mechanoChemML library¶
mechanoChemML is a machine learning software library for computational materials physics. It is designed to function as an interface between platforms that are widely used for scientific machine learning on one hand, and others for solution of partial differential equations-based models of physics. Of special interest here, and the focus of mechanoChemML, are applications to computational materials physics. These typically feature the coupled solution of material transport, reaction, phase transformation, mechanics, heat transport and electrochemistry. mechanoChemML is developed and maintained by the Computational Physics Group at the University of Michigan, Ann Arbor. Following is the list of the main contribtors:
- Arjun Sundararajan
- Elizabeth Livingston
- Greg Teichert
- Jamie Holber
- Matt Duschenes
- Mostafa Faghih Shojaei
- Sid Srivastava
- Xiaoxuan Zhang
- Zhenlin Wang
- Krishna Garikipati
Using this library¶
- Installation
- How to install this library.
- Code structure
- Details of the code structure of this library.
- Workflow summary
- Various workflows shipped with this library.
Development¶
- Contributing
- How to contribute to this library.
- Change log
- The library development changelog.
List of examples¶
Cite mechanoChemML¶
If you find this code useful in your research, please consider citing:
- Zhang, G.H. Teichert, Z. Wang, M. Duschenes, S. Srivastava, A. Sunderarajan, E. Livingston, J. Holber, M. Shojaei, K. Garikipati (2021), mechanoChemML: A software library for machine learning in computational materials physics, arXiv preprint arXiv:2112.04960.