Configuration reference

Every setting of meidnet.yaml, generated from the code (meidnet schema gives the JSON Schema). Unknown keys are rejected with a message naming the key, so typos cannot silently change a run.

Top level

setting	type	default	meaning
name	string	meidnet_run	Name of this project; outputs go to output_dir.
description	string		Free text shown at the top of reports.
output_dir	string	runs/{name}	Where checkpoints, CIFs and reports are written.
family	string/null	None	Family used to align training structures (defaults to generation.family).
model_path	string/null	None	Existing checkpoint to generate from (skip training). Default: /model.pt
plugins	array		Python files that register your own constraints or search terms (see 'Add a constraint').
data	object/null	None
model	section
training	section
generation	object/null	None

data:

setting	type	default	meaning
table	string	required	Path to a CSV/Excel/JSON table with one row per material.
id_column	string	material_id	Column with a unique identifier for each material.
cif_column	string/null	cif	Column that contains the CIF text of each structure. Set to null if you use structures_dir.
structures_dir	string/null	None	Folder with one CIF file per material, named .cif (used when there is no cif_column).
properties	array	required	Numeric columns that form the property modality (any number, at least one).
val_table	string/null	None	Optional separate validation table with the same columns.
val_fraction	number	0.1	Fraction of rows held out for validation when no val_table is given (0 = no validation).
max_sites	integer	20	Largest number of atoms per cell. Bigger structures are skipped.
neighbor_cutoff	number	4.0	Distance (Å) below which two atoms count as neighbours.
align_to_prototype	boolean	True	Re-order the atoms of every structure so they match the family prototype's site order (needed for meaningful generation). The published Perov-5 model was trained without it.
prototype_tolerance	number	0.15	How far (fraction of a cell edge) an atom may sit from its prototype site and still count as that site. Raise it to accept distorted structures; lower it to keep only ideal ones.

data.properties[]

setting	type	default	meaning
column	string	required	Name of the column in your table that holds this property.
label	string/null	None	Human-readable name used in reports (defaults to the column name).
unit	string		Unit shown in reports, e.g. 'eV' or 'eV/atom'.
normalize	boolean	True	Standardise the property to zero mean and unit spread before training. Keep this on when properties have very different scales.

model:

setting	type	default	meaning
latent_dim	integer	128	Size of the shared latent space where structures and properties meet.
node_hidden_dim	integer	128	Width of the per-atom features inside the graph network.
edge_dim	integer	64	Width of the per-bond messages inside the graph network.
species_embedding_dim	integer	64	Size of the learned element embedding.
property_hidden_dim	integer	128	Width of the property encoder's hidden layer.
decoder_coordinate_input	data / zeros / prototype	data	What the crystal decoder receives as starting atom positions during training. 'data' (published model) uses the true positions; generation always starts from 'zeros' or the prototype. 'zeros'/'prototype' make training and generation consistent (experimental).

training:

setting	type	default	meaning
epochs	integer	200	Number of passes over the training data.
batch_size	integer	16	Materials per training step (use 8 if memory is short).
learning_rate	number	0.001	Adam learning rate.
contrastive_weight	number	5.0	Strength of the alignment between structure and property latents.
temperature	number	0.01	Sharpness of the contrastive (InfoNCE) alignment.
contrastive_warmup_epochs	integer/null	None	Epochs over which the alignment strength ramps up from 0. Default: 60% of the epochs (the published model used 1200 of 2000).
loss_weights	section
seed	integer	0	Random seed for weight initialisation and data shuffling.
device	string	auto	'auto', 'cpu' or 'cuda'.
save_every	integer	50	Write an intermediate checkpoint every N epochs.

training.loss_weights:

setting	type	default	meaning
joint_reconstruction	number	1.0	Rebuild the crystal from the joint (structure+property) latent.
joint_property	number	1.0	Predict the properties from the joint latent.
property_reconstruction	number	0.5	Rebuild the crystal from the property latent alone - this is what makes inverse design possible.
property_property	number	0.5	Predict the properties back from the property latent.

generation:

setting	type	default	meaning
family	string	perovskite_abx3	Built-in family name or path to your own family .yaml file.
variant	string/null	None	Sub-family, e.g. 'halide' or 'oxide' for perovskites.
objectives	array	required	Which properties to steer and how.
targets	array	required	One entry per design target, e.g. {dir_gap: 1.5, heat_all: -0.1}. Each runs its own search.
per_target	integer	4	Candidates to save for each target.
population	integer	48	Latent vectors optimised in parallel in each round.
rounds	integer	20	Maximum search rounds per target.
steps	integer	800	Gradient steps per round.
learning_rate	number	0.0012	Step size of the latent search.
anchor_weight	number	12.0	Keeps the search close to the latent that the target properties map to.
temperature_start	number	1.6	Softness of element choices at the start of a round.
temperature_end	number	0.9	Softness of element choices at the end of a round.
diversity_weight	number	0.9	Pushes the parallel searches apart from each other.
diversity_tau	number	0.25
history_weight	number	1.0	Pushes new searches away from latents that already produced a saved candidate.
history_tau	number	0.25
restart_patience	integer	250	Steps without improvement before a search is restarted with noise.
restart_noise	number	0.35
grad_clip	number	1.0
z_clip	number	5.0
init_sigma	number	0.35	Noise added to the starting latents.
init_anchor_mix	number	0.75	Share of the property-encoder latent in the starting point.
init_rff_mix	number	0.6	Share of the target-dependent random-feature direction in the starting point.
init_noise_mix	number	0.25	Share of random noise in the starting point.
rff_frequencies	integer	48
decode_temperature	number	1.25	Randomness when turning element scores into a choice.
decode_topk	integer	12	Only the k best-scoring elements of a group can be chosen.
decode_tries	integer	12	Attempts per latent to find a composition that passes all constraints.
anti_repeat_alpha	number	0.6	Down-weights elements that were already used in saved candidates.
anti_repeat_group	string/null	None	Group the anti-repeat applies to (default: first sampled group).
geometry_scale	number	1.0	Global strength of the family's search terms.
property_first	boolean	False	Ignore the family's search terms during the search (properties only).
dedup_formula	boolean	True	Save each composition at most once.
min_cosine_sep	number	0.985	Skip candidates whose latent is this similar (cosine) to an already saved one.
unique_decimals	integer	3
exclude_elements	array		Elements never to use, e.g. [Pb, Cd].
only_elements	object		Restrict groups to these elements, e.g. {B: [Ti, Zr, Hf]}.
overrides	object		Change parameters of the family's search terms or constraints, e.g. {tolerance_factor: {max: 1.0}}.
extra_constraints	array		Additional rules appended to the family's constraints, e.g. [{name: property_window, property: dir_gap, min: 1.0, max: 3.0}].
disabled_rules	array		Rules to switch off for this run, by name (or id when two rules share a name), e.g. [charge_neutrality]. The generation report lists them.
seed	integer	937	Random seed of the search.
amp	boolean	True	Use mixed precision on GPUs.
output_prefix	string/null	None	File-name prefix of saved CIFs (default: family variant).

generation.objectives[]

setting	type	default	meaning
property	string	required	Property (column name) this objective refers to.
loss	l2 / l1 / at_most / at_least	l2	How a prediction is compared with the target: l2 = squared distance, l1 = absolute distance, at_most = only values above the target are penalised, at_least = only values below.
weight	number	10000.0	Importance of this objective during the latent search.
select_weight	number	1.0	Importance of this objective when ranking finished candidates.
select_loss	string/null	None	Comparison used for ranking (defaults to 'loss').