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MF-LOGP, a new method for determining a single component octanol–water partition coefficients ($$LogP$$$\mathrm{LogP}$) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make$$LogP$$$\mathrm{LogP}$predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average$$RMSE$$$\mathrm{RMSE}$= 0.77 ± 0.007,$$MAE$$$\mathrm{MAE}$= 0.52 ± 0.003, and$${R}^{2}$$$R^2$= 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ($$RMSE$$$\mathrm{RMSE}$= 0.42–1.54,$$MAE$$$\mathrm{MAE}$= 0.09–1.07, and$${R}^{2}$$$R^2$= 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

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Less than 5% of polystyrene is recycled, motivating a search for energy efficient and economical methods for polystyrene recycling that can be deployed at scale. One option is chemical recycling, consisting of thermal depolymerization and purification to produce monomer-grade styrene (>99%) and other co-products. Thermal depolymerization and distillation are readily scalable, well-established technologies; however, to be considered practical, they must be thermodynamically efficient, economically feasible, and environmentally responsible. Accordingly, mass and energy balances of a pyrolysis reactor for thermal depolymerization and two distillation columns to separate styrene from α-methyl styrene, styrene dimer, toluene, and ethyl benzene co-products, were simulated using ASPEN to evaluate thermodynamic and economic feasibility. These simulations indicate that monomer-grade styrene can be recovered with energy inputs <10MJ/kg, comparable to the energy content of pyrolysis co-products. Thermodynamic sensitivity analysis indicates the scope to reduce these values and enhance the robustness of the predictions. A probabilistic economic analysis of multiple scenarios combined with detailed sensitivity analysis indicates that the cost for recycled styrene is approximately twice the historical market value of fossil-derived styrene when styrene costs are fixed at 15% of the total product cost or less than the historical value when feedstock costs are assumed to be zero. A Monte Carlo and Net Present Value-based economic performance analysis indicates that chemical recycling is economically viable for scenarios assuming realistic feedstock costs. Furthermore, the CO2 abatement cost is roughly $1.5 per ton of averted CO2, relative to a pyrolysis process system to produce fuels. As much as 60% of all polystyrene used today could be replaced by chemically recycled styrene, thus quantifying the potential benefits of this readily scalable approach. 

Two-dimensional, 2D, niobium carbide MXene, Nb2CTx, has attracted attention due to its extraordinarily high photothermal conversion efficiency that has applications ranging from medicine, for tumor ablation, to solar energy conversion. Here, we characterize its electronic properties and investigate the ultrafast dynamics of its photoexcitations with a goal of shedding light onto the origins of its unique properties. Through density functional theory, DFT, calculations, we find that Nb2CTx is metallic, with a small but finite DOS at the Fermi level for all experimentally relevant terminations that can be achieved using HF or molten salt etching of the parent MAX phase, including –OH, –O, –F, –Cl, –Br, –I. In agreement with this prediction, THz spectroscopy reveals an intrinsic long-range conductivity of ∼60 Ω−1 cm−1, with significant charge carrier localization and a charge carrier density (∼1020 cm−3) comparable to Mo-based MXenes. Excitation with 800 nm pulses results in a rapid enhancement in photoconductivity, which decays to less than 25% of its peak value within several picoseconds, underlying efficient photothermal conversion. At the same time, a small fraction of photoinjected excess carriers persists for hundreds of picoseconds and can potentially be utilized in photocatalysis or other energy conversion applications. 

Automated decision-making systems are increasingly deployed in domains such as hiring and credit approval where negative outcomes can have substantial ramifications for decision subjects. Thus, recent research has focused on providing explanations that help decision subjects understand the decision system and enable them to take actionable recourse to change their outcome. Popular counterfactual explanation techniques aim to achieve this by describing alterations to an instance that would transform a negative outcome to a positive one. Unfortunately, little user evaluation has been performed to assess which of the many counterfactual approaches best achieve this goal. In this work, we conduct a crowd-sourced between-subjects user study (N = 252) to examine the effects of counterfactual explanation type and presentation on lay decision subjects’ understandings of automated decision systems. We find that the region-based counterfactual type significantly increases objective understanding, subjective understanding, and response confidence as compared to the point-based type. We also find that counterfactual presentation significantly effects response time and moderates the effect of counterfactual type for response confidence, but not understanding. A qualitative analysis reveals how decision subjects interact with different explanation configurations and highlights unmet needs for explanation justification. Our results provide valuable insights and recommendations for the development of counterfactual explanation techniques towards achieving practical actionable recourse and empowering lay users to seek justice and opportunity in automated decision workflows. 

Circular economy-based investments remain modest when compared to sustainability investments. Private investors interested in the circular economy currently have limited choices. To advance the transition to a circular economy, understanding private funding motivation, options, and outcomes are research directions that need to be pursued. Interdisciplinary researchers from environmental sustainability, ecological economics, and finance communities are urged to explore private financing options for the circular economy. 

Human-generated Spatial-Temporal Data (HSTD), represented as trajectory sequences, has undergone a data revolution, thanks to advances in mobile sensing, data mining, and AI. Previous studies have revealed the effectiveness of employing attention mechanisms to analyze massive HSTD. However, traditional attention models face challenges when managing lengthy and noisy trajectories as their computation comes with large memory overheads. Furthermore, attention scores within HSTD trajectories are sparse (i.e., most of the scores are zeros), and clustered with varying lengths (i.e., consecutive tokens clustered with similar scores). To address these challenges, we introduce an innovative strategy named Memory-efficient Trajectory Attention (MeTA). We leverage complicated spatial-temporal features (e.g., traffic speed, proximity to PoIs) and design an innovative feature-based trajectory partition technique to shrink trajectory length. Additionally, we present a learnable dynamic sorting mechanism, with which attention is only computed between sub-trajectories that have prominent correlations. Empirical validations using real-world HSTD demonstrate that our approach not only yields competitive results but also significantly lowers memory usage compared with state-of-the-art methods. Our approach presents innovative solutions for memory-efficient trajectory attention, offering valuable insights for handling HSTD efficiently. 

We report on THz emission in single-crystalline SnS2 in response to above bandgap excitation. Symmetry properties of THz generation suggest that its origin is an ultrafast surface shift current, a 2nd order nonlinear effect that can occur as a result of above-gap photoexcitation of a non-centrosymmetric semiconductor. Multilayer SnS2 can exist in several polytypes that differ in the layer stacking. Of those polytypes, 2H and 18R are centrosymmetric while 4H is not. While Raman spectroscopy suggests that the single crystalline SnS2 in our experiments is 2H, its THz emission has symmetry that are fully consistent with the P3m1 phase of 4H polytype. We hypothesize that the stacking disorder, where strain-free stacking faults that interrupt regions of 2H polytype, can break inversion symmetry and result in THz emission. These results lay the foundations for application of SnS2 as an efficient, stable, flexible THz source material, and highlight the use of THz spectroscopy as a sensitive tool for establishing symmetry properties of materials. 

Use of nanomaterials for photocatalysis faces challenges such as complex synthesis, high cost, low scalability, and dependance on UV radiation for initiating the photocatalytic activity. We recently demonstrated scalable, one-pot syntheses of one-dimensional (1D) lepidocrocite-based nanofilaments (NFs), 1DL NFs, that have the potential to overcome some of the challenges. 1DL NFs are exceptionally stable in water, have a large surface to volume ratio, and sub-square-nanometer cross sections. Initial reports show the semiconducting nature of this material, with an indirect band gap energy of 4.0 eV, one of the highest ever reported for a titania material. In this work, we present a study of the electronic and optical properties of these newly discovered 1DL NFs using ultrafast transient optical absorption. We show that despite the large band gap of this material, sub-gap states can be accessed with visible light illumination only, and photoexcited species reveal decay times in the nanosecond scale. Long lived photoexcitations in the visible range, without assistance by UV illumination, pave the way for possible application in photocatalysis. 

Material scientists have made progress in controlling alloy performance through microstructure quantification. However, attempts at numerically modeling microstructures have failed due to the complex nature of the solidification process. In this research, we present the AlloyGAN deep learning model to generate microstructures for castable aluminum alloys. This innovative model demonstrates its capacity to simulate the evolution of aluminum alloy microstructures in response to variations in composition and cooling rates. Specifically, it is successful to simulate various effects on castable aluminum, including: (1) the influence of Si and other elements on microstructures, (2) the relationship between cooling rate and Secondary Dendritic Arm Spacing, and (3) the impact of P/Sr elements on microstructures. Our model delivers results that match the accuracy and robustness of traditional computational materials science methods, yet significantly reduces computation time. 

The global metal market, expected to exceed $18.5 trillion by 2030, faces costly inefficiencies from defects in alloy manufacturing. Although microstructure analysis has improved alloy performance, current numerical models struggle to accurately simulate solidification. In this research, we thus introduce AlloyGAN - the first domain-driven Conditional Generative Adversarial Network (cGAN) involving domain prior for generating alloy microstructures of previously not considered chemical and manufactural compositions. AlloyGAN improves cGAN process by involving prior factors from solidification reaction to generate scientifically valid images of alloy microstructure given basic alloy manufacturing compositions. It achieves a faster and equally accurate alternative to traditional material science methods for assessing alloy microstructures. We contribute (1) a novel Alloy-GAN design for rapid alloy optimization; (2) unique methods that inject prior knowledge of the chemical reaction into cGAN-based models; and (3) metrics from machine learning and chemistry for generation evaluation. Our approach highlights the promise of GAN-based models in the scientific discovery of materials. AlloyGAN has successfully transitioned into an AIGC startup with a core focus on model-generated metallography. We open its interactive demo at: https://deepalloy.com/