Tutorials

Jupyter notebooks demonstrate AmorphGen workflows. Clone the repository to keep the notebooks and their companion files together, then install a backend and JupyterLab in the same environment:

git clone https://github.com/SMTG-Bham/AmorphGen.git
cd AmorphGen
python -m pip install -e ".[mace,chgnet]" jupyterlab
jupyter lab Tutorials/

Select that environment as the notebook kernel and run cells in order. For Tutorial 6’s classical calculations, the base package is sufficient; the MACE refinement cell needs the MACE extra. See Installation for platform requirements and Quickstart for CLI examples.

Start here:

Tutorial	Description	Backend	System
Tutorial 1: Quick-start tutorial	Orientation: what AmorphGen does, the three workflows, decision tree, one live demo	CHGNet	a-SiO₂

Workflow tutorials (runtime depends on the system, backend, hardware and simulation settings):

Tutorial	Description	Backend	System
Tutorial 2: Zero-config random gen	Composition is the only input; auto minsep / density / target CN / oxidation state; CHGNet relax + save each structure	CHGNet	Si, SiO₂, In₂O₃, CdTe, AlN, LiCl, TiO₂, Cu
Tutorial 3: Explicit control + ensemble analysis	The opposite end of T2: explicit minsep (from crystal-phase bond lengths) + explicit target density (from cited amorphous-thin-film references). 5-structure ensembles per system; quantitative RDF / energy / CN / bond-angle analysis vs the crystalline reference	MACE	In₂O₃, TiO₂, Al₂O₃, Ga₂O₃
Tutorial 4: Melt-and-quench	Full 7-stage pipeline	CHGNet (CPU) / MACE (GPU)	SiO₂
Tutorial 5: Hybrid batch quench	Random gen → equilibrate → batch quench	CHGNet	TiO₂
Tutorial 6: Classical potentials	Buckingham+Coulomb relaxation, hybrid workflow	Classical; optional MACE refinement	SiO₂, Al₂O₃, TiO₂

Application case studies (these assume you have done the workflow tutorials):

Tutorial	Description	Backend	System
Tutorial 7: Dimer dissociation kinetics	O–O peroxide-defect dissociation in amorphous oxide; Arrhenius temperature scan	MACE	In₂O₃