ANN training parameters and physical meaning

Call chain and models

Configure c01_train_ann.py; it calls aenet_env.training and aenet_env.aenet_backend, which delegate learning to the selected external ænet implementation. The local model approximates total energy as a sum of element-specific atomic ANN contributions from finite-radius descriptors. Long-range physics is not automatically reproduced exactly.

Parameter	v0.0.3 example	Meaning and caveats
`ROOT`, `REFERENCES`, `OUTPUT`	`SiO2`, `SiO2/references`, `SiO2/ann`	Input/output directories; relative paths use the current working directory. Separate experiments by output path.
`SPECIES`	`('Si','O')`	Chemical species and element-specific networks. Match the reference structures and ordering.
`RADIAL_CUTOFF`	`6.0 Å`	Radial environment extent. Consider coordination shells and periodic images.
`ANGULAR_CUTOFF`	`3.5 Å`	Angular environment extent. Check whether relevant local bridges are represented.
`RADIAL_ORDER`	`10`	Chebyshev radial expansion order; balances resolution, feature size, and overfitting.
`ANGULAR_ORDER`	`3`	Chebyshev angular expansion order; evaluate together with cutoff.
`MINIMUM_DISTANCE`	`0.5 Å`	Descriptor lower-distance parameter, not a physical collision threshold for generating structures.
`HIDDEN_NODES`	`30`	Two `tanh` hidden layers with 30 units each in `architecture()`.
`ITERATIONS`	`1000`	Training iteration limit; meanings differ across optimizers.
`TEST_PERCENT`	`10`	Internal test fraction, not a substitute for independent validation.
`DEVICE`	`cpu`	Torch backend only, not the Fortran API path.
`FORCE_WEIGHT`	`0.0`	Torch force-loss weight; zero disables direct force-loss training.
`GENERATE_EXE`, `TRAIN_EXE`	`generate.x`, `train.x`	Fortran executable names or paths.

Training backends

fortran_legacy: prepare_fortran_dataset writes .stp, generate.in, train.in; train_fortran_legacy invokes train.x.
fortran_normal: Fortran-format dataset and TrainingConfig(iterations=ITERATIONS, testpercent=TEST_PERCENT, method=BFGS()) passed to train_fortran_api. Selected in the supplied example.
aenet_python_torch: ChebyshevDescriptor, TorchTrainingConfig, and Adam(mu=0.001, batchsize=16); DEVICE and FORCE_WEIGHT apply here.
separate_aenet_pytorch: independent trainer configured with SEPARATE_TRAINER, SEPARATE_INPUT, SEPARATE_MODELS, and external converters.

Validation and warnings

Descriptor cutoffs are not bond lengths. Check periodic-image correlations in small DFT cells, reference element order, energy/force units, and convergence. Keep related or nearly duplicate structures from leaking across training/test partitions. Evaluate generalization using an independent validation set. Evaluate energy and, where applicable, force errors, structural subgroups, out-of-distribution environments, and transfer to the intended application. A trained model file does not establish successful RMC_POT constraints.