Mahasen H. Albelbeisi, Saleh Chebaane, Sana Ben Khalifa, Norah A. M. Alsaif
The primary causes of the high cost of perovskite solar cells are metal electrodes and hole transport layers. In this theoretical work, we examine the outputs of a hole transport layer‐free carbon‐based solar cell with an FTO/ETL/Cs 2 PtI 6 /Carbon electrode structure using the Solar Cell Capacitance Simulator (SCAPS‐1D). The paper studied various carbon electrode types‐Graphene/Carbon Black (G/CB) (5 eV), Graphene (4.9 eV), Graphene Oxide (GO) (4.8 eV), and Bio‐carbon (4.5 eV)‐ and electron transport layers‐SnO 2 , TiO 2 , LBSO, and WO 3 . The studied parameters included perovskite and ETL layer thickness, doping density, and defect density. The outputs showed that the best PCE of 15.50% resulted from using G/CB electrode and TiO 2 as the ETL, with a thickness of 0.09 µm, and a doping density of 10 × 10 19 cm −3 . Additionally, for the Cs 2 PtI 6 absorber layer, a Cs 2 PtI 6 composition with a thickness of 1.2 µm, a defect density of 1× 10 15 cm −3 , and a doping density of 10 × 10 12 cm −3 demonstrated superior performance, resulting in a PCE of 15.50%. These findings suggest that the FTO/TiO 2 /Cs 2 PtI 6 /G/CB structure, particularly with optimized TiO 2 and Cs 2 PtI 6 layers, holds great potential for hole transport layer‐free‐carbon‐based solar cell fabrication. Furthermore, machine learning models with a random forest algorithm evaluated the relative importance of the features on cell efficiency, and predicted the efficiency of the suggested configuration with R 2 of 0.93 underscoring the potential of machine learning in enhancing solar cell design and performance.
{"title":"Advancing HTL‐Free Cs 2 PtI 6 Carbon Perovskite Solar Cells: Insights from Hybrid Simulation and Machine Learning","authors":"Mahasen H. Albelbeisi, Saleh Chebaane, Sana Ben Khalifa, Norah A. M. Alsaif","doi":"10.1002/adts.202501860","DOIUrl":"https://doi.org/10.1002/adts.202501860","url":null,"abstract":"The primary causes of the high cost of perovskite solar cells are metal electrodes and hole transport layers. In this theoretical work, we examine the outputs of a hole transport layer‐free carbon‐based solar cell with an FTO/ETL/Cs <jats:sub>2</jats:sub> PtI <jats:sub>6</jats:sub> /Carbon electrode structure using the Solar Cell Capacitance Simulator (SCAPS‐1D). The paper studied various carbon electrode types‐Graphene/Carbon Black (G/CB) (5 eV), Graphene (4.9 eV), Graphene Oxide (GO) (4.8 eV), and Bio‐carbon (4.5 eV)‐ and electron transport layers‐SnO <jats:sub>2</jats:sub> , TiO <jats:sub>2</jats:sub> , LBSO, and WO <jats:sub>3</jats:sub> . The studied parameters included perovskite and ETL layer thickness, doping density, and defect density. The outputs showed that the best PCE of 15.50% resulted from using G/CB electrode and TiO <jats:sub>2</jats:sub> as the ETL, with a thickness of 0.09 µm, and a doping density of 10 × 10 <jats:sup>19</jats:sup> cm <jats:sup>−3</jats:sup> . Additionally, for the Cs <jats:sub>2</jats:sub> PtI <jats:sub>6</jats:sub> absorber layer, a Cs <jats:sub>2</jats:sub> PtI <jats:sub>6</jats:sub> composition with a thickness of 1.2 µm, a defect density of 1× 10 <jats:sup>15</jats:sup> cm <jats:sup>−3</jats:sup> , and a doping density of 10 × 10 <jats:sup>12</jats:sup> cm <jats:sup>−3</jats:sup> demonstrated superior performance, resulting in a PCE of 15.50%. These findings suggest that the FTO/TiO <jats:sub>2</jats:sub> /Cs <jats:sub>2</jats:sub> PtI <jats:sub>6</jats:sub> /G/CB structure, particularly with optimized TiO <jats:sub>2</jats:sub> and Cs <jats:sub>2</jats:sub> PtI <jats:sub>6</jats:sub> layers, holds great potential for hole transport layer‐free‐carbon‐based solar cell fabrication. Furthermore, machine learning models with a random forest algorithm evaluated the relative importance of the features on cell efficiency, and predicted the efficiency of the suggested configuration with R <jats:sup>2</jats:sup> of 0.93 underscoring the potential of machine learning in enhancing solar cell design and performance.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"9 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The critical behavior of (1−x) LSMO/xNaF composites with x = 0, 0.05, 0.15, and 0.20 near the second‐order paramagnetic–ferromagnetic transition is investigated through a combination of Arrott–Noakes formalism (ANF) and Kouvel–Fisher (KF) analysis. Critical exponents ( β , γ ) are determined iteratively to be (1.0004, 0.3406), (1.1593, 0.6230), (1.0467, 0.4391), and (1.0479, 0.4673) for (1‐x)LSMO/xNaF with x = 0, 0.05, 0.15, and 0.20, respectively. Furthermore, magnetocaloric entropy changes , computed via Landau theory, exhibited strong correspondence with Maxwell relation results, with minor discrepancies at high fields attributed to saturation effects. Overall, the results highlight the robustness of Landau phenomenology in describing criticality and magnetocaloric behavior, while revealing subtle doping‐induced modifications in exchange interactions.
{"title":"Critical Behavior and Magnetocaloric Simulation in LSMO/NaF Composites Using Landau Theory","authors":"Mohamed Hsini, Nadia Zaidi, Amel Haouas","doi":"10.1002/adts.202501677","DOIUrl":"https://doi.org/10.1002/adts.202501677","url":null,"abstract":"The critical behavior of (1−x) LSMO/xNaF composites with x = 0, 0.05, 0.15, and 0.20 near the second‐order paramagnetic–ferromagnetic transition is investigated through a combination of Arrott–Noakes formalism (ANF) and Kouvel–Fisher (KF) analysis. Critical exponents ( <jats:italic>β</jats:italic> , <jats:italic>γ</jats:italic> ) are determined iteratively to be (1.0004, 0.3406), (1.1593, 0.6230), (1.0467, 0.4391), and (1.0479, 0.4673) for (1‐x)LSMO/xNaF with x = 0, 0.05, 0.15, and 0.20, respectively. Furthermore, magnetocaloric entropy changes , computed via Landau theory, exhibited strong correspondence with Maxwell relation results, with minor discrepancies at high fields attributed to saturation effects. Overall, the results highlight the robustness of Landau phenomenology in describing criticality and magnetocaloric behavior, while revealing subtle doping‐induced modifications in exchange interactions.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"6 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145711415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A conceptual design of a two-terminal tandem solar cell featuring ZrS2 as the top absorber and CH3NH3SnI3 perovskite as the bottom absorber layer of the two sub-cells. Color-coded incident photon paths illustrate wavelength-selective absorption in each sub-cell, highlighting efficient spectral utilization and enhanced energy conversion performance. More details can be found in the Research Article by Avijit Kumar, Shyamal Chatterjee, and co-workers (DOI: 10.1002/adts.202501325).
{"title":"Computational Design of High-Efficiency ZrS2/Perovskite Tandem Solar Cell via Bandgap and Current Matching Optimization (Adv. Theory Simul. 12/2025)","authors":"Nabin Kumar Shaw, Basudeba Maharana, Avijit Kumar, Shyamal Chatterjee","doi":"10.1002/adts.70208","DOIUrl":"https://doi.org/10.1002/adts.70208","url":null,"abstract":"<p>A conceptual design of a two-terminal tandem solar cell featuring ZrS<sub>2</sub> as the top absorber and CH<sub>3</sub>NH<sub>3</sub>SnI<sub>3</sub> perovskite as the bottom absorber layer of the two sub-cells. Color-coded incident photon paths illustrate wavelength-selective absorption in each sub-cell, highlighting efficient spectral utilization and enhanced energy conversion performance. More details can be found in the Research Article by Avijit Kumar, Shyamal Chatterjee, and co-workers (DOI: 10.1002/adts.202501325).\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"8 12","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/adts.70208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Issue Information (Adv. Theory Simul. 12/2025)","authors":"","doi":"10.1002/adts.70207","DOIUrl":"https://doi.org/10.1002/adts.70207","url":null,"abstract":"","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"8 12","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/adts.70207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Khaled Matarneh, Ahmad A. Abubaker, Ubaidullah Yashkun, Adnan Asghar, Raja'i Aldiabat, Liaquat Ali Lund
This numerical study investigates dual solution branches of sodium alginate‐based hybrid nanofluid flow over exponentially stretching and shrinking surfaces. Main objective is to analyze how solid volume fraction affects stretching and shrinking behavior, and to evaluate variations in skin friction coefficient and heat transfer rate under suction and permeability effects. Additionally, influences of permeability, magnetic field strength, and viscous dissipation on velocity and temperature profiles of hybrid nanofluid are thoroughly examined. By applying an exponential similarity transformation, governing partial differential equations are reduced to ordinary differential equations. These transformed equations are solved numerically using the three‐stage Lobatto III‐A formula via MATLAB's bvp4c solver. Results confirm the presence of dual (non‐unique) solutions within specific parameter ranges, with non‐uniqueness arising as controlling parameters approach critical suction or shrinking limits. An increase in permeability parameter decreases heat transfer and skin friction for the upper branch, while a similar decreasing trend is noted for the lower branch. Temperature gradients reduce with higher permeability, whereas higher Eckert numbers amplify thermal effects. Increasing copper nanoparticle volume fraction suppresses heat transfer for the upper branch but enhances it for the lower branch. Overall, findings offer valuable insights for optimizing fluid flow and thermal management in engineering applications.
{"title":"Heat Transfer Analysis of Alumina+Copper/Sodium Alginate Radiative Hybrid Nanofluid in Darcy‐Forchheimer Flow Over a Nonlinear Stretching/Shrinking Porous Surface","authors":"Khaled Matarneh, Ahmad A. Abubaker, Ubaidullah Yashkun, Adnan Asghar, Raja'i Aldiabat, Liaquat Ali Lund","doi":"10.1002/adts.202501778","DOIUrl":"https://doi.org/10.1002/adts.202501778","url":null,"abstract":"This numerical study investigates dual solution branches of sodium alginate‐based hybrid nanofluid flow over exponentially stretching and shrinking surfaces. Main objective is to analyze how solid volume fraction affects stretching and shrinking behavior, and to evaluate variations in skin friction coefficient and heat transfer rate under suction and permeability effects. Additionally, influences of permeability, magnetic field strength, and viscous dissipation on velocity and temperature profiles of hybrid nanofluid are thoroughly examined. By applying an exponential similarity transformation, governing partial differential equations are reduced to ordinary differential equations. These transformed equations are solved numerically using the three‐stage Lobatto III‐A formula via MATLAB's bvp4c solver. Results confirm the presence of dual (non‐unique) solutions within specific parameter ranges, with non‐uniqueness arising as controlling parameters approach critical suction or shrinking limits. An increase in permeability parameter decreases heat transfer and skin friction for the upper branch, while a similar decreasing trend is noted for the lower branch. Temperature gradients reduce with higher permeability, whereas higher Eckert numbers amplify thermal effects. Increasing copper nanoparticle volume fraction suppresses heat transfer for the upper branch but enhances it for the lower branch. Overall, findings offer valuable insights for optimizing fluid flow and thermal management in engineering applications.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"20 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dual‐phase high‐entropy alloys (DPHEAs) exhibit superior strength‐ductility synergy compared to single‐phase counterparts, with properties strongly governed by phase fraction. This necessitates robust quantitative phase prediction, as most prior studies remain qualitative. In this study, we develop and evaluate two complementary frameworks for quantitative phase prediction in DPHEAs: a threshold‐based approach and a machine learning (ML) approach. Both methods incorporate key descriptors of electronic structure and atomic size mismatch, including valence electron concentration (VEC), atomic size difference (δ), d‐orbital energy level (Md), and a newly introduced critical mismatch factor (δ c ). Amongst the threshold‐based approach, VEC + δ c showed the highest accuracy (81%), while in the ML approach, the random forest (RF) achieved 88% accuracy. The Md and VEC have similar predictive capability within the framework of threshold‐based modeling, owing to the high linear correlation between VEC and Md (Pearson coefficient of 0.97). Whereas δ c , which shows minimal correlation with electronic descriptors, emerged as the dominant parameter in threshold‐based modeling. In contrast, feature importance analysis from the RF regression identified Md as the dominant predictor, surpassing the commonly used VEC. The study demonstrated that electronic structure descriptors and atomic mismatch measures are complementary in nature for reliable phase prediction.
{"title":"Effects of the Electron Concentration, d‐Orbital Energy Level, and Atomic Mismatch on Quantitative Phase Prediction in Complex Concentrated Alloys (CCAs): Empirical Criteria and Machine Learning Insights","authors":"Ayush Sourav, Ankit Singh Negi, Shanmugasundaram Thangaraju","doi":"10.1002/adts.202501736","DOIUrl":"https://doi.org/10.1002/adts.202501736","url":null,"abstract":"Dual‐phase high‐entropy alloys (DPHEAs) exhibit superior strength‐ductility synergy compared to single‐phase counterparts, with properties strongly governed by phase fraction. This necessitates robust quantitative phase prediction, as most prior studies remain qualitative. In this study, we develop and evaluate two complementary frameworks for quantitative phase prediction in DPHEAs: a threshold‐based approach and a machine learning (ML) approach. Both methods incorporate key descriptors of electronic structure and atomic size mismatch, including valence electron concentration (VEC), atomic size difference (δ), d‐orbital energy level (Md), and a newly introduced critical mismatch factor (δ <jats:sub>c</jats:sub> ). Amongst the threshold‐based approach, VEC + δ <jats:sub>c</jats:sub> showed the highest accuracy (81%), while in the ML approach, the random forest (RF) achieved 88% accuracy. The Md and VEC have similar predictive capability within the framework of threshold‐based modeling, owing to the high linear correlation between VEC and Md (Pearson coefficient of 0.97). Whereas δ <jats:sub>c</jats:sub> , which shows minimal correlation with electronic descriptors, emerged as the dominant parameter in threshold‐based modeling. In contrast, feature importance analysis from the RF regression identified Md as the dominant predictor, surpassing the commonly used VEC. The study demonstrated that electronic structure descriptors and atomic mismatch measures are complementary in nature for reliable phase prediction.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"36 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a numerical approach to the Zeldovich model using a fourth‐order uniform hyperbolic polynomial B‐spline collocation method. The Zeldovich model, relevant in combustion theory, describes flame propagation, thermal explosions, and detonation phenomena. In the proposed scheme, the time derivative is discretized with a finite difference method, spatial derivatives are approximated using the Crank–Nicolson method, and the nonlinear terms are linearized via the Rubin–Graves technique. The resulting system of algebraic equations satisfies the prescribed boundary conditions and is solved to obtain approximate solutions. Stability is established through von Neumann analysis, while accuracy and convergence are evaluated against exact solutions using error norms and convergence rates. The results demonstrate that the method captures the nonlinear dynamics of the Zeldovich equation with high accuracy and stability, providing a streamlined and efficient alternative for its numerical treatment.
{"title":"Unveiling Numerical Solutions of Zeldovich Model Using Collocation Method via Fourth‐Order Uniform Hyperbolic Polynomial B‐Spline","authors":"Berat Karaagac, Kolade M. Owolabi, Alaattin Esen","doi":"10.1002/adts.202501349","DOIUrl":"https://doi.org/10.1002/adts.202501349","url":null,"abstract":"This study presents a numerical approach to the Zeldovich model using a fourth‐order uniform hyperbolic polynomial B‐spline collocation method. The Zeldovich model, relevant in combustion theory, describes flame propagation, thermal explosions, and detonation phenomena. In the proposed scheme, the time derivative is discretized with a finite difference method, spatial derivatives are approximated using the Crank–Nicolson method, and the nonlinear terms are linearized via the Rubin–Graves technique. The resulting system of algebraic equations satisfies the prescribed boundary conditions and is solved to obtain approximate solutions. Stability is established through von Neumann analysis, while accuracy and convergence are evaluated against exact solutions using error norms and convergence rates. The results demonstrate that the method captures the nonlinear dynamics of the Zeldovich equation with high accuracy and stability, providing a streamlined and efficient alternative for its numerical treatment.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"26 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengyang Liang, Dexin Gao, Yuanming Cheng, JiaQi Zhang, Qing Yang
Regarding the threat posed by lithium‐ion battery charging thermal runaway to electric vehicle (EV) safety applications, this paper proposes a Q‐learning optimized multimodal deep learning framework, and based on this framework, further constructs a lithium‐ion battery charging temperature prediction model for EVs. By integrating the local feature extraction capability of Convolutional Neural Networks (CNN), the temporal memory characteristics of Long Short‐Term Memory networks (LSTM), and the temporal modeling advantages of Temporal Convolutional Networks (TCN), the framework employs a Q‐learning algorithm to optimize network weights, ultimately resulting in the formation of the EV lithium‐ion battery charging temperature prediction model (QCLT) with high‐precision prediction capabilities. Experiments selected highly correlated parameters in EV charging through Pearson correlation coefficient as inputs, and validated the model using charging data from both NCM (Nickel‐Cobalt‐Manganese) and LFP (Lithium Iron Phosphate) lithium batteries. Comparative results showed that the QCLT model demonstrated superior prediction accuracy over other benchmark models. Furthermore, dynamic warning thresholds were established using the sliding window method, with additional validation through thermal runaway data under varying ambient temperatures. Constructed based on the aforementioned multimodal deep learning framework, the QCLT model can effectively predict abnormal temperature residual variations, issuing timely warning signals before thermal runaway occurs. This provides a critical time window for implementing safety protection measures, thereby reducing accident risks.
{"title":"Deep Learning‐Driven Modeling for Thermal Runaway Warning During Lithium‐Ion Battery Charging in Electric Vehicles","authors":"Chengyang Liang, Dexin Gao, Yuanming Cheng, JiaQi Zhang, Qing Yang","doi":"10.1002/adts.202501438","DOIUrl":"https://doi.org/10.1002/adts.202501438","url":null,"abstract":"Regarding the threat posed by lithium‐ion battery charging thermal runaway to electric vehicle (EV) safety applications, this paper proposes a Q‐learning optimized multimodal deep learning framework, and based on this framework, further constructs a lithium‐ion battery charging temperature prediction model for EVs. By integrating the local feature extraction capability of Convolutional Neural Networks (CNN), the temporal memory characteristics of Long Short‐Term Memory networks (LSTM), and the temporal modeling advantages of Temporal Convolutional Networks (TCN), the framework employs a Q‐learning algorithm to optimize network weights, ultimately resulting in the formation of the EV lithium‐ion battery charging temperature prediction model (QCLT) with high‐precision prediction capabilities. Experiments selected highly correlated parameters in EV charging through Pearson correlation coefficient as inputs, and validated the model using charging data from both NCM (Nickel‐Cobalt‐Manganese) and LFP (Lithium Iron Phosphate) lithium batteries. Comparative results showed that the QCLT model demonstrated superior prediction accuracy over other benchmark models. Furthermore, dynamic warning thresholds were established using the sliding window method, with additional validation through thermal runaway data under varying ambient temperatures. Constructed based on the aforementioned multimodal deep learning framework, the QCLT model can effectively predict abnormal temperature residual variations, issuing timely warning signals before thermal runaway occurs. This provides a critical time window for implementing safety protection measures, thereby reducing accident risks.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"602 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Two-dimensional hybrid organic–inorganic perovskites (2D-HOIPs) possess remarkable photoelectric properties, including strong light absorption, high electrical conductivity, and long carrier lifetimes, making them promising candidates for optoelectronic applications. This study aims to accurately predict their band gaps using machine learning (ML) to identify high-performance 2D-HOIPs. A total of 354 data points are collected from the HHPMDB database, and 32 compositional and structural features are selected via recursive feature elimination with fivefold cross-validation. An Artificial Neural Network (ANN) model is developed, achieving an excellent predictive performance with an R2 of 0.926. Shapley Additive Explanations (SHAP) analysis is employed to interpret feature contributions to the band gap. We compared the predicted values from our models with those calculated using Generalized Gradient Approximation (GGA), ensuring an error range of approximately 0.2 eV, thereby confirming the accuracy of our models. Additionally, comparisons between Perdew–Burke–Ernzerhof (PBE) and High Local Exchange 2016 (HLE16) band gaps further confirmed model accuracy. This approach enables rapid and cost-effective prediction of the 2D-HOIP band gap.
{"title":"Interpretable Artificial Neural Networks for Band Gap Prediction in 2D Hybrid Organic–Inorganic Perovskites","authors":"Jian Chen, Jianwei Wei, Kexin Chen, Yaohui Yin, Ai Wang, Chao Xin","doi":"10.1002/adts.202500902","DOIUrl":"https://doi.org/10.1002/adts.202500902","url":null,"abstract":"Two-dimensional hybrid organic–inorganic perovskites (2D-HOIPs) possess remarkable photoelectric properties, including strong light absorption, high electrical conductivity, and long carrier lifetimes, making them promising candidates for optoelectronic applications. This study aims to accurately predict their band gaps using machine learning (ML) to identify high-performance 2D-HOIPs. A total of 354 data points are collected from the HHPMDB database, and 32 compositional and structural features are selected via recursive feature elimination with fivefold cross-validation. An Artificial Neural Network (ANN) model is developed, achieving an excellent predictive performance with an R<sup>2</sup> of 0.926. Shapley Additive Explanations (SHAP) analysis is employed to interpret feature contributions to the band gap. We compared the predicted values from our models with those calculated using Generalized Gradient Approximation (GGA), ensuring an error range of approximately 0.2 eV, thereby confirming the accuracy of our models. Additionally, comparisons between Perdew–Burke–Ernzerhof (PBE) and High Local Exchange 2016 (HLE16) band gaps further confirmed model accuracy. This approach enables rapid and cost-effective prediction of the 2D-HOIP band gap.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"247 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The receptor–ligand interactions are crucial for understanding the mechanisms of biological regulation and these interactions give a theoretical basis for the design and discovery of new drug targets. Understanding the molecular interactions between D 2 dopamine receptor and dopamine‐related analogues is essential for designing effective therapeutics. In this study, we performed a comprehensive computational investigation of the binding interactions between D 2 R and a set of catecholamines (dopamine, adrenaline, and noradrenaline) along with L‐DOPA and epinine, structurally related analogues with pharmacological significance. Molecular docking was carried out to predict binding poses and affinities, followed by molecular dynamics (MD) simulations to assess the stability and conformational dynamics of the ligand‐receptor complexes. Binding free energy using the MM‐PBSA method, NCIPLOT, QTAIM and SAPT energy decomposition are carried out to provide quantitative insights into ligand binding strengths. The results indicated that L‐DOPA exhibits the most stable interaction with D 2 R, forming persistent hydrogen bonds and hydrophobic contacts within the receptor's orthosteric binding site.
{"title":"Exploring Ligand–Receptor Dynamics: Comparative Analysis of Catecholamines, L‐DOPA, and Epinine Binding to the D 2 Dopamine Receptor","authors":"Bhabesh Baro, Biplab Sarkar","doi":"10.1002/adts.202501486","DOIUrl":"https://doi.org/10.1002/adts.202501486","url":null,"abstract":"The receptor–ligand interactions are crucial for understanding the mechanisms of biological regulation and these interactions give a theoretical basis for the design and discovery of new drug targets. Understanding the molecular interactions between D <jats:sub>2</jats:sub> dopamine receptor and dopamine‐related analogues is essential for designing effective therapeutics. In this study, we performed a comprehensive computational investigation of the binding interactions between D <jats:sub>2</jats:sub> R and a set of catecholamines (dopamine, adrenaline, and noradrenaline) along with L‐DOPA and epinine, structurally related analogues with pharmacological significance. Molecular docking was carried out to predict binding poses and affinities, followed by molecular dynamics (MD) simulations to assess the stability and conformational dynamics of the ligand‐receptor complexes. Binding free energy using the MM‐PBSA method, NCIPLOT, QTAIM and SAPT energy decomposition are carried out to provide quantitative insights into ligand binding strengths. The results indicated that L‐DOPA exhibits the most stable interaction with D <jats:sub>2</jats:sub> R, forming persistent hydrogen bonds and hydrophobic contacts within the receptor's orthosteric binding site.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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