong , Yuqiang Li , Tong Zhu , Guokun Liu , Bin Ren

CHEMSMART (Chemistry Simulation and Modeling Automation Toolkit) is an open-source, Python-based framework designed to streamline quantum chemistry workflows for homogeneous catalysis and molecular modeling. By integrating job preparation,…

Chemical Physics · Physics 2025-08-28 Xinglong Zhang , Huiwen Tan , Jingyi Liu , Zihan Li , Lewen Wang , Benjamin W. J. Chen

We previously introduced a conformational sampling method, a multi-dimensional virtual-system coupled molecular dynamics (mD-VcMD), to enhance conformational sampling of a biomolecular system by computer simulations. Here, we present a new…

Motivation: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often…

Designing and implementing an intelligent and user friendly human machine interface for any kind of software or hardware oriented application is always be a challenging task for the designers and developers because it is very difficult to…

Human-Computer Interaction · Computer Science 2010-08-26 Zeeshan Ahmed

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning…