Subsequently, the final layer with fixed weights and zero bias produces Free Energy. We test our model for the metal oxide PhononDB dataset which has just 873 entries. We use 4-fold cross-validation to compute the performance of the model on the full dataset. The penultimate and final layer outputs vs their respective ground truth values are shown in Figure . This shows the efficacy of Ther-moLearn across three distinct outputs. Notably, an exceptional coefficient of determination (R 2 ) of 0.99 has been attained for all three outputs. This achievement highlights how well the model can accurately capture complex patterns within the dataset. However, it should be noted that since the target quantity for our study is Gibbs free energy, we use higher penalties on free energy deviations during training. Conversely, should the target variables be energy or entropy, a similar strategy can be applied for optimal results. Moreover for the JANAF dataset, the ThermoLearn does equally well for all three thermodynamic quantities. This has been showcased in the Figure . Till now we have benchmarked the performance of ThermoLearn across various materials over a fixed temperature. To further evaluate the efficacy of ThermoLearn, we conducted a comprehensive analysis across a spectrum of temperatures using the PhononDB dataset. Multiple instances of ThermoLearn are trained on temperatures ranging from 50K to 1500K in increments of 50K. Subsequently, the performance of these instances is assessed through rigorous testing on unseen materials. Figure illustrates two such test cases, showcasing the performance of ThermoLearn for all three thermodynamic quantities across various temperatures. This plot verifies the robustness of the model across various temperatures. In the featurization section, it is noted that either crystal composition features or CGCNN features may be employed to construct the ML model utilizing the phononDb dataset. As depicted in SI (Table in SI), ThermoLearn demonstrates superior performance compared to the conventional neural network, regardless of the chosen featurization scheme. This indicates that advanced featurization techniques, as utilized by algorithms such as CGCNN and M3GNET, can be adopted. By fine-tuning the final layers of the network to incorporate physical insights derived from thermodynamic equations, the prediction of free energy is enhanced. This adaptability of ThermoLearn opens up potential applications to vastly different systems, such as proteins and biomolecules.