Design high-entropy carbide ceramics from machine learning

High-entropy ceramics (HECs) have shown great application potential under demanding conditions, such as high stresses and temperatures. However, the immense phase space poses great challenges for the rational design of new high-performance HECs. In this work, we develop machine-learning (ML) models to discover high-entropy ceramic carbides (HECCs). Built upon attributes of HECCs and their constituent precursors, our ML models demonstrate a high prediction accuracy (0.982). Using the well-trained ML models, we evaluate the single-phase probability of 90 HECCs that are not experimentally reported so far. Several of these predictions are validated by our experiments. We further establish the phase diagrams for non-equiatomic HECCs spanning the whole composition space by which the single-phase regime can be easily identified. Our ML models can predict both equiatomic and non-equiatomic HECs based solely on the chemical descriptors of constituent transition-metal-carbide precursors, which paves the way for the high-throughput design of HECCs with superior properties.

First-principles search of hot superconductivity in La-X-H ternary hydrides

Motivated by the recent claim of hot superconductivity with critical temperatures up to 550 K in La + x hydrides, we investigate the high-pressure phase diagram of compounds that may have formed in the experiment, using first-principles calculations for evolutionary crystal structure prediction and superconductivity. Starting from the hypothesis that the observed Tc may be realized by successive heating upon a pre-formed LaH10 phase, we examine plausible ternaries of lanthanum, hydrogen and other elements present in the diamond anvil cell: boron, nitrogen, carbon, platinum, gallium, gold. We find that only boron and, to a lesser extent, gallium form metastable superhydride-like structures that can host high-Tc superconductivity, but the predicted Tc’s are incompatible with the experimental reports. Our results indicate that, while the claims of hot superconductivity should be reconsidered, it is very likely that unknown H-rich ternary or multinary phases containing lanthanum, hydrogen, and possibly boron or gallium may have formed under the reported experimental conditions, and that these may exhibit superconducting properties comparable, or even superior, to those of currently known hydrides.

Two-dimensional Stiefel-Whitney insulators in liganded Xenes

Two-dimensional (2D) Stiefel-Whitney insulator (SWI), which is characterized by the second Stiefel-Whitney class, is a class of topological phases with zero Berry curvature. As an intriguing topological state, it has been well studied in theory but seldom realized in realistic materials. Here we propose that a large class of liganded Xenes, i.e., hydrogenated and halogenated 2D group-IV honeycomb lattices, are 2D SWIs. The nontrivial topology of liganded Xenes is identified by the bulk topological invariant and the existence of protected corner states. Moreover, the large and tunable bandgap (up to 3.5 eV) of liganded Xenes will facilitate the experimental characterization of the 2D SWI phase. Our findings not only provide abundant realistic material candidates that are experimentally feasible but also draw more fundamental research interest towards the topological physics associated with Stiefel-Whitney class in the absence of Berry curvature.

Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

COPEX: co-evolutionary crystal structure prediction algorithm for complex systems

Crystal structure prediction has been widely used to accelerate the discovery of new materials in recent years. Up to this day, it remains a challenge to predict the stable stoichiometries and structures of ternary or more complex systems due to the explosive increase of the size of the chemical and configurational space. Numerous novel materials with a series of unique characteristics are expected to be found in this virgin territory while new algorithms to predict crystal structures in complex systems are urgently called for. Inspired by co-evolution in biology, here we propose a co-evolutionary algorithm, which we name COPEX, and which is based on the well-known evolutionary algorithm USPEX. Within this proposed algorithm, a few USPEX calculations for ternary systems and multiple for energetically-favored pseudobinary or fixed-composition systems are carried out in parallel, and co-evolution is achieved by sharing structural information on the fittest individuals among different USPEX sub-processes during the joint evolution. We have applied the algorithm to W–Cr–B, Mg–Si–O, and Hf–Ta–C, three very different systems, and many ternary compounds have been identified. Our results clearly demonstrate that the COPEX algorithm combines efficiency and reliability even for complex systems.

Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning

The optimal design of shape memory alloys (SMAs) with specific properties is crucial for the innovative application in advanced technologies. Herein, inspired by the recently proposed design concept of concentration modulation, we explore martensitic transformation (MT) in and design the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Systematic phase-field simulations generate data of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom. An ML-assisted strategy is adopted to perform multiobjective optimization of the mechanical properties, through which promising nanocomposite configurations are prescreened for the next set of phase-field simulations. The ML-guided simulations discover an optimized nanocomposite, composed of Nb-rich matrix and Nb-lean nanofillers, that exhibits a combination of mechanical properties, including ultralow modulus, linear super-elasticity, and near-hysteresis-free in a loading-unloading cycle. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work demonstrates the great potential of ML-guided phase-field simulations in the design of advanced materials with extraordinary properties.

Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.