Potential Model Research Articles

Learner Assessment and Program Evaluation: Supporting Precision Education.

Precision education (PE) systematically leverages data and advanced analytics to inform educational interventions that, in turn, promote meaningful learner outcomes. PE does this by incorporating analytic results back into the education continuum through continuous feedback cycles. These data-informed sequences of planning, learning, assessing, and adjusting foster competence and adaptive expertise. PE cycles occur at individual (micro), program (meso), or system (macro) levels. This article focuses on program- and system-level PE.Data for PE come from a multitude of sources, including learner assessment and program evaluation. The authors describe the link between these data and the vital role evaluation plays in providing evidence of educational effectiveness. By including prior program evaluation research supporting this claim, the authors illustrate the link between training programs and patient outcomes. They also describe existing national reports providing feedback to programs and institutions, as well as 2 emerging, multiorganization program- and system-level PE efforts. The challenges encountered by those implementing PE and the continuing need to advance this work illuminate the necessity for increased cross-disciplinary collaborations and a national cross-organizational data-sharing effort.Finally, the authors propose practical approaches for funding a national initiative in PE as well as potential models for advancing the field of PE. Lessons learned from successes by others illustrate the promise of these recommendations.

Bioremediation potential of the hard clam Mercenaria mercenaria as an intensive shrimp aquaculture pond polyculture condidate

Polyculture practices are important for achieving sustainable aquaculture development. Recently, hard clams polyculture in intensive shrimp ponds has been encouraged because bivalves can consume excess nutrients in aquaculture systems and sequester carbon. To evaluate the bioremediation potential of hard clams polyculture in intensive shrimp ponds, this study built an assessment model based on individual growth models and estimated the potential for nitrogen and phosphorus removal as well as CO2 fixation by hard clams. Firstly, key parameters required for model construction were obtained through field surveys and physiological experiments. Subsequently, an individual growth model for the hard clam Mercenaria mercenaria was developed based on the Dynamic Energy Budget (DEB) theory. Fitting of the growth data indicated that the model accurately replicated the growth patterns of hard clams, with relative root mean square errors of 9.87 % for shell length and 5.02 % for dry tissue weight. Finally, the assessment model for the bioremediation potential of hard clams demonstrated that, over 110 days in the intensive shrimp mariculture pond, the net removal of nitrogen and phosphorus by hard clams were 3.68 kg ha−1 and 0.81 kg ha−1, respectively, and CO2 fixation was 507.00 kg ha−1. These findings suggested that the DEB model is an effective tool for evaluating bivalve ecological remediation potential and can aid in selecting species for sustainable polyculture.

Onset mechanism of an inverted U-shaped solar filament eruption revealed by NVST, SDO, and STEREO-A observations

Context. Solar filaments, also called solar prominences when appearing on the solar limb, consist of dense, cool plasma suspended in the hot and tenuous corona. They are the main potential sources of solar storms. Aims. To understand the onset mechanism of solar filaments, we investigated the eruption process of an inverted U-shaped solar filament and two precursory jet-like activities. Methods. Using observations from the New Vacuum Solar Telescope (NVST), the Solar Dynamics Observatory (SDO), and the Solar Terrestrial Relations Observatory-Ahead (STEREO-A), we investigated the event from two distinct observational perspectives: on the solar disk using NVST and SDO, and on the solar limb using STEREO-A. We employed both a nonlinear force-free field model and a potential field model to reconstruct the coronal magnetic field with the aim to understand its magnetic properties. Results. Two precursor jet-like activities were observed before the eruption, displaying an untwisted rotation. The second activity released an estimated twist of over two turns. During these two jet-like activities, Y-shaped brightenings, which are newly emerging magnetic flux accompanied by magnetic cancellation, and the formation of newly moving fibrils were identified. When these observational features are combined, it can be inferred that these two precursor jet-like activities released the magnetic field that constrained the filament and were caused by newly emerging magnetic flux. Before the filament eruption, it was observed that some moving flows had been ejected from the site as the onset of two jet-like activities, indicating the same physical process as in the two jet-like activities. Extrapolations revealed that the filament was below the height of the decay index of 1.0 and had a strong magnetic field (540 Gauss) and a high twist number (2.4 turns) before the eruption. An apparent rotational motion was observed during the filament eruption. Conclusions. We deduce that the solar filament, exhibiting an inverted U-shape, is a significantly twisted flux rope. The eruption of the filament was initiated by the release of constraining magnetic fields through continuous magnetic reconnection. This reconnection process was caused by the emergence of new magnetic flux.

Exploring social networks through stochastic multilayer graph modeling

Several graph models are available today to model online social networks. These graph models are used to analyze the structural properties of the online social network, such as detecting communities, finding the influential spreader and predicting the behavior of the network. However, these models are based on deterministic single-layer graphs that may not be appropriate when online users use multiple social networks at the same time and social networks provide specific services. Moreover, because of the unknown and dynamic nature related to the behaviors and activities of online users, as well as structural and behavioral parameters, which may vary over time, stochastic multi-layer models could be applied to better capture and represent this phenomenon, as well as the dynamic nature of social networks. For example, in recommender systems, users' interests are unknown parameters and vary over time. Therefore, stochastic multilayer graph modeling can be used to develop recommender systems by considering different layers for different types of interests or preferences. In this paper, we propose a stochastic multilayer graph in which the edges are associated with random variables as a potential graph model for the analysis of online social networks. Therefore, after redefine some network measures related to stochastic multilayer graphs, we propose a Cellular Goore Game (CGG) based algorithm to computes the redefine network measures. A CGG-based algorithm computes defined network measures by learning automata from the edges of stochastic multilayer graphs. The experimental results show that the new CGG-based algorithm requires fewer samples from the edges of stochastic multilayer graphs than the standard sampling method in network measures calculation. Furthermore, the obtained results demonstrate that, from an evaluation perspective, the CGG-based algorithm provides superior results in terms of Kolmogorov-Smirnov (KS-test), Pearson Correlation Coefficient (PCC), Normalized Root Mean Square Error (NRMSE) and Kullback–Leibler divergence (KL-test).

Evaluation and Enhancement Model of the Development Potential of Green Tourism in Beijing's Ecological Conservation Area

Evaluation and Enhancement Model of the Development Potential of Green Tourism in Beijing's Ecological Conservation Area

Anti-inflammatory effects and related mechanisms in vitro and in vivo of Hedychium coccineum rhizome essential oil

Ethnopharmacological relevanceHedychium coccineum rhizome is an anti-inflammatory ethnomedicine used to remedy inflammation-related swelling and bronchial asthma. Aim of the studyThe study aimed to analyze the phytochemical constituents of H. coccineum rhizome essential oil (EO) and evaluate its in vitro and in vivo anti-inflammatory effects and underlying mechanisms. Materials and methodsPhytochemical constituents of H. coccineum rhizome EO were analyzed using GC-FID/MS. In RAW264.7 macrophages induced by LPS, blockade of PGE2, NO, IL-1β, IL-6, and TNF-α secretion by H. coccineum rhizome EO was measured, and then Western blot, qRT-PCR, and immunofluorescent staining were used to evaluate its underlying mechanisms. Moreover, we used the xylene-induced ear edema model for testing anti-inflammatory potential in vivo and examined auricular swelling as well as tissue and serum contents of IL-1β, IL-6, and TNF-α. ResultsEO's main components were E-nerolidol (40.5%), borneol acetate (24.8%), spathulenol (4.5%), linalool (3.8%), elemol (3.5%), and borneol (3.4%). In RAW264.7 cells stimulated by LPS, EO downregulated the expression of pro-inflammatory enzyme (iNOS and COX-2) genes and proteins, thereby suppressing pro-inflammatory mediators (NO and PGE2) secretion. Simultaneously, it reduced TNF-α, IL-1β, and IL-6 release by downregulating their mRNA expression. Besides, H. coccineum EO attenuated LPS-stimulated activation of NF-κB (by reducing IκBα phosphorylation and degradation to inhibit NF-κB nuclear translocation) and MAPK (by downregulating JNK, p38, and ERK phosphorylation). In xylene-induced mouse ear edema, EO relieved auricular swelling and lowered serum and tissue levels of TNF-α, IL-1β, and IL-6. ConclusionsH. coccineum EO had powerful in vivo and in vitro anti-inflammatory effects by inhibiting MAPK and NF-κB activation. Hence, H. coccineum EO should have great potential for application in the pharmaceutical field as a novel anti-inflammatory agent.

Mimetic Mediators in Mark: How Graeco-Roman Biographies Use Secondary Characters to Offer Multiple Patterns of Imitation

Can the Markan disciples still be viewed as potential role models for the Gospel audience if Mark’s writing is identified as a biography? This long-standing line of narrative interpretation has recently been rejected as anachronistic by Helen K. Bond, who maintains that in Graeco-Roman biographies, secondary characters are only included for what they bring to the portrait of the protagonist. In response, this paper demonstrates that ancient biographies regularly use followers of their main characters to provide multiple mimetic patterns that clarify, broaden, and mitigate what it means to imitate their heroes. In particular, Mark’s cast of secondary characters offers three alternative patterns of behaviour for potential followers of Jesus: apostles, who emulate his itinerant lifestyle of preaching, healing, and exorcism; hosts, who provide apostles with food and shelter in their homes; and supporters, who serve the movement in other ways in accordance with their abilities and social status.

Numerical modelling of the IMF influence on the large-scale structure of the ionosphere, including the poles displacement

The interplanetary magnetic field (IMF), frozen into the solar wind plasma, determines the level of geomagnetic activity and significantly affects the large-scale structure of the high-latitude ionosphere. Variations in the IMF components lead to non-stationarity of the magnetospheric convection electric field, causing changes in the spatio-temporal distribution of charged particles in the ionosphere. The situation is complicated by the fact that the geographic and geomagnetic poles do not coincide, which leads to universal time control (UT control) of the parameters of the high-latitude ionosphere. The effect of the poles displacement is most manifest in winter. Therefore, in this work, within the framework of a nonstationary mathematical model of the F-region of the ionosphere in Euler variables, taking into account the displacement between the geographic and geomagnetic poles, we study the influence of the interplanetary magnetic field components on the large-scale structure of the high-latitude ionosphere in winter. For this, the Weimer model of high-latitude electric potentials, which depends on the IMF, was used. As a result, of numerical modelling, it is shown that the IMF components affect the shape, size and spatio-temporal location of the main structural features of the high-latitude ionosphere depending on universal time. The obtained effect of ionisation tongue separation requires experimental confirmation. The obtained results demonstrate that the developed non-stationary three-dimensional model of the ionosphere in Euler variables can be applied to the study of ionospheric disturbances, including ionospheric disturbances associated with changes in the IMF components.

Parameters of the cognitive potential of educational text

INTRODUCTION. The subject of the study is the cognitive potential of educational text. The purpose of the study is to determine the parameters of the cognitive potential of an educational text that make up its conceptual model. The relevance of the study is explained by the insufficient knowledge of the cognitive characteristics of educational text as a means of cognitive activity.MATERIALS AND METHODS. To achieve the goal, the author analyzes theoretical and practical material using generalization, systematization and comparison and modeling methods. The research uses domestic and foreign research materials on the problem of academic training text and electronic English-language educational texts for teaching English.RESULTS AND DISCUSSION. The author uses the concept of cognitive potential to identify and summarize cognitive characteristics. The cognitive potential of educational text is understood as its ability to accumulate, store and transmit various types of knowledge in linguistic form. According to the notion, the a uthor proposes a model of the cognitive potential of the text, which includes three main parameters: the goal-oriented parameter, the subject-oriented parameter, and the text-oriented parameter.CONCLUSION. A conclusion is made about the relationship and heterogeneity of the parameters. It is noted that some parameters have a level structure, since the subjects of cognitive activity are the author and the addressee of the educational text, and the text parameter should be considered in conceptual and linguistic aspects. The development of research can complement and clarify the proposed model of the cognitive potential of educational text. The prospect is to study the relationship between knowledge formats, levels of knowledge in an educational text, as well as the study of cognitive and linguistic models and mechanisms of formation and representation of meaning.

Predicting the tensile and compressive modulus of electrospun fiber mat‐reinforced hydrogels using the Halpin–Tsai equations

AbstractThe reinforcement of mechanically‐weak hydrogels to yield composites with increased stiffness, strength, or toughness is a well‐established approach. In particular, introducing electrospun nanofibers into hydrogels is a common strategy for biomedical applications, as the resulting hierarchical structure mimics biology and allows for control over fiber diameter and alignment and tuning of mechanical properties. However, further study of the link between the constituent materials and the mechanical properties of the composite is uncommon. One potential model to understand the mechanical properties of fiber‐reinforced hydrogels involves the Halpin–Tsai equations, which relate the modulus values of the fibers and hydrogel matrix and the fiber volume fraction, to the modulus of the composite. To assess the application of this model to fiber‐reinforced hydrogels, predicted values were compared with experimental values from mechanical testing of a poly(ethylene glycol) (PEG) matrix reinforced with an electrospun polycaprolactone (PCL) fiber mat. Although the equations described these systems well in tension, providing a facile approach to identify a fiber volume fraction that will achieve a desired modulus, the Halpin–Tsai approach was less successful under compression. This study motivates additional investigation of the role of structural features of hydrogel composites in determining mechanical properties to enable design of materials for specific applications.

On stellar migration from Andromeda to the Milky Way

ABSTRACT Recent Gaia observations suggest that some hypervelocity stars (HVSs) might originate from outside the Galaxy. We ask whether these HVSs could come from as far as Andromeda. Therefore, we simulate HVSs originating in Andromeda with initial conditions based on attributes of high-velocity stars measured in the Milky Way and a simple model for the gravitational potential of Andromeda and the Milky Way. We evaluate the validity of this scenario based on the simulation results. While we expect that the vast majority of HVSs in our Galaxy will originate here, we expect the number of stars present from Andromeda at any one time to be between 12 and 3910, depending upon model assumptions. Further, we analyse the properties of HVSs that are able to reach the Milky Way. We discuss whether they could be detected experimentally based on recent constraints set on the ejection rate of HVSs from the Milky Way centre.

Confidence and consistency in discrimination: A new family of evaluation metrics for potential distribution models

Potential distribution models (PDMs) are widely applied to understand and predict biogeographic patterns. PDM evaluation, however, presents major challenges, including (1) matches of predictions with observed absences and presences being treated similarly and (2) treatment of predicted presences falling outside the observations as errors, while a major motivation of PDMs is to identify such locations. Our aim was to construct a family of model performance metrics to measure the reliability and transferability of PDMs while providing solutions to the problems mentioned above.Instead of binarisation, predictions are reclassified into three categories for model evaluation: certain negative, uncertain and certain positive predictions. Model performance is tested solely within known presences to reduce the effect of unoccupied but suitable sites registered as observed absences. Metrics were developed for both cases: (1) when the target of the modelling is the identification of potential presence locations and (2) when the target is the evaluation of potential presences and absences equally.The new measures offer evaluation optimised for PDM models. On the one hand, (1) the proposed metrics concentrate on matches within observed presences. Thus, matches within the typically large amount of observed absences do not inflate the metric values. On the other hand, (2) the proposed metrics do not treat all mismatches within observed presences as errors and thus allow exploitation of information in the mismatches, too. Besides the theoretical background, we also provide a new R package for calculating the measures.We tested the metrics both on field and simulation data. Both field-based and simulation-based case studies underlined that the new metrics capture a different aspect of model performance than the traditional metrics, such as AUC, TSS, sensitivity and specificity.We conclude that our metrics help to identify whether the modelling process could capture the preferences of the object well enough to reliably find further suitable sites or stay reliable when transferred in space and time.

Spectroscopic and Theoretical Identifications of Two Structural Motifs of (H2O)10 Cluster.

Precise characterization of archetypal systems of aqueous hydrogen-bonding networks is essential for developing accurate potential functions and universal models of water. The structures of water clusters (H2O)n (n = 2-9) have been verified recently through size-specific infrared spectroscopy with a vacuum ultraviolet free electron laser (VUV-FEL) and quantum chemical studies. For (H2O)10, the pentagonal prism and butterfly motifs were proposed to be important building blocks and were observed in previous experiments. Here we report the size-specific infrared spectra of (H2O)10 via a joint experimental and theoretical study. Well-resolved spectra provide a unique signature for the coexistence of pentagonal prism and butterfly motifs. These (H2O)10 motifs develop from the dominant structures of (H2O)n (n = 8, 9) clusters. This work provides an intriguing prelude to the diverse structure of liquid water and opens avenues for size-dependent measurement of larger systems to understand the stepwise formation mechanism of hydrogen-bonding networks.

MSCs alleviate LPS-induced acute lung injury by inhibiting the proinflammatory function of macrophages in mouse lung organoid–macrophage model

Acute lung injury (ALI) is an inflammatory disease associated with alveolar injury, subsequent macrophage activation, inflammatory cell infiltration, and cytokine production. Mesenchymal stem cells (MSCs) are beneficial for application in the treatment of inflammatory diseases due to their immunomodulatory effects. However, the mechanisms of regulatory effects by MSCs on macrophages in ALI need more in-depth study. Lung tissues were collected from mice for mouse lung organoid construction. Alveolar macrophages (AMs) derived from bronchoalveolar lavage and interstitial macrophages (IMs) derived from lung tissue were co-cultured, with novel matrigel-spreading lung organoids to construct an in vitro model of lung organoids–immune cells. Mouse compact bone-derived MSCs were co-cultured with organoids–macrophages to confirm their therapeutic effect on acute lung injury. Changes in transcriptome expression profile were analyzed by RNA sequencing. Well-established lung organoids expressed various lung cell type-specific markers. Lung organoids grown on spreading matrigel had the property of functional cells growing outside the lumen. Lipopolysaccharide (LPS)-induced injury promoted macrophage chemotaxis toward lung organoids and enhanced the expression of inflammation-associated genes in inflammation-injured lung organoids–macrophages compared with controls. Treatment with MSCs inhibited the injury progress and reduced the levels of inflammatory components. Furthermore, through the nuclear factor-κB pathway, MSC treatment inhibited inflammatory and phenotypic transformation of AMs and modulated the antigen-presenting function of IMs, thereby affecting the inflammatory phenotype of lung organoids. Lung organoids grown by spreading matrigel facilitate the reception of external stimuli and the construction of in vitro models containing immune cells, which is a potential novel model for disease research. MSCs exert protective effects against lung injury by regulating different functions of AMs and IMs in the lung, indicating a potential mechanism for therapeutic intervention.

Effectiveness of the Pasifika Women's Diabetes Wellness Program (PWDWP): Protocol for a Pilot Intervention and Feasibility Randomized Controlled Trial.

Type 2 diabetes poses public health challenges for Māori and Pasifika communities in Australia. The women of these communities face a greater burden from type 2 diabetes-related mortality and comorbidities. Lifestyle modification behaviors through previous women's wellness programs have been shown to reduce the risk of developing complications in established type 2 diabetes. The Pasifika Women's Diabetes Wellness Program (PWDWP) pilot study, co-designed with Māori and Pasifika communities, was aimed at addressing late hospital presentations from diabetes-related complications. This study (1) examines the efficacy of women with type 2 diabetes in the intervention group for improved glycated hemoglobin (HbA1c) clinical levels and diabetes self-management compared with the control group from baseline (T0) to week 12 (T1) and week 24 (T2; postintervention) and (2) assesses the cultural adaptability, acceptability, and feasibility of the pilot intervention for future studies. This study uses a quasiexperimental design that involves a 24-week intervention. We recruited 50 Māori and Pasifika women with type 2 diabetes (25 in the intervention group from the south side of Brisbane and 25 in the control group from the north side of Brisbane) using participatory talanoa methodologies. The intervention group participated in face-to-face and virtual whānau education workshops (5 weeks) and had access to individual coaching and virtual support delivered by trained Māori and Pasifika health professionals and community health workers. The control group received usual care with their identified health provider. Both groups received copies of the PWDWP journal, fact sheets, and a health check passbook with tailored motivational text messages. An advisory committee was set up to oversee the program implementation, including protocols of engagement, health checks, and data collection in community settings. The quantitative data were collected at T0, T1, and T2 with HbA1c as the primary outcome measure. Secondary outcomes measured changes in diabetes self-care and body composition (eg, BMI, waist circumference). Qualitative data will ascertain the program's feasibility and cultural adaptability using talanoa focus groups. This pilot study was approved by the Queensland University of Technology Human Ethic Research Committee (5609) and began in January 2023 after participant recruitment between July 2022 and December 2022. The final data collection including the health check, focus group, and survey data was completed in November 2023, and data analysis and reporting are expected to conclude in 2024. This study provides a blueprint for PWDWP. Collaborative partnerships with community organizations and stakeholders are crucial for program success and suggest a potential model for targeting diabetes management for Māori and Pasifika communities, emphasizing the need for culturally relevant interventions. The findings will have significant implications for policymakers and practitioners when developing and implementing public health initiatives, particularly for communities with unique cultural nuances. Australian New Zealand Clinical Trials Registry ACTRN12622001100785p; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384470&isReview=true. DERR1-10.2196/55435.

Influence of repulsion on entropy scaling and density scaling of monatomic fluids.

Entropy scaling is applied to the shear viscosity, self-diffusion coefficient, and thermal conductivity of simple monatomic fluids. An extensive molecular dynamics simulation series is performed to obtain these transport properties and the residual entropy of three potential model classes with variable repulsive exponents: n, 6 Mie (n = 9, 12, 15, and 18), Buckingham's exponential-six (α = 12, 14, 18, and 30), and Tang-Toennies (αT = 4.051, 4.275, and 4.600). A wide range of liquid and supercritical gas- and liquid-like states is covered with a total of 1120 state points. Comparisons to equations of state, literature data, and transport property correlations are made. Although the absolute transport property values within a given potential model class may strongly depend on the repulsive exponent, it is found that the repulsive steepness plays a negligible role when entropy scaling is applied. Hence, the plus-scaled transport properties of n, 6 Mie, exponential-six, and Tang-Toennies fluids lie basically on one master curve, which closely corresponds with entropy scaling correlations for the Lennard-Jones fluid. This trend is confirmed by literature data of n, 6 Mie, and exponential-six fluids. Furthermore, entropy scaling holds for state points where the Pearson correlation coefficient R is well below 0.9. The condition R > 0.9 for strongly correlating liquids is thus not necessary for the successful application of entropy scaling, pointing out that isomorph theory may be a part of a more general framework that is behind the success of entropy scaling. Density scaling reveals a strong influence of the repulsive exponent on this particular approach.

Diffusiophoretic Behavior of Polyelectrolyte-Coated Particles.

Diffusiophoresis, the movement of particles under a solute concentration gradient, has practical implications in a number of applications, such as particle sorting, focusing, and sensing. For diffusiophoresis in an electrolyte solution, the particle velocity is described by the electrolyte relative concentration gradient and the diffusiophoretic mobility of the particle. The electrolyte concentration, which typically varies throughout the system in space and time, can also influence the zeta potential of particles in space and time. This variation affects the diffusiophoretic behavior, especially when the zeta potential is highly dependent on the electrolyte concentration. In this work, we show that adsorbing a single bilayer (or 4 bilayers) of a polyelectrolyte pair (PDADMAC/PSS) on the surface of microparticles resulted in effectively constant zeta potential values with respect to salt concentration throughout the experimental range of salt concentrations. This allowed a constant potential model for diffusiophoretic transport to describe the experimental observations, which was not the case for uncoated particles in the same electrolyte system. This work highlights the use of simple polyelectrolyte pairs to tune the zeta potential and maintain constant values for precise control of diffusiophoretic transport.

Approximate bound state solutions of the fractional Schr\"{o}dinger equation under the spin-spin-dependent Cornell potential

In this work, the approximate bound state solutions of the fractional Schr\"{o}dinger equation under a spin-spin-dependent Cornell potential are obtained via the convectional Nikiforov-Uvarov approach. The energy spectra are applied to obtain the mass spectra of the heavy mesons such as bottomonium, charmonium and bottom-charm. The masses for the singlet and triplet spin numbers increase as the quantum numbers increase. The fractional Schr\"{o}dinger equation improves the mass spectra compared to other theoretic masses. The bottomonium masses agree with the experimental results where percentage errors for fractional parameters of $\beta =1,\alpha =0.97$ and $\beta =1,\alpha =0.50$ were found to be 0.67\% and 0.49\% respectively. The respective percentage errors of 1.97\% and 1.62\% for fractional parameters of $\beta =1,\alpha =0.97$ and $\beta =1,\alpha =0.50$ were obtained for charmonium meson. The results indicate that the potential curves coupled with the fractional parameters account for the short-range gluon exchange between the quark-antiquark interactions and the linear confinement phenomena which is associated with the quantum chromo-dynamic and phenomenological potential models in particle and high-energy physics.

Calorine: A Python package for constructing and sampling neuroevolution potential models

calorine: A Python package for constructing and sampling neuroevolution potential models

System energy and band gap prediction of titanium dioxide based on machine learning

Fast and accurate prediction of certain properties from a given structure, as well as pinpointing key factors influencing particular properties, is crucial for the targeted design of new compounds with desired properties or crystal phase compositions. In the current work, a total of 2,419,200 Gaussian-type structure descriptors (GTSDs) were utilized as a bridge to establish artificial neural network (ANN) potential models between 1400 titanium dioxide (TiO2) structures and their corresponding system energies and band gaps. After training, the ANN showed high accuracy in predicting energy, with Rtrain of 0.985, Rtest of 0.967, and RMSE of 0.0489. However, for the band gap values with intense data fluctuation, the Rtrain, Rtest, and RMSE changed to 0.864, 0.832, and 0.1304, respectively, indicating that the ANN's predictive ability for drastically changing data is limited. Compared with ab initio calculations, using ANN model can increase the calculation speed by 4–6 orders of magnitude. Furthermore, we identified the most significant features affecting the system energy and band gap values as the shape and distortion of [TiOx] polyhedra through the random forest model. Our work aims to provide a new toolkit and research ideas for accelerating the development of new materials.