mlqd 1.1.1

MLQD: A Python Package for Machine Learning-based Quantum Dissipative Dynamics

MLQD- A Python package for Machine Learning-based Quantum Dissipative Dynamics

In MLQD, we provide three Machine Learning (ML) methods for propagating Qauntum Dissipative Dynamics [For Licence statement, please [Click here]]

The corresponding preprint is on arXiv http://arxiv.org/abs/2303.01264

• Kernel Ridge Regression (KRR)-based recursive (iterative) Quantum Dissipative Dynamics method: Here is the corresponding article $\boldsymbol{\rightarrow}$ Speeding up quantum dissipative dynamics of open systems with kernel methods. Recently, we have performed a comparative study where KKR method outperforms NN models, here is the article $\boldsymbol{\rightarrow}$ A comparative study of different machine learning methods for dissipative quantum dynamics
• AIQD non-recursive (non-iterative) approach: Here is the corresponding article $\boldsymbol{\rightarrow}$ Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics
• The blazingly fast OSTL non-recursive (non-iterative) approach: Here is the corresponding article $\boldsymbol{\rightarrow}$ One-Shot Trajectory Learning of Open Quantum Systems Dynamics

We have also released a database with quantum dissipative dynamics datasets https://doi.org/10.48550/arXiv.2301.12096, you can use it to train your own model

For hands-on-paractice MLQD provides

• Propagation of dynamics with the existing trained models [Click here]
• Training convolutional neural networks (CNN) and KRR models on the data [Click here]
• Transformation of data into input files X and Y [Click here] and direct training with out transformation [Click here]
• Optimization of the hyperparameters in CNN and KRR models [Click here]
• Auto-plotting

You can also Run MLQD on the XACS cloud computing platform (https://xacs.xmu.edu.cn/) and the user manual is here (http://mlatom.com/manual/#mlqd)

Installation and dependencies

Some dependencies:

Create a conda environment

conda create --name mlqd

Activate the environment

conda activate mlqd

While installing MLQD, it will also install the following required dependencies

• tensorflow conda install -c conda-forge tensorflow

• scikit-learn conda install -c anaconda scikit-learn

• hyperopt conda install -c conda-forge hyperopt

• matplotlib conda install -c conda-forge matplotlib

• MLatom pip install MLatom

User-Manual

MLQD provides User-Manual and to get to that, we need to import quant_dyn class from evolution.py [in MLQD folder]

from mlqd.evolution import quant_dyn

and then call quant_dyn with out passing any parameters, i.e., quant_dyn()

For hands-on-paractice, we provide Jupyter Notebooks at https://github.com/Arif-PhyChem/MLQD

Dynamics Propagation [Go to Top]

(Go to Jupyter_Notebooks folder for ready made scripts) We provide already trained QD models which can be found here [coming soon], you can download them and test the code. If you want to train your own model, then go to Training on your own data section. First of all, we need to import quant_dyn class from evolution.py .

from evolution import quant_dyn

• KRR model:

For KRR model, You need to provide the following parameters. Just to emphsize, MLQD is using MLatom package [http://mlatom.com/] for KRR in the backend.

param={ 
        'time': 20,                     # float: Propagation time in picoseconds (ps)  for FMO complex and in (a.u.) for spin-boson model
        'time_step': 0.1,               # float: Time-step for time-propagation (you are restricted to the time-step used in the training data). Default for KRR is 0.1
        'QDmodel': 'useQDmodel',        # str: In MLQD, the dafault option is useQDmodel tells the MLQD to propagate dynamics with an existing trained model
        'QDmodelType': 'KRR',           # str: The type of model we wanna use (KRR, AIQD, or OSTL). Here KRR and the default option is OSTL
        'XfileIn': 'x_input',           # str: Name of a txt file where a short time trajectory (equal to the length the input-model was trained on) is saved. In x-input file, the data should be row wise.  
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelIn': 'KRR_SB_model',    # str: (Not optional for useQDmodel), provide the name of the trained ML model
        'QDtrajOut': 'Qd_trajectory'    # str: (Optional), File name where the trajectory should be saved
        'xlim': 20,                     # float: Xaxis limit for plotting. Default is equal to propagation time
        'plotNstates': 2,               # int: Number of states to be plotted. Default option is to plot all of them 
        'refTraj': 'test_set/sb/2_epsilon-0.0_Delta-1.0_lambda-0.1_gamma-4.0_beta-1.0.npy' # str: Reference Trajectory for plotting. If not provided, MLQD will ignore plotting
    }
quant_dyn(**param)

• AIQD model:

For each time-step, the AIQD approach predicts the corresponding reduced density matrix in the following format $$\mathcal{R}[\rho_{11}(t)], \mathcal{R}[\rho_{12}(t)], \mathcal{I}[\rho_{12}(t)] \dots, \mathcal{R}[\rho_{1N}(t)], \mathcal{I}[\rho_{1N}(t)], \mathcal{R}[\rho_{22}(t)], \dots, \mathcal{R}[\rho_{2N}(t)], \mathcal{I}[\rho_{2N}(t)], \mathcal{R}[\rho_{33}(t)], \dots, \mathcal{R}[\rho_{3N}(t)],\mathcal{I}[\rho_{3N}(t)],\dots, \dots, \mathcal{R}[\rho_{NN}(t)]$$

where $N$ is the dimension of the reduced density matrix and $\mathcal{R}$ and $\mathcal{I}$ represent the real and imaginary parts of the off-diagonal terms, respectively.

While training the model, the normalization constants for $\epsilon, \Delta, \gamma, \lambda$ and $\beta$, number of Logistic functions and constants for Logistic functions, i.e., a, b, c, d, were saved as a pickle file (with the similar name as the model name). Thus the user does not need to provide them, MLQD will automatically load them.

I. Case-1: If a user wants to provide parameters for propagation in a file, in the shape of an array or in the form of a list. (In this case, the user needs to normalized the data him/herself). AIQD uses a logistic function to normalize the dimension of time, i.e., $$f(t) = a/(1 + b \exp(-(t + c)/d))$$ where $a, b, c$ and $d$ are constants. Check out the Supplementary Figure 3 of our AIQD papar Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

param={ 
        'n_states': 2,                          # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'time': 20,                             # float: Propagation time in picoseconds (ps) for FMO complex and in (a.u.) for spin-boson model
        'time_step': 0.05,                      # float: Time-step for time-propagation (you are not restricted to the time-step used in the training data, however better  stick to that for good accuracy). Default values are 0.05 (a.u.) for spin-boson model) and 5fs for FMO complex
        'QDmodel': 'useQDmodel',                # str: In MLQD, the dafault option is useQDmodel tells the MLQD to propagate dynamics with an existing trained model
        'QDmodelType': 'AIQD',                  # str: Type of model we wanna use, here AIQD. The default option is OSTL
        'XfileIn': 'x_input',                   # str: Input parameters should be in the same format as the model was trained on. Here "x_input" can be a txt file ('XfileIn': 'x_input'). It can be a list or an array and in this case you need to pass the name of the array or list (XfileIn = x_input). 
        'systemType': 'SB',                     # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelIn': 'AIQD_SB_model.hdf5',      # str: (Not Optional for useQDmodel), provide the name of the trained ML model
        'QDtrajOut': 'Qd_trajectory'            # str: (Optional), File name where the trajectory should be saved
        'xlim': 20,                             # float: Xaxis limit for plotting. Default is equal to propagation time
        'plotNstates': 2,                       # int: Number of states to be plotted. Default option is to plot all of them 
        'refTraj': 'test_set/sb/2_epsilon-0.0_Delta-1.0_lambda-0.1_gamma-4.0_beta-1.0.npy' # str: Reference Trajectory for plotting. If not provided, MLQD will ignore plotting
    }
quant_dyn(**param)

II. Case-2: A user can also just provide simulation parameters (Characteristic frequency, System-bath coupling strength, Temperature (or inverse temperature) etc.) and MLQD will predict the correspinding dynamics.

param={ 
        'initState': 1,                         # int:  Initial state with Initial Excitation case (only required in FMO complex case, Default is '1')
        'n_states': 2,                          # Int:  Number of states (SB) or sites (FMO). Default is 2 (SB) and 7 (FMO).
        'time': 20,                             # float: Propagation time in picoseconds (ps) for FMO complex and in (a.u.) for spin-boson model
        'time_step': 0.05,                      # float: Time-step for time-propagation (you are not restricted to the time-step used in the training data, however better stick to that for good accuracy) Default values are 0.05 (a.u.) for spin-boson model) and 5fs for FMO complex
        'energyDiff': 1.0,                      # float: Energy difference between the two states. Only required in SB model
        'Delta': 1.0,                           # float: The tunneling matrix element. Only required in SB model
        'gamma': 10,                            # float: Characteristic frequency 
        'lamb': 0.1,                            # float: System-bath coupling strength  
        'temp': 1.0,                            # float: temperature or (inverse temperature) 
        'QDmodel': 'useQDmodel',                # str: In MLQD, the dafault option is useQDmodel tells the MLQD to propagate dynamics with an existing trained model
        'QDmodelType': 'AIQD',                  # str: The type of model we wanna use, here AIQD. The default option is OSTL
        'systemType': 'SB',                     # str: (Not optional)  Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelIn': 'AIQD_SB_model.hdf5',      # str: (Not Optional for useQDmodel), provide the name of the trained ML model
        'QDtrajOut': 'Qd_trajectory'            # str: (Optional), File name where the trajectory should be saved
        'xlim': 20,                             # float: Xaxis limit for plotting. Default is equal to propagation time
        'plotNstates': 2,                       # int: Number of states to be plotted. Default option is to plot all of them 
        'refTraj': 'test_set/sb/2_epsilon-0.0_Delta-1.0_lambda-0.1_gamma-4.0_beta-1.0.npy' # str: Reference Trajectory for plotting. If not provided, MLQD will ignore plotting
    }
quant_dyn(**param)

• OSTL model (Recommended for fast and smooth propagation of dyanmics) For OSTL, the input is the same as AIQD except in OSTL, we just don't use the logistic functions here. The OSTL predict the whole dynamics in one shot in the following format $$\boldsymbol{\mathcal{Y}}(t_0), \boldsymbol{\mathcal{Y}}(t_1), \dots, \boldsymbol{\mathcal{Y}}(t_{k-1}), \boldsymbol{\mathcal{Y}}(t_k), \boldsymbol{\mathcal{Y}}(t_{k+1}), \dots, \boldsymbol{\mathcal{Y}}(t_M)$$ where $$\boldsymbol{\mathcal{Y}}(t) = \mathcal{R}[\rho_{11}(t)], \mathcal{R}[\rho_{12}(t)], \mathcal{I}[\rho_{12}(t)] \dots, \mathcal{R}[\rho_{1N}(t)], \mathcal{I}[\rho_{1N}(t)], \mathcal{R}[\rho_{22}(t)], \dots, \mathcal{R}[\rho_{2N}(t)], \mathcal{I}[\rho_{2N}(t)], \mathcal{R}[\rho_{33}(t)], \dots, \mathcal{R}[\rho_{3N}(t)],\mathcal{I}[\rho_{3N}(t)],\dots, \dots, \mathcal{R}[\rho_{NN}(t)]$$

where $N$ is the dimension of the reduced density matrix and $\mathcal{R}$ and $\mathcal{I}$ represent the real and imaginary parts of the off-diagonal terms, respectively.

While training the model, the normalization constants for $\epsilon, \Delta, \gamma, \lambda$ and $\beta$ were saved as a pickle file (with the similar name as the model name). Thus the user does not need to provide them, MLQD will automatically load them.

I. Case-I: If a user wants to provide parameters for propagation in a file, in the shape of an array or in the form of a list. (In this case, the user needs to normalized the data him/herself).

param={ 
        'n_states': 2,                          # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'time': 20,                             # float: Propagation time in picoseconds (ps) for FMO complex and in (a.u.) for spin-boson model
        'time_step': 0.1,                       # float: Time-step for time-propagation (you are not restricted to the time-step used in the training data, however better  stick to that for good accuracy). Default values are 0.05 (a.u.) for spin-boson model) and 5fs for FMO complex
        'QDmodel': 'useQDmodel',                # str: In MLQD, the dafault option is useQDmodel tells the MLQD to propagate dynamics with an existing trained model
        'QDmodelType': 'AIQD',                  # str: Type of model we wanna use, here AIQD. The default option is OSTL
        'XfileIn': 'x_input',                   # str: Input parameters should be in the same format as the model was trained on. Here "x_input" can be a txt file ('XfileIn': 'x_input'). It can be a list or an array and in this case, you need to pass the name of the array or list (XfileIn = x_input). 
        'systemType': 'SB',                     # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelIn': 'OSTL_SB_model.hdf5',      # str: (Not Optional for useQDmodel), provide the name of the trained ML model
        'QDtrajOut': 'Qd_trajectory'            # str: (Optional), File name where the trajectory should be saved
        'xlim': 20,                             # float: Xaxis limit for plotting. Default is equal to propagation time
        'plotNstates': 2,                       # int: Number of states to be plotted. Default option is to plot all of them 
        'refTraj': 'test_set/sb/2_epsilon-0.0_Delta-1.0_lambda-0.1_gamma-4.0_beta-1.0.npy' # str: Reference Trajectory for plotting. If not provided, MLQD will ignore plotting
    }
quant_dyn(**param)

II. Case-2 A user can also just provide simulation parameters (Characteristic frequency, System-bath coupling strength, Temperature etc.) and MLQD will predict the correspinding dynamics.

param={ 
        'n_states': 2,                          # int:  Number of states (SB) or sites (FMO). Default is 2 (SB) and 7 (FMO).
        'time': 20,                             # float: Propagation time in picoseconds (ps)  for FMO complex and in (a.u.) for spin-boson model
        'time_step': 0.05,                      # float: Time-step for time-propagation (OSTL does not use it, however will use it in the output file). Default values are 0.05 (a.u.) for spin-boson model) and 5fs for FMO complex
        'energyDiff': 1.0,                      # float: Energy difference between the two states. Only required in SB model
        'Delta': 1.0,                           # float: The tunneling matrix element. Only required in SB model
        'gamma': 10,                            # float: Characteristic frequency 
        'lamb': 0.1,                            # float: System-bath coupling strength 
        'temp': 1.0,                            # float: temperature or inverse temperature
        'QDmodel': 'useQDmodel',                # str: In MLQD, the dafault option is useQDmodel tells the MLQD to propagate dynamics with an existing trained model
        'QDmodelType': 'OSTL',                  # str: The type of model we wanna use, here AIQD. The default option is OSTL
        'systemType': 'SB',                     # str: (Not optional)  Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelIn': 'OSTL_SB_model.hdf5',      # str: (Not Optional for useQDmodel), provide the name of the trained ML model
        'QDtrajOut': 'Qd_trajectory'            # str: (Optional), File name where the trajectory should be saved 
        'xlim': 20,                             # float: Xaxis limit for plotting. Default is equal to propagation time
        'plotNstates': 2,                       # int: Number of states to be plotted. Default option is to plot all of them 
        'refTraj': 'test_set/sb/2_epsilon-0.0_Delta-1.0_lambda-0.1_gamma-4.0_beta-1.0.npy' # str: Reference Trajectory for plotting. If not provided, MLQD will ignore plotting
    }
 quant_dyn(**param)

Model training on your own data [Go to Top]

Here we will show how to train data on you data. If you don't have your own data, you can go to our recently released dataset QD3SET-1: A Database with Quantum Dissipative Dynamics Datasets and download the data. If you don't want to train your own model and want to use our provided ready made trained models, click here [Coming soon] and how to to propagate dynamics with it, go to Dynamics Propagation.

Training a model along with the preparation of training data and optimization of hyperparameters

• KRR

MLQD is using MLatom package [http://mlatom.com/] for KRR in the backend. For KRR model, You need to provide the following parameters

param={ 
        'QDmodel': 'createQDmodel',     # str: create QD model. The dafault option is useQDmodel
        'QDmodelType': 'KRR',           # str: The type of model. Here KRR and the default option is OSTL
        'prepInput' : 'True',           # str: Prepare input files from the data (Default False)
        'XfileIn': 'x_train',           # str: (Optional, txt file) The prepared X file will be saved at the provided file name 
        'YfileIn': 'y_train',           # str: (Optional, txt file) The prepared Y file will be saved at the provided file name
        'dataPath': 'data/sb' ,         # str: Data path
        'dataCol': 1,                   # int: Default is 1, we may have multiple columns in our data files, mention a single column (KRR model works only for single output)
        'dtype': 'real',                # str: Default is real. If the data in complex and if we pass 'real', it will prepare data only for real part and if we pass 'imag' is mentioned, only imaginary data will be considered. 
        'xlength': 81,                  # int:  Default is 81. Length of the short trajectory which will be used as an input
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default values)
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelOut': 'KRR_SB_model'    # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

• AIQD

Just to emphasize, the data files should be in the same naming and type format as was adopted in our QD3SET-1: A Database with Quantum Dissipative Dynamics Datasets

param={ 
        'n_states': 2,                  # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'time': 20,                     # float: Propagation time in picoseconds (ps) for FMO complex and in (a.u.)for spin-boson model
        'time_step': 0.05,              # float: Time-step for time-propagation. Default values are 0.05 (a.u.) for spin-boson model and 5fs for FMO complex.
        'QDmodel': 'createQDmodel',     # str: createQDmodel, the dafault option is useQDmodel
        'QDmodelType': 'AIQD',          # str: Type of model. The default option is OSTL
        'prepInput' : 'True',           # str: Prepare input files from the data (Default False)
        'XfileIn': 'x_data',            # str: (Optional, npy file) The prepared X file will be saved at the provided file name 
        'YfileIn': 'y_data',            # str: (Optional, npy file) The prepared Y file will be saved at the provided file name 
        'numLogf': 10,                  # int: Number of Logistic function for the normalization of time dimension. Default value is 1.0.    
        'LogCa' : 1.0,                  # float: Coefficient "a" in the logistic function, default values is 1.0 
        'LogCb' : 15.0,                 # float: Coefficient "b" in the logistic function, default values is 15.0 
        'LogCc' : -1.0,                 # float: Coefficient "a" in the logistic function, default values is -1.0 
        'LogCd' : 1.0,                  # float: Coefficient "d" in the logistic function, default values is 1.0 
        'energyNorm': 1.0,              # float: Normalizer for the energy difference between the two states (in spin-boson model)
        'DeltaNorm': 1.0,               # float: Normalizer for the the tunneling matrix element (in spin-boson model)
        'gammaNorm': 10,                # float: Normalizer for Characteristic frequency. Default values are 500  (FMO complex) and 10 (spin-boson model)
        'lambNorm': 1.0,                # float: Normalizer for System-bath coupling strength. Default values are 520 (FMO complex) and 1 (spin-boson model)
        'tempNorm': 1.0,                # float: Normalizer for temperature. Default values are 510 (FMO complex) and 1 (spin-boson model)
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default structure)
        'patience': 10,                 # int: Patience for early stopping in CNN training 
        'OptEpochs': 10,                # int: Number of epochs for CNN optimization 
        'TrEpochs': 10,                 # int: Number of epochs for CNN training  
        'max_evals': 100,               # int: Number of maximum evaluations in hyperopt optimization
        'dataPath': 'sb_data',          # str: Data path
        'QDmodelOut': 'AIQD_SB_model'   # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

• OSTL

Just to emphasize, the data files should be in the same naming and type format as was adopted in our QD3SET-1: A Database with Quantum Dissipative Dynamics Datasets

param={ 
        'n_states': 2,                  # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'QDmodel': 'createQDmodel',     # str: createQDmodel, the dafault option is useQDmodel
        'QDmodelType': 'OSTL',          # str: Type of model. The default option is OSTL
        'prepInput' : 'True',           # str: Prepare input files from the data (Default False)
        'XfileIn': 'x_data',            # str: (Optional, npy file) The prepared X file will be saved at the provided file name 
        'YfileIn': 'y_data',            # str: (Optional, npy file) The prepared Y file will be saved at the provided file name
        'energyNorm': 1.0,              # float: Normalizer for the energy difference between the two states (in spin-boson model)
        'DeltaNorm': 1.0,               # float: Normalizer for the the tunneling matrix element (in spin-boson model)
        'gammaNorm': 10,                # float: Normalizer for Characteristic frequency. Default values are 500 (FMO complex) and 10 (spin-boson model)
        'lambNorm': 1.0,                # float: Normalizer for System-bath coupling strength. Default values are 520 (FMO complex) and 1 (spin-boson model)
        'tempNorm': 1.0,                # float: Normalizer for temperature. Default values are 510 (FMO complex) and 1 (spin-boson model)
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default structure)
        'patience': 10,                 # int: Patience for early stopping in CNN training
        'OptEpochs': 10,                # int: Number of epochs for CNN optimization 
        'TrEpochs': 10,                 # int: Number of epochs for CNN training  
        'max_evals': 100,               # int: Number of maximum evaluations in hyperopt optimization
        'dataPath': 'sb_data',          # str: Data path
        'QDmodelOut': 'OSTL_SB_model'   # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

Training a model without preparation of training data [Go to Top]

Let suppose we already have our prepared training data as was prepared in above examples, then

• KRR

For KRR model, You need to provide the following parameters

param={ 
        'QDmodel': 'createQDmodel',     # str: create QD model. The dafault option is useQDmodel
        'QDmodelType': 'KRR',           # str: The type of model. Here KRR and the default option is OSTL
        'XfileIn': 'x_train',           # str: (Not Optional, txt file) The X file 
        'YfileIn': 'y_train',           # str: (Not Optional, txt file) The Y file
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default values)
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'QDmodelOut': 'KRR_SB_model'    # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

• AIQD

Here provide the ready made X and Y files. Here normalization constants are provided so MLQD can save them for later use while predicting dynamics.

param={ 
        'n_states': 2,                  # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'QDmodel': 'createQDmodel',     # str: createQDmodel, the dafault option is useQDmodel
        'QDmodelType': 'AIQD',          # str: Type of model. The default option is OSTL
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'XfileIn': 'x_data',            # str: (Not Optional, npy file) The X file 
        'YfileIn': 'y_data',            # str: (Not Optional, npy file) The Y file 
        'numLogf': 10,                  # int: Number of Logistic function for the normalization of time dimension. Default value is 1.0.    
        'LogCa' : 1.0,                  # float: Coefficient "a" in the logistic function, default values is 1.0 
        'LogCb' : 15.0,                 # float: Coefficient "b" in the logistic function, default values is 15.0 
        'LogCc' : -1.0,                 # float: Coefficient "a" in the logistic function, default values is -1.0 
        'LogCd' : 1.0,                  # float: Coefficient "d" in the logistic function, default values is 1.0 
        'energyNorm': 1.0,              # float: Normalizer for the energy difference between the two states (in spin-boson model)
        'DeltaNorm': 1.0,               # float: Normalizer for the the tunneling matrix element (in spin-boson model)
        'gammaNorm': 10,                # float: Normalizer for Characteristic frequency. Default values are 500  (FMO complex) and 10 (spin-boson model)
        'lambNorm': 1.0,                # float: Normalizer for System-bath coupling strength. Default values are 520 (FMO complex) and 1 (spin-boson model)
        'tempNorm': 1.0,                # float: Normalizer for temperature. Default values are 510 (FMO complex) and 1 (spin-boson model)
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default structure)
        'patience': 10,                 # int: Patience for early stopping in CNN training
        'OptEpochs': 10,                # int: Number of epochs for CNN optimization 
        'TrEpochs': 10,                 # int: Number of epochs for CNN training  
        'max_evals': 100,               # int: Number of maximum evaluations in hyperopt optimization
        'QDmodelOut': 'AIQD_SB_model'   # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

• OSTL

Here provide the ready made X and Y files. Here normalization constants are provided so MLQD can save them for later use while predicting dynamics.

param={ 
        'n_states': 2,                  # int:  Number of states (SB) or sites (FMO), default 2 (SB) and 7 (FMO).
        'QDmodel': 'createQDmodel',     # str: createQDmodel, the dafault option is useQDmodel
        'QDmodelType': 'OSTL',          # str: Type of model. The default option is OSTL
        'systemType': 'SB',             # str: (Not optional) Need to define, wether your model is spin-boson (SB) or FMO complex (FMO) 
        'XfileIn': 'x_data',            # str: (Not Optional, npy file) The X file 
        'YfileIn': 'y_data',            # str: (Not Optional, npy file) The X file 
        'energyNorm': 1.0,              # float: Normalizer for the energy difference between the two states (in spin-boson model)
        'DeltaNorm': 1.0,               # float: Normalizer for the the tunneling matrix element (in spin-boson model)
        'gammaNorm': 10,                # float: Normalizer for Characteristic frequency. Default values are 500 (FMO complex) and 10 (spin-boson model)
        'lambNorm': 1.0,                # float: Normalizer for System-bath coupling strength. Default values are 520 (FMO complex) and 1 (spin-boson model)
        'tempNorm': 1.0,                # float: Normalizer for temperature. Default values are 510 (FMO complex) and 1 (spin-boson model)
        'hyperParam': 'True',           # str: Default is False, we can pass True (optimize the hyperparameters) or False (don't optimize and run with the default structure)
        'patience': 10,                 # int: Patience for early stopping in CNN training
        'OptEpochs': 10,                # int: Number of epochs for CNN optimization 
        'TrEpochs': 10,                 # int: Number of epochs for CNN training  
        'max_evals': 100,               # int: Number of maximum evaluations in hyperopt optimization
        'QDmodelOut': 'OSTL_SB_model'   # str: (Optional), providing a name to save the model at
    }
quant_dyn(**param)

Licence statement [Go to Top]

MLQD is a python package developed for Machine Learning-based Quantum Dissipative Dynamics                 https://github.com/Arif-PhyChem/MLQD
                   Copyright (c) 2022 Arif Ullah
All rights reserved. This work is licensed under the Apache Software License 2.0.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

The software is provided "as is" without warranty of any kind, express or implied, including but not limited to the warranties ofmerchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.

Cite as:

1. Ullah A. and Dral P. O., New Journal of Physics, 2021, 23(11), 113019
2. Ullah A. and Dral P. O., Nature Communications, 2022, 13(1), 1930
3. Ullah A. and Dral P. O., Journal of Physical Chemistry Letters, 2022, 13(26), 6037
4. Rodriguez L. E. H.; Ullah A.; Espinosa K. J. R.; Dral P. O. and A. A. Kananenka, Machine Learning: Science and Technology, 2022, 3(4), 045016"
5. Ullah A. and Dral P. O., arXiv:2303.01264 (2023)

Contributers List:

1. Arif Ullah (main)
2. Pavlo O. Dral