lpcML 0.1.2

LPC ML is a machine learning workflow developed to optimize and analyze the impact of different design parameters on laser power converters (LPCs) solar cells

LPC ML

LPC ML is a tool developed in the CiTIUS, USC by the MODEV group for training a multi-layer perceptron (MLP) neural network to optimize and analyze the impact of different design parameters on laser power converters (LPCs) solar cells.

Fig.1: GaAs-based horizontal laser power converter

Data used to feed the neural networks is shared in data/hLPC_GaAS_5W_ml.csv.

Installation

First you need to have installed pip3 on your system. For Ubuntu, open up a terminal and type:

sudo apt update
sudo apt install python3-pip

Installation of lpcML via pip3

Install the tool using pip3:

pip3 install lpcML

and check the library is installed by importing it from a python3 terminal:

import lpcML

Unless an error comes up, LPC ML is now installed on your environment.

⚠️ WARNING If the module is already installed, make sure to upgrade it to the latest version before using it.

pip3 install lpcML --upgrade

❗ CAUTION
To ensure the versions compatibility and avoiding the urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! error use the following command:

pip3 install --upgrade requests

First Steps

To store the simulation data from optimizations or an iterative simulation process into a json file, you can use the simulations_to_json.ipynb file.

An step by step example to train an MLP neural network model with the simplest output (one FoM) is reported in the jupyter notebook: lpc_ml_calibration.ipynb.

You can find another example of a dynamic-ouptut (multiple FoMs) MLP neural network in lpc_ml_calibration_multiplefoms.ipynb

The I-V curves can also be predicted by training an MLP model as shown in lpc_ml_calibration_iv.ipynb