Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can eectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Combined constraints on modified Chaplygin gas model from cosmological observed data: Markov Chain Monte Carlo approach

We use the Markov Chain Monte Carlo method to investigate a global constraints on the modified Chaplygin gas (MCG) model as the unification of dark matter and dark energy from the latest observational data: the Union2 dataset of type supernovae Ia (SNIa), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. In a flat universe, the constraint results for MCG model are, $\Omega_{b}h^{2}=0.02263^{+0.00184}_{-0.00162}$ ($1\sigma$) $^{+0.00213}_{-0.00195}$ $(2\sigma)$, $B_{s}=0.7788^{+0.0736}_{-0.0723}$ ($1\sigma$) $^{+0.0918}_{-0.0904}$ $(2\sigma)$, $\alpha=0.1079^{+0.3397}_{-0.2539}$ ($1\sigma$) $^{+0.4678}_{-0.2911}$ $(2\sigma)$, $B=0.00189^{+0.00583}_{-0.00756}$ ($1\sigma$) $^{+0.00660}_{-0.00915}$ $(2\sigma)$, and $H_{0}=70.711^{+4.188}_{-3.142}$ ($1\sigma$) $^{+5.281}_{-4.149}$ $(2\sigma)$.Comment: 12 pages, 1figur

Experimental Study of Physical Models for Sinkhole Collapses in Wuhan, China

Many sinkholes collapsed since 1998 in Wu Han, China. Sinkhole collapses have put local people\u27s properties and safety in great danger. This paper introduces a large-scale experimental study to implement physical models of sinkhole collapse in this area. Two conceptual models of sinkhole collapsing have been established based on the processes of sinkhole formation, basic geologic conditions, hydrodynamic features, and human activities in this area. Rock, soil, and water samples from sinkhole collapsing areas have been used for a large-scale experiment to verify the conceptual models. High resolution fluid pressure transducers and soil pressure transducers were used in the experimental study to monitor pressure changes in fluids and soil. Measurements of pressure changes were automatically collected and inspected during the experiment. Quantitative studies of Quaternary groundwater use, karst water use, Quaternary soil texture and structure, and the size of the pipelines of karst openings have been conducted to investigate their relationships with the sinkhole collapsing processes. The threshold values of dominant physical properties to trigger a sinkhole collapse have also been studied in this area. Results from this research project provided important information and guidelines on how to prevent future sinkhole collapse in this area