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Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs. 

Ceramics are an important class of materials with widespread applications because of their high thermal, mechanical, and chemical stability. Computational predictions based on first principles methods can be a valuable tool in accelerating materials discovery to develop improved ceramics. It is essential to experimentally confirm the material properties of such predictions. However, materials screening rates are limited by the long processing times and the poor compositional control from volatile element loss in conventional ceramic sintering techniques. To overcome these limitations, we developed an ultrafast high-temperature sintering (UHS) process for the fabrication of ceramic materials by radiative heating under an inert atmosphere. We provide several examples of the UHS process to demonstrate its potential utility and applications, including advancements in solid-state electrolytes, multicomponent structures, and high-throughput materials screening. 

Au is one of the most promising electrocatalysts to convert CO 2 into CO in an aqueous-phase electrochemical reduction. However, ultrasmall Au nanocatalysts (AuNCs, <2 nm) have proven to be favorable for water reduction over CO 2 , although they possess a large surface-to-volume ratio and potentially are ideal for CO 2 reduction. We herein report that ultrasmall AuNCs (1.9 ± 0.3 nm) supported on nitrided carbon are remarkably active and selective for CO 2 reduction. The mass activity for CO of AuNCs reaches 967 A g −1 with a faradaic efficiency for CO of ∼83% at −0.73 V ( vs . reversible hydrogen electrode) that is an order of magnitude more active than the state-of-the-art results. The high activity is endowed by the large surface area per unit weight and the high selectivity of ultrasmall AuNCs for CO 2 reduction originates from the cooperative effect of Au and the nitrided carbon support where the surface N sites act as Lewis bases to increase the surface charge density of AuNCs and enhance the localized concentration of CO 2 nearby catalytically active Au sites. We show that our results can be applied to other pre-synthesized Au catalysts to largely improve their selectivity for CO 2 reduction by 50%. Our method is expected to illustrate a general guideline to effectively lower the cost of Au catalysts per unit weight of the product while maintaining its high selectivity for CO 2 reduction. 

Abstract Fire retardant coatings have been proven effective at reducing the heat release rate (HRR) of structural materials during burning; yet effective methods for increasing the ignition temperature and delay time prior to burning are rarely reported. Herein, a strong, fire‐resistant wood structural material is developed by combining a densification treatment with an anisotropic thermally conductive flame‐retardant coating of hexagonal boron nitride (h‐BN) nanosheets to produce BN‐densified wood. The thermal management properties created by the BN coating provide fast, in‐plane thermal diffusion, slowing the conduction of heat through the densified wood, which improves the material's ignition properties. Compared with densified wood without the BN coating, a 41 °C enhancement in ignition temperature (T_ig), a twofold increase in ignition delay time (t_ig), and a 25% decrease in the maximum HRR of BN‐densified wood can be achieved. As a proof of concept for scalability, the pieces of the BN‐densified wood are fabricated with a length larger than 25 cm, width greater than 15 cm, and thickness more than 7 mm. The improved thermal management, fire resistance, mechanical strength, and scalable production of BN‐densified wood position it as a promising structural material for safe and energy‐efficient buildings.