Use self interaction energies directly from paper (cabf2708) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/symmetry_function_regression.py

+0 −15

Original line number	Diff line number	Diff line
		@@ -283,21 +283,6 @@ class ANIRegression(TensorGraph):
		"""
		num_atoms = atomic_nums.shape[0]

		# minimizer_kwargs = {
		# "jac": self.grad_one,
		# "args": (atomic_nums,),
		# "method": "CG",
		# "options": {"disp": True},
		# }

		# res = scipy.optimize.basinhopping(
		# self.pred_one,
		# X,
		# niter=75,
		# T=0.015, # ~10kcal/mol
		# stepsize=0.17,
		# minimizer_kwargs=minimizer_kwargs)

		res = scipy.optimize.minimize(
		self.pred_one,
		X,

examples/roitberg/roitberg.py

+15 −21

Original line number	Diff line number	Diff line
		@@ -16,7 +16,8 @@ def convert_species_to_atomic_nums(s):


		# replace with your own scratch directory
		data_dir = "/media/yutong/datablob/deepchem"
		data_dir = "/media/yutong/datablob/datasets"
		model_dir = "/media/yutong/datablob/models"

		all_dir = os.path.join(data_dir, "all")
		test_dir = os.path.join(data_dir, "test")
		@@ -57,10 +58,10 @@ def load_roiterberg_ANI(mode="atomization"):
		hdf5files = [
		'ani_gdb_s01.h5',
		'ani_gdb_s02.h5',
		# 'ani_gdb_s03.h5',
		# 'ani_gdb_s04.h5',
		# 'ani_gdb_s05.h5',
		# 'ani_gdb_s06.h5',
		'ani_gdb_s03.h5',
		'ani_gdb_s04.h5',
		'ani_gdb_s05.h5',
		'ani_gdb_s06.h5',
		# 'ani_gdb_s07.h5',
		# 'ani_gdb_s08.h5'
		]
		@@ -111,21 +112,20 @@ def load_roiterberg_ANI(mode="atomization"):
		offset = np.amin(E)
		elif mode == "atomization":

		# approximation via B3LYP/6-31G* in Jaguar
		# to shrink the dynamic range
		atomizationMapInHartrees = {
		# self-interaction energies taken from
		# https://github.com/isayev/ANI1_dataset README
		atomizationEnergies = {
		0: 0,
		1: -0.50027278266,
		6: -37.77568079030,
		7: -54.47752723207,
		8: -74.95668301867,
		16: -398.04142581461
		1: -0.500607632585,
		6: -37.8302333826,
		7: -54.5680045287,
		8: -75.0362229210
		}

		offset = 0

		for z in nonpadded:
		offset -= atomizationMapInHartrees[z]
		offset -= atomizationEnergies[z]
		elif mode == "absolute":
		offset = 0
		else:
		@@ -196,8 +196,6 @@ if __name__ == "__main__":
		dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")
		]

		model_dir = "/tmp/ani8.npz"

		if os.path.exists(model_dir):
		print("Restoring existing model...")
		model = dc.models.ANIRegression.load_numpy(model_dir=model_dir)
		@@ -238,13 +236,9 @@ if __name__ == "__main__":
		mode="regression")

		# # For production, set nb_epoch to 100+
		for i in range(1):
		for i in range(10):
		model.fit(train_dataset, nb_epoch=1, checkpoint_interval=100)


		# model.save_numpy(save_path)
		# dc.models.ANIRegression.load_numpy(save_path, **kwargs)

		print("Saving model...")
		model.save_numpy()
		print("Done.")

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