def __init__(self, files, cnstfile, saefile, nnfprefix, Nnet, gpuid=0, sinet=False):
self.files=files #List of files containing the molecules to run md on
self.coor_train=[] #Where the coordinates of the molecules with high standard deviation will be saved
self.Na_train=[] #Where the number of atoms of the molecules with high standard deviation will be saved
self.S_train=[] #Where the atomic species of the molecules with high standard deviation will be saved
self.hstd=[] #Where the standard deviation of the molecules with high standard deviation will be saved
#The path to the network
self.net = ensemblemolecule(cnstfile, saefile, nnfprefix, Nnet, gpuid) #Load the network
def __init__(self, netdict, storedir, smilefile, Nmol, Nsamp, sigma, rng, seed, gpuid=0):
self.storedir = storedir
self.Nmol = Nmol
self.Nsamp = Nsamp
self.sigma = sigma
self.rng = rng
smiles = [sm for sm in np.loadtxt(smilefile, dtype=np.str,comments=None)]
np.random.seed(seed)
np.random.shuffle(smiles)
self.smiles = smiles[0:Nmol*20]
self.ens = ensemblemolecule(netdict['cnstfile'], netdict['saefile'], netdict['nnfprefix'], netdict['num_nets'], gpuid)
def __init__(self,cnstfile,saefile,nnfprefix,Nnet,gpuid=0, sinet=False):
self.Nbad = 0
# Number of networks
self.Nn = Nnet
# Build ANI ensemble
self.aens = ensemblemolecule(cnstfile, saefile, nnfprefix, Nnet, gpuid)
# Construct pyNeuroChem class
#self.ncl = [pync.molecule(cnstfile, saefile, nnfprefix+str(i)+'/networks/', gpuid, sinet) for i in range(self.Nn)]
# Initalize PBC
self.pbc = False
self.pbl = 0.0
def __init__(self,
cnstfile,
saefile,
nnfprefix,
Nnet,
molecule_list,
gpuid=0,
sinet=False):
# Molecules list
self.mols = molecule_list
# Number of networks
self.Nn = Nnet
# Build ANI ensemble
self.aens = ensemblemolecule(cnstfile, saefile, nnfprefix, Nnet, gpuid)
def __init__(self):
# Set locations of all required network files
rootdir = os.environ.get('ANI_PATH')
wkdir = rootdir + '/ANI-1x/' # Note the relative path
cnstfile = wkdir + 'rHCNO-4.6A_16-3.1A_a4-8.params' # AEV parameters
saefile = wkdir + 'sae_6-31gd.dat' # Atomic shifts
nnfdir = wkdir + '/train' # network prefix
Nn = 5 # Number of networks in the ensemble
gpu = 0 # GPU ID -- this defaults to 0
# Initilize network ensemble
try:
self.aens = ensemblemolecule(cnstfile, saefile, nnfdir, Nn, gpu)
except RuntimeError:
print('Couldn\'t load ASI network (GPU probably not available)!')
return
def __init__(self,
cnstfile,
saefile,
nnfprefix,
Nnet,
solv_list,
solu_list,
gpuid=0,
sinet=False):
# Molecules list
self.solv_list = solv_list
self.solu_list = solu_list
# Number of networks
self.Nn = Nnet
# Build ANI ensemble
self.aens = ensemblemolecule(cnstfile, saefile, nnfprefix, Nnet, gpuid)
self.edgepad = 1.0
self.mindist = 2.0
def __init__(self,cnstfile,saefile,nnfdir,Nn,gpuid=0, sinet=False):
# Construct pyNeuroChem class
#self.nc = [pync.molecule(cnstfile, saefile, nnfdir+str(i)+'/', gpuid, sinet) for i in range(Nn)]
self.ens = ensemblemolecule(cnstfile,saefile,nnfdir,Nn,gpuid,sinet)
Nn = 5
#nnfdir = wkdir + 'networks/'
# Load molecule
mol = read(molfile)
#print('test')
L = 70.0
mol.set_cell(([[L, 0, 0], [0, L, 0], [0, 0, L]]))
mol.set_pbc((True, True, True))
#print(mol.get_chemical_symbols())
# Set NC
aens = ensemblemolecule(cnstfile, saefile, nnfdir, Nn, 5)
# Set ANI calculator
mol.set_calculator(ANIENS(aens, sdmx=20000000.0))
print(mol.get_potential_energy())
print(np.where(mol.get_forces() > 200.0))
print("size: ", len(mol.get_chemical_symbols()))
# Optimize molecule
start_time = time.time()
dyn = QuasiNewton(mol)
dyn.run(fmax=C)
print('[ANI Total time:', time.time() - start_time, 'seconds]')
