Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts.

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.

Changes in arthropod community but not plant quality benefit a specialist herbivore on plants under reduced water availability.

Plants growing under reduced water availability can affect insect herbivores differently, in some instances benefitting them. However, the forces mediating these positive impacts remain mostly unclear. To identify how water availability impacts plant quality and multi-trophic interactions, we conducted manipulative field studies with two populations of the specialist herbivore Pieris rapae, and its host plant, Rorippa indica. We found that P. rapae larvae experienced higher survival on R. indica growing under low water availability compared with plants grown under high water availability. Higher survival of eggs and larvae was related to the reduced abundance of other herbivores and natural enemies. Water availability had differential impacts on other members of the herbivore community by altering plant quality. Low water availability decreased the quality of R. indica to most herbivores, as indicated by reduced abundance in the field and decreased relative growth rate in laboratory feeding assays. In contrast, P. rapae larval performance was not affected by sympatric R. indica grown under different water availability. These results indicate that local P. rapae populations possess physiological adaptations to overcome fluctuations in host quality. Our findings illustrate that reduced water availability is beneficial to a specialist herbivore but detrimental to most other herbivores. Our work highlights the complex effects of the arthropod communities associated with plants in determining the impacts of water availability on insect herbivores.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training.

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

Development of a Skeletal Mechanism of a Four-Component Diesel Surrogate Fuel Using the Decoupling Method.

Alkylcyclohexanes with a long alkyl chain account for more than 30% of diesel fuel but seldom used in the oxidation mechanism of diesel surrogate fuel due to the lack of a reduced skeletal mechanism. Hence, a four-component diesel surrogate fuel was developed with n-butylcyclohexane (NBCH) as the representative of alkylcyclohexanes with a long alkyl chain in real diesel. The surrogate fuel can reproduce the physicochemical characteristics of real diesel, especially the distillation range. The reduced mechanism of NBCH was developed, and the skeletal mechanism of the surrogate fuel was formulated including 80 species and 251 reactions based on the decoupling method. The mechanism was validated under a wide range of conditions with the experimental results of ignition delay time (IDT), laminar flame speed, and species concentrations of both pure components and diesel. The accuracy of the mechanism on the spray and ignition performance was further validated against the experimental data obtained in a constant volume combustion chamber system. The calculated results showed a satisfactory agreement, in which the maximum error of flame lift-off length is 7.82 mm and that of IDTs is 0.16 ms. It was proven that the mechanism is suitable to reproduce the physicochemical properties of diesel and further predict the diesel spray and ignition performance.