Discovering Melting Temperature Prediction Models of Inorganic Solids by Combining Supervised and Unsupervised Learning
arxiv(2024)
摘要
The melting temperature is important for materials design because of its
relationship with thermal stability, synthesis, and processing conditions.
Current empirical and computational melting point estimation techniques are
limited in scope, computational feasibility, or interpretability. We report the
development of a machine learning methodology for predicting melting
temperatures of binary ionic solid materials. We evaluated different
machine-learning models trained on a data set of the melting points of 476
non-metallic crystalline binary compounds, using materials embeddings
constructed from elemental properties and density-functional theory
calculations as model inputs. A direct supervised-learning approach yields a
mean absolute error of around 180 K but suffers from low interpretability. We
find that the fidelity of predictions can further be improved by introducing an
additional unsupervised-learning step that first classifies the materials
before the melting-point regression. Not only does this two-step model exhibit
improved accuracy, but the approach also provides a level of interpretability
with insights into feature importance and different types of melting that
depend on the specific atomic bonding inside a material. Motivated by this
finding, we used a symbolic learning approach to find interpretable physical
models for the melting temperature, which recovered the best-performing
features from both prior models and provided additional interpretability.
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