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Register a new userPredicting novel superconducting hydrides using machine learning approaches
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Searching for superconducting hydrides has so far largely focused on finding materials exhibiting the highest possible critical temperatures ($T_c$). This has led to a bias towards materials stabilised at very high pressures, which introduces a number of technical difficulties in experiment. Here we apply machine learning methods in an effort to identify superconducting hydrides which can operate closer to ambient conditions. The output of these models informs structure searches, from which we identify and screen stable candidates before performing electron-phonon calculations to obtain $T_c$. Hydrides of alkali and alkaline earth metals are identified as particularly promising; a $T_c$ of up to 115 K is calculated for RbH$_{12}$ at 50 GPa and a $T_c$ of up to 90 K is calculated for CsH$_7$ at 100 GPa.
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The search for hydride compounds that exhibit high $T_c$ superconductivity has been extensively studied. Within the range of binary hydride compounds, the studies have been developed well including data-driven searches as a topic of interest. Toward the search for the ternary systems, the number of possible combinations grows rapidly, and hence the power of data-driven search gets more prominent. In this study, we constructed various regression models to predict $T_c$ for ternary hydride compounds and found the extreme gradient boosting (XGBoost) regression giving the best performance. The best performed regression predicts new promising candidates realizing higher $T_c$, for which we further identified their possible crystal structures. Confirming their lattice and thermodynamical stabilities, we finally predicted new ternary hydride superconductors, YKH$_{12}$ [$C2/m$ (No.12), $T_c$=143.2 K at 240 GPa] and LaKH$_{12}$ [$Rbar{3}m$ (No.166), $T_c$=99.2 K at 140 GPa] from first principles.
The Anderson Impurity Model (AIM) is a canonical model of quantum many-body physics. Here we investigate whether machine learning models, both neural networks (NN) and kernel ridge regression (KRR), can accurately predict the AIM spectral function in all of its regimes, from empty orbital, to mixed valence, to Kondo. To tackle this question, we construct two large spectral databases containing approximately 410k and 600k spectral functions of the single-channel impurity problem. We show that the NN models can accurately predict the AIM spectral function in all of its regimes, with point-wise mean absolute errors down to 0.003 in normalized units. We find that the trained NN models outperform models based on KRR and enjoy a speedup on the order of $10^5$ over traditional AIM solvers. The required size of the training set of our model can be significantly reduced using furthest point sampling in the AIM parameter space, which is important for generalizing our method to more complicated multi-channel impurity problems of relevance to predicting the properties of real materials.
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ($T_{mathrm{c}}$) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their $T_{mathrm{c}}$ values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of $T_{mathrm{c}}$ for cuprate, iron-based, and low-$T_{mathrm{c}}$ compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.
The discovery of superconductivity at 203K in SH$_3$ is an important step toward higher values of $T_c$. Predictions based on state-of-the-art DFT for the electronic structure, including one preceding experimental confirmation, showed the mechanism to be the electron-phonon interaction. This was confirmed in optical spectroscopy measurements. For photon energies between $sim 450$ and 600 meV in SH$_3$, the reflectance in the superconducting state is below that in its normal state. This difference decreases as $T$ approaches $T_c$. Decreasing absorption with increasing $T$ is opposite to what is expected in ordinary metals. Such an anomalous behavior can be traced back to the energy dependence of the superconducting density of states which is highly peaked at the energy gap value $Delta$ but decays back to the constant normal state value as energy is increased, on a scale of a few $Delta$, or by increasing $T$ towards $T=T_c$. The process of phonon-assisted optical absorption is encoded with a knowledge of the $T$-dependence of $Delta$, the order parameter of the superconducting state. Should the energy of the phonon involved be very large, of order 200 meV or more, this process offers the possibility of observing the closing of the superconducting order parameter with $T$ at correspondingly very large energies. The very recent experimental observation of a $T_csimeq 250$ K in LaH$_{10}$ has further heightened interest in the hydrides. We compare the relevant phonon structure seen in optics with related features in the real and imaginary part of the frequency dependent gap, quasiparticle density of states, reflectance, absorption, and optical scattering rate. The phonon structures all carry information on the $T_c$ value and the $T$-dependence of the order parameter, and can be used to confirm that the mechanism involved in superconductivity is the electron-phonon interaction.
With the motivation of discovering high-temperature superconductors, evolutionary algorithm is employed to search for all stable compounds in the Sn-H system. In addition to the traditional SnH$_4$, new hydrides SnH$_8$, SnH$_{12}$ and SnH$_{14}$ are found to be thermodynamically stable at high pressure. Dynamical stability and superconductivity of tin-hydrides are systematically investigated. I$bar{4}$m2-SnH$_8$, C2/m-SnH$_{12}$ and C2/m-SnH$_{14}$ exhibit higher superconducting transition temperatures of 81, 93 and 97 K compared to the traditional compound SnH$_4$ with T$_c$ of 52 K at 200 GPa. An interesting bent H$_3^-$ in I$bar{4}$m2-SnH$_8$ and novel liner H$_4^-$ in C2/m-SnH$_{12}$ are observed. All the new tin-hydrides remain metallic over their predicted range of stability. The intermediate-frequency wagging and bending vibrations have more contribution to electron-phonon coupling parameter than high-frequency stretching vibrations of H$_2$ and H$_3$.
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