Please get in touch if you are willing to cooperate on a research paper with some computations on Thero-Calc. You must have a license to use the Thero-Calc software.
My emails:
Thermo-Calc software can be used to predict the thermodynamic properties of molten salts. It is typically composed of ionic compounds, which can be modeled using thermodynamic databases and models available in Thermo-Calc. We can also use Python and other tools as well.
To predict the properties of molten salts using Thermo-Calc, you would first need to define the composition of the salt. This can be done by specifying the elements and their concentrations in the salt. Once the composition is defined, you can use Thermo-Calc to predict a wide range of thermodynamic properties, including but not limited to:
- Enthalpy and entropy
- Heat capacity
- Phase equilibria
- Electrical conductivity
- Viscosity
- Surface tension
To make these predictions, you would need to select a thermodynamic database and model that is appropriate for the system you are studying. Thermo-Calc provides a range of databases and models for ionic compounds, including the CALPHAD (CALculation of PHAse Diagrams) method, which is a widely used approach for predicting the thermodynamic properties of materials.
Thermo-Calc also provides a range of tools and features for analyzing and visualizing the results of your predictions, such as phase diagrams, property graphs, and tables.
It should be noted that accurate predictions of molten salt properties using Thermo-Calc can be challenging, particularly for complex systems. It is important to carefully validate your results and to use appropriate experimental data to refine your models and databases as needed.
Hope it helps. I can work with you. Sending an email as well my email is [email protected] Thx
Credit: mainly AI Tools
Mohammad Imam
Dear Dr. Imam
Thank you for your detailed answer and for your kind email.
I will get in touch with you to discuss the cooperation in this work.
Best regards,
Mostafa
Python program that collects data from a file, cleans the data, predicts the data with an AUC curve, and visualizes it using the `pandas`, `numpy`, `sklearn`, and `matplotlib` libraries:
```python
import pandas as pd
import numpy as np
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
# Collect data from file
data = pd.read_csv('data.csv')
# Clean the data
data = data.dropna() # Remove any rows with missing values
data = data.drop_duplicates() # Remove any duplicate rows
# Split the data into training and testing sets
X = data.drop('target_variable', axis=1)
y = data['target_variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Predict the data and generate an AUC curve
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)
# Visualize the AUC curve
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()
Additional scalable and flexible approach
- Badges
- Science topic