Thermal energy storage offers numerous
benefits by reducing
energy
consumption and promoting the use of renewable energy sources. Thermal
energy storage materials have been investigated for many decades with
the aim of improving the overall efficiency of energy systems. However,
finding solid materials that meet the requirement of high heat capacity
has been a grand challenge for material scientists. Herewith, by training
various machine learning models on 3377 high-quality data from full
density functional theory (DFT) calculations, we efficiently search
for potential materials with high heat capacity. We build four traditional
machine learning models and two graph neural network models. Cross-comparison
of the prediction performance and model accuracy was conducted among
different models. The deeperGATGNN model exhibits high prediction
accuracy and is used for predicting the heat capacity of 32,026 structures
screened from the open quantum material database. We gain deep insight
into the correlation between heat capacity and structure descriptors
such as space group, prototype, lattice volume, atomic weight, etc.
Twenty-two structures were predicted to possess high heat capacity,
and the results were further validated with DFT calculations. We also
identified one special structure, namely, MnIn2Se4, with space group no. 227 (Fd3̅m), that exhibits extremely high heat capacity, even higher than that
of the Dulong–Petit limit at room temperature. This study paves
the way for accelerating the discovery of novel thermal energy storage
materials by combining machine learning with minimal DFT inquiry.
Despite the machine learning (ML) methods have been largely used recently, the predicted materials properties usually cannot exceed the range of original training data. We deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory (DFT) in searching extreme material properties. This combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry, but also yields properties beyond original training range. We use Stein novelty to recommend outliers and then verify using DFT. Validated data are then added into the training dataset for next round iteration. We test the loop of training-recommendation-validation in mechanical property space. By screening 85,707 crystal structures, we identify 21 ultrahigh hardness structures and 11 negative Poisson’s ratio structures. The algorithm is very promising for future materials discovery that can push materials properties to the limit with minimal DFT calculations on only ~1% of the structures in the screening pool.
High-throughput screening and material informatics have shown a great power in novel materials discovery including batteries, high entropy alloys, photocatalysts, etc. However, the lattice thermal conductivity (κ) oriented high-throughput screening...
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