def test_crystal_model_v2(self):
cg = CrystalGraph()
s = Structure(Lattice.cubic(3), ['Si'], [[0, 0, 0]])
with ScratchDir('.'):
model = MEGNetModel(nfeat_edge=None,
nfeat_global=2,
nblocks=1,
lr=1e-2,
n1=4,
n2=4,
n3=4,
npass=1,
ntarget=1,
graph_converter=cg,
centers=np.linspace(0, 4, 10),
width=0.5)
model = model.train([s, s], [0.1, 0.1], epochs=2)
t = model.predict_structure(s)
self.assertTrue(t.shape == (1, ))
test_structures = structures[80:]
train_targets = targets[:80]
test_targets = targets[80:]
from megnet.models import MEGNetModel
from megnet.data.graph import GaussianDistance
from megnet.data.crystal import CrystalGraph
from megnet.utils.preprocessing import StandardScaler
import numpy as np
gc = CrystalGraph(bond_converter=GaussianDistance(np.linspace(0, 5, 100), 0.5),
cutoff=4)
model = MEGNetModel(100, 2, graph_converter=gc, lr=1e-3)
INTENSIVE = False # U0 is an extensive quantity
scaler = StandardScaler.from_training_data(train_structures,
train_targets,
is_intensive=INTENSIVE)
model.target_scaler = scaler
model.train(train_structures, train_targets, epochs=500, verbose=2)
predicted_tests = []
for i in test_structures:
predicted_tests.append(model.predict_structure(i).ravel()[0])
print(type(test_targets), type(predicted_tests))
for i in range(10):
print(test_targets[i], predicted_tests[i])