Searching Extreme Mechanical Properties Using Active Machine Learning and Density Functional Theory

Materials with extreme mechanical properties leads to future technological advancements. However, discovery of these materials is non-trivial. The use of machine learning (ML) techniques and density functional theory (DFT) calculation for structure properties prediction has helped to the discovery of novel materials over the past decade. ML techniques are highly efficient, but less accurate and density functional theory (DFT) calculation is highly accurate, but less efficient. We proposed a technique to combine ML methods and DFT calculations in discovering new materials with desired properties. This combination improves the search for materials because it combines the efficiency of ML and the accuracy of DFT. Here, we train a ML algorithm, the algorithm is used to make prediction. We use stein novelty (SN) score which recommends potential candidates from the ML prediction. We then verify the potential candidates using DFT calculation. In our demonstration, we use three property space for our search: Bulk Modulus vs Shear Modulus, Shear Modulus vs Hardness and Pugh’s ratio vs Poisson’s ratio. In exploring our property space, we found 30 crystal structures with high bulk and shear moduli, 21 crystal structures with ultrahigh hardness, and 11 crystal structures with negative Poisson’s ratio from original 85,707 crystal structures taking from material project database. The method deployed herein can be extended to push other material properties to the limit

Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity

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