Software
| Name and Description | Link to Live Application | Source Code | Documentation | Data |
|---|---|---|---|---|
| AniSOAP A Python library for creating descriptors of chemical systems suitable for machine learning use. This project aims to extend the popular Smooth Overlap of Atomic Positions (SOAP) descriptors to coarse-grained systems consisting of aspherical particles with anisotropic interactions. | https://github.com/cersonsky-lab/AniSOAP | https://anisoap.readthedocs.io/en/latest/ | ||
| scikit-matter A toolbox of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows. | https://scikit-matter.readthedocs.io/en/latest/ | 10.12688/openreseurope.15789.2 | ||
| INQ INQ is an open-source, GPU-accelerated electronic-structure software package for density functional theory (DFT) and real-time time-dependent density functional theory (RT-TDDFT). It is designed for modern high-performance computing architectures and can be used both as a standalone electronic-structure code and as a modular library for developing new electronic-structure and non-equilibrium simulation methods. INQ supports ground-state and real-time simulations, GPU and distributed-memory parallelization, and spin-polarized and non-collinear spin calculations. | N/A | https://gitlab.com/npneq/inq | https://inq.readthedocs.io/en/latest/ Additional tutorials and developer documentation are available through the INQ GitLab wiki: https://gitlab.com/npneq/inq/-/wikis/home | No separate data repository is currently associated with the software. Example calculations, tests, and tutorials are distributed with the source code and through the documentation. |
Software Resulting from Previous Grant 2017-2023 (DMR – 1720415)
| Name and Description | Link to Live Application | Source Code | Documentation | Data |
|---|---|---|---|---|
| MAterials Simulation Toolkit – Machine Learning (MAST-ML)
MAterials Simulation Toolkit – Machine Learning (MAST-ML) is an automated tool for setting up, executing, and managing output machine learning tasks in materials science. | Source Code | Documentation Curriculum | Education Datasets | |
| Web App: Optimization of Area of a Box
Brousseau, M., Afflerbach, B., Morgan, D. Web apps and materials for learning about optimization (high-school level), August 2019. Work was done as part of a Research Experience for Teachers (RET) program (mrsec.wisc.edu/ret). | Application | |||
| Web App: Local Extrema with Two Independent Variables
Brousseau, M., Afflerbach, B., Morgan, D. Web apps and materials for learning about optimization (high-school level), August 2019. Work was done as part of a Research Experience for Teachers (RET) program (mrsec.wisc.edu/ret). | Application |