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Los Alamos National Laboratory
- Santa Fe, NM
- seatonullberg.github.io
Stars
User-friendly Scheduler for sub-node tasks for HPC systems
Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials Project website.
Public development project of the LAMMPS MD software package
High accuracy RAG for answering questions from scientific documents with citations
Open-Source API Development Ecosystem • https://hoppscotch.io • Offline, On-Prem & Cloud • Web, Desktop & CLI • Open-Source Alternative to Postman, Insomnia
Display and Edit Molecules (https://zndraw.icp.uni-stuttgart.de)
A code to generate atomic structure with symmetry
doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defect simulation workflow in an efficient, reproducible, user-fr…
A hierarchical, component based molecule builder
A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.
quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.
A high performance Python graph library implemented in Rust.
Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
A for finding optimized SQS structures tool written in C++
Atomsk: A Tool For Manipulating And Converting Atomic Data Files -
Python interface to TD Ameritrade (https://developer.tdameritrade.com)
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
pyiron - an integrated development environment (IDE) for computational materials science.
Serde serializable and deserializable trait objects
AiiDA plugin for Gaussian quantum chemistry software
Library for reading and writing chemistry files
DScribe is a python package for creating machine learning descriptors for atomistic systems.