matplotlib==3.1.1
numpy==1.17.4
torch==1.4.0
tdqm==4.43.0
python>=3.6.9
tested modules are marked by the check marks. All classes could be used directly once the header module is imported, please check the code examples.
All initialization data for MD / NN trainer should be stored at ./Langevin_Machine_Learning/init folder, using the phase space class ( look at code example MCMC ) and all the states to be used for ML integrator should be stored at ./Langevin_Machine_Learning/Integrator/states folder according to the naming convention (eg. ‘LF_state_best.pth’ for Leapfrog). Moreover T999 is just a dummy value in init, to indicate a mix of temperature from 1 - 10 used for test dataset. It is used to compare the performance between ML and non-ML.
Secondly, to view the plot of training loss/ validation loss and hamiltonian, by using backend tensorboard by pytorch, input following command in the terminal :
$ tensorboard --logdir==runs
By default, all the data is stored in the runs folder which has the same path to the Langevin_Machine_Learning folder.
Full documentation could be found by using python help() function on the enquired class
energy = Hamiltonian.Hamiltonian() # energy model container energy.append(Hamiltonian.asymmetrical_double_well())
configuration = { ‘kB’ : 1.0, # put as a constant ‘Temperature’ : 1.0, ‘DIM’ : 1, ‘m’ : 1, ‘N’ : 1, ‘hamiltonian’ : energy, }
integration_setting = { ‘iterations’ : 2500, ‘DumpFreq’ : 1, ‘dq’ : 1.0, }
configuration.update(integration_setting) # combine the 2 dictionaries
MSMC_integrator = Integrator.MSMC(**configuration) q_hist = MSMC_integrator.integrate()
Momentum_sampler = Integrator.momentum_sampler(**configuration) p_hist = Momentum_sampler.integrate() confStat.plot_stat(q_hist, p_hist, ‘q_dist’, **configuration)
DIM = q_hist.shape[-1] # flatten the q_hist of samples x N X DIM to a phase space q_list = q_hist.reshape(-1,DIM) p_list = p_hist.reshape(-1,DIM) phase_space = phase_space.phase_space() # wrapper of phase space class phase_space.set_q(q_list) phase_space.set_p(p_list) phase_space.write(filename = ./PATH TO INIT FOLDER/phase_space_N2500_T1_DIM1.npy) #the folder init could be used for next NN / MD initialization, check the convention of name saving
2. Using MD Langevin Simulator ( NVT Ensemble / NVE Ensemble if gamma = 0 )
import Langevin_Machine_Learning.hamiltonian as Hamiltonian import Langevin_Machine_Learning.Integrator as Integrator import Langevin_Machine_Learning.Integrator.methods as methods import Langevin_Machine_Learning.utils as confStat # configuration statistics
energy = Hamiltonian.Hamiltonian() energy.append(Hamiltonian.asymmetrical_double_well()) energy.append(Hamiltonian.Lennard_Jones(epsilon = 1, sigma = 1)) # arbitrary constants energy.append(Hamiltonian.kinetic_energy(mass = 1))
configuration = { ‘kB’ : 1.0, # put as a constant ‘Temperature’ : 1.0, # desired temperature for NVT Ensemble ‘DIM’ : 1, ‘m’ : 1, ‘N’ : 100, ‘hamiltonian’ : energy, ‘periodicity’ : True, # only periodic boundary condition is applied here ‘BoxSize’ : 10, }
integration_setting = { ‘iterations’ : 500, ‘DumpFreq’ : 1, ‘gamma’ : 0, # gamma 0 turns off the Langevin heat bath, setting it to NVE Ensemble ‘time_step’ : 0.01, ‘integrator_method’ : methods.position_verlet, #method class to be passed }
configuration.update(integration_setting) MD_integrator = Integrator.Langevin(**configuration) #only load for initial condition Temperature = 1.0 MD_integrator.set_phase_space(samples = 100)
#update configuration after loading configuration = MD_integrator.get_configuration() q_hist, p_hist = MD_integrator.integrate() confStat.plot_stat(q_hist, p_hist, ‘q_dist’,**configuration) #plot the statistic of q distribution based on current state configuration
#to save the current phase space to continue as a checkpoint MD_integrator.save_phase_space() # by default, it is in init file
3. Using Stacked NN trainer
from Langevin_Machine_Learning.HNN.loss import qp_MSE_loss
import Langevin_Machine_Learning.HNN as HNN
import Langevin_Machine_Learning.hamiltonian as Hamiltonian
import Langevin_Machine_Learning.Integrator.methods as methods
import torch.optim as optim
energy = Hamiltonian.Hamiltonian() # base constructor container energy.append(Hamiltonian.asymmetrical_double_well()) energy.append(Hamiltonian.kinetic_energy(mass = 1))
configuration = { ‘kB’ : 1.0, # put as a constant ‘Temperature’ : 1.0, ‘DIM’ : 1, ‘m’ : 1, ‘hamiltonian’ : energy, }
#Generate dataset ground truth, turn off Langevin drag coefficient integration_setting = { ‘iterations’ : 500, ‘DumpFreq’ : 1, ‘gamma’ : 0, # turn off Langevin heat bath ‘time_step’ : 0.001, ‘integrator_method’ : methods.position_verlet }
model = HNN.MLP2H_Separable_Hamil_PV(2, 20) loss = qp_MSE_loss
lr = 1e-3 NN_trainer_setting = { ‘optim’ : optim.Adam(model.parameters(), lr = lr), ‘model’ : model, ‘loss’ : loss, ‘epoch’ : 500, ‘batch_size’ : 32, }
Dataset_setting = { ‘Temperature_List’ : [1,2,3,4,5,6,7,8,9,10], ‘sample’ : 2500, # samples per temperature }
configuration.update(integration_setting) configuration.update(NN_trainer_setting) configuration.update(Dataset_setting)
SHNN = HNN.SHNN_trainer(level = 2, folder_name = ‘PV_Hidden20_Batch32’, **configuration) SHNN.train() ```