Database Of Properties Research Articles

Compositionally restricted atomistic line graph neural network for improved thermoelectric transport property predictions

Graph neural networks (GNNs) have evolved many variants for predicting the properties of crystal materials. While most networks within this family focus on improving model structures, the significance of atomistic features has not received adequate attention. In this study, we constructed an atomistic line GNN model using compositionally restricted atomistic representations which are more elaborate set of descriptors compared to previous GNN models, and employing unit graph representations that account for all symmetries. The developed model, named as CraLiGNN, outperforms previous representative GNN models in predicting the Seebeck coefficient, electrical conductivity, and electronic thermal conductivity that are recorded in a widely used thermoelectric properties database, confirming the importance of atomistic representations. The CraLiGNN model allows optional inclusion of additional features. The supplement of bandgap significantly enhances the model performance, for example, more than 35% reduction of mean absolute error in the case of 600 K and 1019 cm−3 concentration. We applied CraLiGNN to predict the unrecorded thermoelectric transport properties of 14 half-Heusler and 52 perovskite compounds, and compared the results with first-principles calculations, showing that the model has extrapolation ability to identify the thermoelectric potential of materials.

Advancing early detection of biological events by digital holographic microscopy and simulation of microorganisms

There is a global need to advance bio-aerosol sensing for CBRN (Chemical, Biological, Radiological, and Nuclear) applications by compact and cost-effective devices. Employing digital holographic microscopy (DHM) and deep learning, we developed a system called HoloZcan to automate the analysis of airborne microbial pathogens and particles. DHM provides valuable information, but obtaining data from biological specimens for robust investigations is challenging. This paper introduces a custom simulation approach using the open-source software Meep and the finite-difference time-domain (FDTD) method to overcome limitations of existing Mie-based simulators, especially when dealing with complex microbial shapes. The simulation tool enables the modelling of specific microorganisms, offering a safer and more flexible alternative for CBRN research by bypassing ethical and logistical constraints associated with live pathogens. The study details the simulation workflow, built upon the construction of a database of optical properties of biological materials, for realistic simulations of light-microbe interactions. Evaluations on homogeneous and non-homogeneous objects demonstrate the tool’s limited intrinsic errors and superior sensitivity to refractive index changes compared to traditional Mie-based simulations. This work significantly advances our capability to accurately simulate and analyse CBRN-related scenarios, enhancing comprehensive research in bio-aerosol sensing.Graphical abstract

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R–CR–R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

Thermodynamic modeling of liquid binary alloys of the Al–Er system

The paper presents the results of a study of the thermochemical properties of the Al–Er system. The thermodynamic characteristics were evaluated (△fH0298, S0298, (H0298−H00), Cp(T) and Cp(liq)) for the intermetallic compounds Al3Er, Al2Er, AlEr, Al2Er3, AlEr2. The values of△fH0298 calculated based on the semiempirical Miedema model adapted for the group of Al–REM alloys were taken for calculations and amounted to –47.7, –58.4, –63, –55.2, –46.8 kJ/mol∙at, respectively. The mixing characteristics of liquid alloys of this system were evaluated by Terra software package for modeling the equilibrium states of heterogeneous inorganic systems with an extensive database of properties of individual substances. The model of ideal solutions of interaction products was used as a computational model. Modeling of equilibrium composition and properties of melts was carried out in the temperature range of 1900–2100 K, in an argon atmosphere at a total pressure of 0.1 MPa in the system. Comparison of the obtained results with the simulation results in the approximation of an ideal solution, allowed us to determine the excess integral thermodynamic properties of liquid alloys (Gibbs energy, enthalpy, and entropy). It is shown that in the studied temperature range, with an increase of temperature, there is a natural, though not significant, decrease in the values of these parameters by absolute value. It is established that the formation of liquid alloys of the Al–Er system is accompanied by significant heat release: the value of the integral enthalpy of mixing at a temperature T = 2100 K is –58.9 kJ/ mol∙at. When comparing the thermochemical properties of the Al–Er system with the binary systems Al–Y and Al–Sc studied by the same methods, it is shown that all energy curves pass through the extremum at XSc,Y,Er ≈ 0.5. The strongest interaction of the components is observed in the Al–Y system, (ΔHmix = –58.9 kJ/mol∙at), which is close enough to the maximum modulo value of the enthalpy of mixing in the Al–Er system. The weakest interaction is observed in the Al–Sc system (ΔHmix = –44.8 kJ/mol·at). The results obtained in this work provide a theoretical basis for further experimental study of erbium–containing aluminum alloys.

HyStor: An experimental database of hydrogen storage properties for various metal alloy classes

In this work, we introduce the HyStor database, consisting of 1282 metal alloys along with their maximum hydrogen storage capacity (H2wt%) at a given absorption temperature. The curated HydPark database consist of 831 entries. We sourced compositions from research articles and various patent documents, resulting in addition of 451 compositions to the HydPark database. The addition is reflected in the data across all existing classes of alloys. Further, low entropy alloys (LEA), medium entropy alloys (MEA) and high entropy alloys (HEA) have been newly included classes. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains representation of 54 elements, with a temperature range of 200–800 K, and H2wt% ranging from 0.1 to 7.19. We conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database to ensure data quality. Furthermore, we conducted multiple tests to identify potential outlier compositions. The data curation and updation reflects into slight improved error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.31 to 0.29 and increasing the R2 score from 0.77 to 0.79.

Modeling viscosity of commercial glass melts: Adam–Gibbs equation and Gaussian process regression

ABSTRACT A statistically designed property database of six commercial glass families is leveraged to develop models relating viscosity to temperature and composition. Specialized models for each glass family were designed based on the Adam–Gibbs equation. We show that the whole range of viscosity data of one glass family from 100 to 1012 Pa s can be accurately modeled using one composition-dependent and two composition-independent parameters when glass transition temperature and viscosity at that temperature are independently determined. Generalization to a broader composition space using the same approach leads to a loss of accuracy compared to specialized models. However, this can be mostly negated when Gaussian process regression is used to estimate the compositional dependence of configuration entropy and fragility exponent parameters of the Adam–Gibbs equation.

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks.

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

The reverse-DADI method: Computation of frequency-dependent atomic polarizabilities for carbon and hydrogen atoms in hydrocarbon structures

A specific method, combining some ingredients of the well-known DDA and PDI approaches, has been developed in our group since many years to calculate the absorption cross-sections of carbonaceous nanoparticles based on their atomistic details. This method, here named the Dynamic Atomic Dipole Interaction (DADI) model, requires the knowledge of the position and frequency-dependent polarizability of each atom constituting the nanoparticles. While the atomic positions can be quite easily obtained, for example as the results of molecular dynamics simulations, obtaining the frequency-dependent atomic polarizabilities is a trickier task. Here, a fitting procedure, named the reverse-DADI method, has been applied to calculate the frequency-dependent atomic polarizability values for carbon and hydrogen atoms involved in aromatic cycles or in aliphatic chains, on the basis of frequency-dependent molecular polarizabilities of various PAH and alkane molecules, calculated with the TD-DFT theory, in the UV–Visible range. Then, using these frequency-dependent atomic polarizabilities as input parameters in the DADI model has been shown to lead to an accurate representation of the absorption cross-sections of various PAH and alkane molecules with respect to the corresponding values obtained at the TD-DFT level, with however the great advantage of a much shorter time of calculations. Furthermore, these results are indications of a good transferability of the frequency-dependent atomic polarizability values obtained here to any C or H atom of any PAH or alkane molecule. This opens the way for building large databases of optical properties for carbonaceous species of atmospheric or astrophysical interests.

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610–0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520–0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119–0.148, MAE-LOOTRAIN = 0.132–0.177, predictive R2 or Q2F1 = 0.538–0.567, Q2F2 = 0.500–0.565, MAETEST = 0.122–0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (−ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.

Unified, Integral Approach to Modeling and Design of High-Pressure Pipelines: Assessment of Various Flow and Temperature Models for Supercritical Fluids

Abstract A unified one-dimensional, steady-state flow and heat transfer model is presented for the pipeline transport of fluids at high pressures, including the supercritical conditions. The model includes a generalized temperature equation, presented here for the first time, and accounts for all of the important effects, including the property variation, viscous dissipation, Joule-Thomson (J-T) cooling, and heat exchange with the surrounding. With appropriate approximations, this model can yield all isothermal and non-isothermal pipe flow solutions reported thus far. A generalized multi-zone integral method is developed which solves the two resulting algebraic equations for pressure and temperature in conjunction with a property database, such as the National Institute of Standard and Technology (NIST) Reference Fluid Thermodynamic and Transport Properties (REFPROP). With appropriately selected number and size of the zones and using property values at the mean temperature and pressure within each zone, this integral method can accurately predict the complex effects of the governing parameters, such as the pipe diameter and length, inlet and exit pressures, mass flow rate, J-T cooling, and inlet and surrounding temperatures. Its accuracy for small-to-large diameter pipes has been ascertained by a comparison with the numerical solutions of the differential form of governing equations that requires a large number of small grids along the pipe and the values of mean properties within each grid. Indeed, this integral model can be used for the pipeline transport at both subcritical and supercritical pressures as long as the fluid does not encounter its anomalous states and the phase-change.

Investigating the evolution of road surface descriptors according to observation angles using a database of the reflection properties of urban materials

For road lighting design, road surface descriptors like the degree of lightness Q0 and specular factor S1 are derived from reflection properties (or Bidirectional Reflectance Distribution Function (BRDF)) of road surfaces. However, these BRDF are based on outdated road surface characteristics and are also partial (they are called r-tables) because only the motorist’s 1° observation angle is considered. Thus, the need to have full BRDF measurements of current road surfaces available. This paper proposes to use a recently released open source database of measured whole BRDF by means of our in-lab gonioreflectometer to investigate the performance of Q0 and S1 according to observation angles. We first compare descriptors obtained from measured and interpolated r-tables and find a good accuracy, especially for Q0. Then we propose a model for these descriptors function of the observation angle, and find quite an accurate fitting. Finally, we suggest a methodology for the classification of road surfaces which equally considers Q0, S1 and the observation angles, and propose a tentative classification of road surfaces based, not in terms of their materials, but rather in terms of their usage-driven performance.

Herbal Technological Prospects of Morus nigra L.: A Systematic Patent Analysis Review.

Morus nigra L. is a plant with significant potential for drug development due to the presence of numerous bioactive compounds in its various parts. This article aims to compile the technological perspectives of Morus nigra L. towards drug development and therapeutic indications based on registered patents in databases. The study analyzed patents published within the last five years, focusing on products derived from different parts of the Morus nigra L. plant. Patent databases such as the European Patent Office (EPO), the United States Patent and Trademark Office (USPTO), the World Intellectual Property Organization (WIPO), and the National Institute of Industrial Property Databases (INPI) were examined. A total of 45 patents were categorized by country of origin, type of applicant, extraction method, and therapeutic indications. China had the highest number of patent filings (43.48%), and private companies were the primary technology patent holders (38.64%). Noteworthy extraction methods included ultrasound-assisted extraction, decoction, infusion, and maceration. The most utilized plant parts were leaves (44.44%), followed by fruits (35.56%), root bark (15.56%), and stems (4.44%). The main therapeutic indications identified were the treatment of hyperglycemia and dyslipidemia (43.33%), along with digestive problems, cosmetics, nutrition, and cleaning applications. The study of patents covers discoveries and advancements often absent in scientific articles, making a review focused on this advanced information crucial for expanding existing scientific knowledge. Even if some therapies have been explored previously, patents can reveal innovative approaches and fresh perspectives that contribute to sustained scientific progress.

Research on high-entropy spinel microwave absorption materials: Exploration of machine learning and experimental integration

With the advancement of electronic communication technology, the intensity of electromagnetic radiation is increasing, and traditional wave-absorbing materials no longer meet the demands in various current environments. Spinel ferrite is one of the earliest materials used for absorbing electromagnetic waves, and the introduction of "high entropy" and controlling the degree of entropy disorder can achieve performance regulation. Nonetheless, the lengthy testing period and high costs associated with trial and error have posed challenges, limiting the development of high entropy microwave absorbing materials. This study utilized machine learning techniques to construct a high-entropy spinel microwave absorption property database. The design of CoxNi0.4-xCu0.2Zn0.2Mn0.2Fe2O4 (X = 0, 0.1, 0.2, 0.3, 0.4) was informed by the SHAP plots of the GBR model. Machine learning demonstrates that factors such as cobalt and nickel content, particle size, and others significantly influence microwave absorption performance. The experimental result manifests it and Co content regulate the microwave absorbing performance through adjusting the defect density and particle size. The CoxNi0.4-xCu0.2Zn0.2Mn0.2Fe2O4 with optimized Co content achieved a reflection loss (RL) of −45.32 dB, and an effective absorption bandwidth (EAB) of 6.48 GHz. This approach significantly reduced the research period, presented a novel research methodology for other scholars, and expedited the research progress in the field of microwave absorbing materials.

Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl2-KCl-MgCl2 Melt.

The local structure and thermophysical properties of SrCl2-KCl-MgCl2 melt were revealed by deep potential molecular dynamicsdriven by machine learning to facilitate the development of molten salt electrolytic Mg-Sr alloys. The short- and intermediate-range order of the SrCl2-KCl-MgCl2 melts was explored through radial distribution functions and structure factors, respectively, and their component and temperature dependence were discussed comprehensively. In the MgCl2-rich system, the intermediate-range order is more pronounced, and its evolution with temperature exhibits a non-Debye-Waller behavior. Mg-Cl is dominated by 4,5 coordination and Sr-Cl by 6,7 coordination, and their coordination geometries exhibit distorted octahedra and distorted pentagonal bipyramids, respectively. A database of thermophysical properties of SrCl2-KCl-MgCl2 melts, including density, self-diffusion coefficient, viscosity, and ionic conductivity, was thus developed, covering the temperature range from 873 to 1173 K.

Thermophysical properties of UO2-ZrO2 melt measured by aerodynamic levitation

This study addresses the measurement of thermophysical properties in molten core materials (corium), which is crucial for modeling severe accidents in light water reactors. Due to the measurement challenges at temperatures above 3000 K, there is a significant lack of experimental data for corium. Focusing on a UO2-ZrO2 melt (atomic ratio U/Zr=0.456), density and viscosity measurements were conducted using aerodynamic levitation, a non-contact method chosen to avoid interactions between the samples and container walls. Samples were prepared using the pressing sintering method and levitated by argon gas above a conical converging-diverging nozzle. Density was measured using the cooling traces method under the axisymmetric ellipsoid hypothesis, while viscosity was measured by inducing damped oscillations which occurred when the levitated droplet was forced to oscillate around its resonant frequency through acoustic excitation, followed by the cessation of the acoustic excitation. The provided data and derived equations for the density and viscosity of the UO2-ZrO2 melt contribute to enriching the thermophysical property database for materials in nuclear reactors.

A synthesized field survey database of vegetation and active-layer properties for the Alaskan tundra (1972–2020)

Abstract. Studies in recent decades have shown strong evidence of physical and biological changes in the Arctic tundra, largely in response to rapid rates of warming. Given the important implications of these changes for ecosystem services, hydrology, surface energy balance, carbon budgets, and climate feedbacks, research on the trends and patterns of these changes is becoming increasingly important and can help better constrain estimates of local, regional, and global impacts as well as inform mitigation and adaptation strategies. Despite this great need, scientific understanding of tundra ecology and change remains limited, largely due to the inaccessibility of this region and less intensive studies compared to other terrestrial biomes. A synthesis of existing datasets from past field studies can make field data more accessible and open up possibilities for collaborative research as well as for investigating and informing future studies. Here, we synthesize field datasets of vegetation and active-layer properties from the Alaskan tundra, one of the most well-studied tundra regions. Given the potentially increasing intensive fire regimes in the tundra, fire history and severity attributes have been added to data points where available. The resulting database is a resource that future investigators can employ to analyze spatial and temporal patterns in soil, vegetation, and fire disturbance-related environmental variables across the Alaskan tundra. This database, titled the Synthesized Alaskan Tundra Field Database (SATFiD), can be accessed at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics (Chen et al., 2023: https://doi.org/10.3334/ORNLDAAC/2177).

Current limitations and future research needs for predicting soil precompression stress: A synthesis of available data

Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions.

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

Aiming toward a sustainable energy era, the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied. One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying. However, the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space, providing an opportunity for machine learning (ML) approaches to help accelerate the discovery of new multicomponent alloy materials. A conventional prerequisite for ML approaches is a large database of accurate material properties, which may require exhaustive computational and/or experimental resources. This study demonstrates that the screening of solid-solution alloys (up to hexanary systems) can be performed using a small database to minimize (and optimize) the number of high-level computational calculations. Specifically, we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method (α-method). Furthermore, we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.

Models for predicting corrosion inhibition efficiency of common drugs on steel surfaces: A rationalized comparison among methodologies

Several anticorrosive treatments have been proposed over time to create protective layers to hinder the corrosion phenomenon. In recent years, organic molecules from plant extracts and expired drugs have been tested due to their potential corrosion inhibition properties. However, direct corrosion inhibition efficiency (IE%) evaluation requires costly reactants and a specific experimental setup. Quantitative-structure activity relationship (QSAR) proposes modeling IE% in terms of variables measured in previous experiments or determined by theoretical approaches. Computed descriptors, such as ionization energy (I), electronic affinity (A), or global hardness, were added to a database of physicochemical properties. This work compares several methodologies to obtain precise yet portable mathematical models for predicting corrosion inhibition efficiency. As an original approach from this research group, nonlinear autoregressive moving average with exogenous inputs (NARMAX), using forward regression with orthogonal least squares (FROLS), models were implemented as a robust method to get nonlinear portable models and to determine the most important variables impacting IE%. Contrastingly, ordinary least squares (OLS) methodology was employed with the novelty of applying power series expansions from the promoted FROLS variables for linear and polynomial regression with only one independent variable, which resulted in clearer graph visualization of trends and the ease of proposing thumb rules based on raw information. Finally, IBM Watson was also compared as a robust yet non-portable and highly parametrized alternative to conventional mathematical approaches, based on extra trees regressor (ETR). The models were compared using mean absolute percentage error (MAPE), mean-squared error (MSE), and root-mean-squared error (RMSE). Overall, models with fewer variables and up to second-order terms show improved performance. The main tendencies of IE%, drawn by inferences for 630 substances by second-order NARX, are analyzed. Also, the determinant role of the highest occupied molecular orbital energy was reported. Experimentalists can take advantage of a “cost-free” general approach that can obtain estimations for IE% values with errors of about 6 %, in particular the second-order NARX model.

An outcome-driven threshold for pulse pressure amplification

Pulse pressure amplification (PPA) is the brachial-to-aortic pulse pressure ratio and decreases with age and cardiovascular risk factors. This individual-participant meta-analysis of population studies aimed to define an outcome-driven threshold for PPA. Incidence rates and standardized multivariable-adjusted hazard ratios (HRs) of cardiovascular and coronary endpoints associated with PPA, as assessed by the SphygmoCor software, were evaluated in the International Database of Central Arterial Properties for Risk Stratification (n = 5608). Model refinement was assessed by the integrated discrimination (IDI) and net reclassification (NRI) improvement. Age ranged from 30 to 96 years (median 53.6). Over 4.1 years (median), 255 and 109 participants experienced a cardiovascular or coronary endpoint. In a randomly defined discovery subset of 3945 individuals, the rounded risk-carrying PPA thresholds converged at 1.3. The HRs for cardiovascular and coronary endpoints contrasting PPA < 1.3 vs ≥1.3 were 1.54 (95% confidence interval [CI]: 1.00–2.36) and 2.45 (CI: 1.20–5.01), respectively. Models were well calibrated, findings were replicated in the remaining 1663 individuals analyzed as test dataset, and NRI was significant for both endpoints. The HRs associating cardiovascular and coronary endpoints per PPA threshold in individuals <60 vs ≥60 years were 3.86 vs 1.19 and 6.21 vs 1.77, respectively. The proportion of high-risk women (PPA < 1.3) was higher at younger age (<60 vs ≥60 years: 67.7% vs 61.5%; P < 0.001). In conclusion, over and beyond common risk factors, a brachial-to-central PP ratio of <1.3 is a forerunner of cardiovascular coronary complications and is an underestimated risk factor in women aged 30–60 years. Our study supports pulse wave analysis for risk stratification.