Machine Learning for Improved Density Functional Theory Thermodynamics
This work addresses accuracy issues in DFT thermodynamics for alloy design, particularly in aerospace and protective coatings, but is incremental as it applies an existing ML method to a specific domain problem.
The researchers tackled the limited predictive accuracy of density functional theory (DFT) for alloy formation enthalpies by developing a machine learning approach to correct errors, resulting in improved reliability for first-principles predictions in systems like Al-Ni-Pd and Al-Ni-Ti.
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine learning (ML) approach to systematically correct these errors, improving the reliability of first-principles predictions. A neural network model has been trained to predict the discrepancy between DFT-calculated and experimentally measured enthalpies for binary and ternary alloys and compounds. The model utilizes a structured feature set comprising elemental concentrations, atomic numbers, and interaction terms to capture key chemical and structural effects. By applying supervised learning and rigorous data curation we ensure a robust and physically meaningful correction. The model is implemented as a multi-layer perceptron (MLP) regressor with three hidden layers, optimized through leave-one-out cross-validation (LOOCV) and k-fold cross-validation to prevent overfitting. We illustrate the effectiveness of this method by applying it to the Al-Ni-Pd and Al-Ni-Ti systems, which are of interest for high-temperature applications in aerospace and protective coatings.