Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Tuteja, Keshvi, Olenik, Gregor + 12 more
Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplicity and ease of use. Yet high-performance sparse kernels in Python remain limited in functionality, especially on modern CPU and GPU…
Zeyi Zhang, Carlos Mora Perez, Patrick Kwon, Martin Head‐Gordon + 1 more
PARSEC.py is a Python-based real-space Kohn-Sham density functional theory (real-space KS-DFT) framework designed to provide a user- and developer-friendly platform for first-principles electronic-structure simulations. Discretization on real-space grids eliminates basis-set approximations, while enabling systematic…
Bruno M. Saraiva, Iván Hidalgo-Cenalmor, António D. Brito, Damián Martínez + 3 more
> 1 Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2 Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland 3 InFLAMES Research Flagship Center, University of Turku, Turku, Finland 4 Turku Bioscience Centre, University of Turku…
Ragnar Bjornsson
We introduce ASH, a multi-scale, multi-theory modeling program for quantum mechanics (QM), molecular mechanics (MM), and hybrid calculations, written in the Python programming language. ASH is written in response to the increasingly diverse computational chemistry software landscape that features more QM and MM…
Indranil Saha, Daniel Willimetz, Lukáš Grajciar
Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customization, interactivity, and efficiency with large datasets. To address this, we developed CRISP (Comprehensive Repository for Insightful Simulation Post-Processing), an…
Fabio Cumbo, Jayadev Joshi, Daniel Blankenberg
Background – The integration of command-line tools into the Galaxy platform is crucial for making complex computational methods accessible to a broader audience and ensuring reproducible research. However, the manual development of tool wrappers (i.e., the XML files that define the user interface and execution logic in…
Michael J. O’Brien, David Silva-Sánchez, Geoffrey Woollard, Kwanghwi Je + 5 more
While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular…
Authors not listed
With the rapid growth of chemical data and information, there is an increasing need for chemistry undergraduates to master Python tools for analyzing large chemical datasets and extracting key or feature information. Currently, more than 100,000 types of metal-organic frameworks (MOFs), as the material recently awarded…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Ihor Kendiukhov
ergodicity is an open-source Python library for computational work on stochastic dynamics, with particular emphasis on non-ergodicity, time-average behavior, heavy-tailed processes, and decision making under uncertainty. The package brings together three layers that are often split across ad hoc scripts: process…
Authors not listed
The analysis of molecular dynamics (MD) simulations is a critical but fragmented process, often requiring researchers to chain together multiple software tools and write bespoke scripts for routine structural and dynamic analyses. This workflow complexity creates a significant barrier to efficiency, standardization…
Daniel P. Marshall, Elie S. Farah, Eric D. Musselman, Nicole A. Pelot + 2 more
of model nerve fibers to electrical stimulation PyFibers: A Python package to simulate nerve fiber responses to electrical stimulation Authors: Daniel P. Marshall, Elie S. Farah, Eric D. Musselman, Nicole A. Pelot, Warren M. Grill, Alain Nogaret Computational modeling of peripheral nerve fibers is a key tool for…
Authors not listed
TurtleMol is an open-source Python package that aims to help users generate large, complex molec- ular systems. In the current version, users can generate systems by filling volumes defined by basic geometric shapes (e.g. cube, sphere), or by shapes of arbitrary gemoetries defined meshes created in other software (such…
Jose L Figueroa, Richard Allen White
We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC)…
Heber L. Rocha, Elmar Bucher, Shuming Zhang, Atul Deshpande + 3 more
Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Tsai, Shin-Rong, Schive, Hsi-Yu + 2 more
In the exascale computing era, handling and analyzing massive datasets have become extremely challenging. In situ analysis, which processes data during simulation runtime and bypasses costly intermediate I/O steps, offers a promising solution. We present libyt (https://github.com/yt-project/libyt), an open-source C…
Darshan Mandge, Anıl Tuncel, Aurélien Jaquier, Ilkan Kilic + 5 more
The diversity of labs, tools, and data formats in neuroscientific research has historically posed challenges for data sharing and collaboration. Researchers often needed to convert data between various formats and adapt their software to new environments, which could divert attention from their main research goals.…
Jeremy Li, Alex Rubinsteyn, Sergey Feldman, Timothy O’Donnell + 18 more
Scientific computing has become a central component of modern scientific discovery. Yet many computational tools are developed by small, specialized teams under incentives that encourage the release of rapidly prototyped tooling without commensurate attention to engineering concerns, including performance and…
Fanwang Meng, Marco Martínez González, Valerii Chuiko, Alireza Tehrani + 7 more
Selector is a free, open-source Python library for selecting diverse subsets from any dataset, making it a versatile tool across a wide range of application domains. Selector implements different subset sampling algorithms based on sample distance, similarity, and spatial partitioning, along with metrics to quantify…
Haofei Gao, Tingjia Miao, Wenkai Jin, Muhua Zhang + 11 more
Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise required to turn research motivation into reliable computing workflows. Here we present \textsc{LQCDMaster}, a tool-augmented, skill-guided…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Authors not listed
Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This…
Shivamshan Sivanesan, Kazem Ardaneh
Fortran has been the cornerstone of high-performance computing for decades and remains unmatched in many domains. Yet the language faces an expertise gap: a new generation of scientists is barely familiar with it, while many experienced Fortran developers are only now transitioning to modern ecosystems such as JAX.…