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}
Km Priyanka, Devansh Srivastava, Shalini Vardhan, Ritu Raj Singh
An electro‐optic modulator design with optimized structural dimensions is proposed to develop a broadband interferometric phase modulator with filled slot waveguide as one arm. Analysis of the transmittance, net phase change, interference, and characterization of the device for optical modulation speed and modulation bandwidth is performed. The proposed design has a total footprint area of 342.565 in which the footprint area of the active region is 2.302 . It works in the operating spectrum of 1280 to 1625 nm and covers the entire telecom optical wavelength band (O,E,S,C, and L‐band). It also covers the modulation speed of entire mm wave band, which is 30 to 300 GHz. This capability of high modulation speed makes it a potential modulator for 5th, 6th, and next upcoming generation network architecture.
{"title":"SOI Based Broadband LiNbO 3 Interferometer Using Slot Waveguide Phase Modulator","authors":"Km Priyanka, Devansh Srivastava, Shalini Vardhan, Ritu Raj Singh","doi":"10.1002/adts.202501378","DOIUrl":"https://doi.org/10.1002/adts.202501378","url":null,"abstract":"An electro‐optic modulator design with optimized structural dimensions is proposed to develop a broadband interferometric phase modulator with filled slot waveguide as one arm. Analysis of the transmittance, net phase change, interference, and characterization of the device for optical modulation speed and modulation bandwidth is performed. The proposed design has a total footprint area of 342.565 in which the footprint area of the active region is 2.302 . It works in the operating spectrum of 1280 to 1625 nm and covers the entire telecom optical wavelength band (O,E,S,C, and L‐band). It also covers the modulation speed of entire mm wave band, which is 30 to 300 GHz. This capability of high modulation speed makes it a potential modulator for 5th, 6th, and next upcoming generation network architecture.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"158 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657513","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}
Shivam Chaturvedi, Amar Deep Pathak, Nishant Sinha, Ananth Govind Rajan
Electrochemical processes, such as water splitting and carbon dioxide/monoxide (CO 2 /CO) reduction, will play a prominent role in the ongoing quest for mitigating climate change. For such reactions, microkinetic modeling (MKM) is a valuable tool to relate electrolyzer operating conditions, such as pH, temperature, and potential, to current densities and faradaic efficiencies. However, previous studies have solely focused on steady‐state modeling of electrochemical kinetics. Here, we perform unsteady‐state MKM (USS‐MKM) with and without potential sweeping to capture transient dynamics and realistically model reaction kinetics. This analysis demonstrates that sweeping leads to accurate description of the dynamics of current‐potential relationships that arise during experimental linear sweep voltammetry or staircase voltammetry measurements. The proposed approach is validated using CO reduction and oxygen evolution reactions, where good agreement is observed between this long‐time USS‐MKM results, USS‐MKM with potential sweeping, and previously reported steady‐state MKM data. It is also showed that this approach leads to reasonable agreement with experimental CO reduction current density data. Moreover, this proposed approach is automated, scaling to large reaction mechanisms, and enables a graphical representation of electrochemical reaction networks. Overall, by enabling USS‐MKM with potential sweeping, this framework simplifies the study of complex electrocatalytic mechanisms and offers valuable insights into their operation under dynamic conditions.
{"title":"Transient Microkinetic Modeling of Electrochemical Reactions: Capturing Unsteady Dynamics of CO Reduction and Oxygen Evolution","authors":"Shivam Chaturvedi, Amar Deep Pathak, Nishant Sinha, Ananth Govind Rajan","doi":"10.1002/adts.202500799","DOIUrl":"https://doi.org/10.1002/adts.202500799","url":null,"abstract":"Electrochemical processes, such as water splitting and carbon dioxide/monoxide (CO <jats:sub>2</jats:sub> /CO) reduction, will play a prominent role in the ongoing quest for mitigating climate change. For such reactions, microkinetic modeling (MKM) is a valuable tool to relate electrolyzer operating conditions, such as pH, temperature, and potential, to current densities and faradaic efficiencies. However, previous studies have solely focused on steady‐state modeling of electrochemical kinetics. Here, we perform unsteady‐state MKM (USS‐MKM) with and without potential sweeping to capture transient dynamics and realistically model reaction kinetics. This analysis demonstrates that sweeping leads to accurate description of the dynamics of current‐potential relationships that arise during experimental linear sweep voltammetry or staircase voltammetry measurements. The proposed approach is validated using CO reduction and oxygen evolution reactions, where good agreement is observed between this long‐time USS‐MKM results, USS‐MKM with potential sweeping, and previously reported steady‐state MKM data. It is also showed that this approach leads to reasonable agreement with experimental CO reduction current density data. Moreover, this proposed approach is automated, scaling to large reaction mechanisms, and enables a graphical representation of electrochemical reaction networks. Overall, by enabling USS‐MKM with potential sweeping, this framework simplifies the study of complex electrocatalytic mechanisms and offers valuable insights into their operation under dynamic conditions.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"68 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651003","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}
In this paper, a simple reconfigurable antenna with polarization agility across three switchable frequency bands is proposed and rigorously modeled using two full‐wave electromagnetic solvers. The simulation results confirm that the antenna successfully tunes to the selected center frequencies of 4.4, 4.7, and 5.18 GHz, with corresponding impedance bandwidths of 4.3–4.52, 4.65–4.83, and 5.12–5.2 GHz, respectively, while achieving reconfigurable circular polarization (CP) in all bands. Notably, the proposed design uses only two varactor diodes for both frequency tuning and polarization control, making it one of the simplest and most cost‐effective implementations reported in the open literature. Despite this simplicity, it achieves a wide tuning range (TR) of 16.28%, an acceptable –10 dB impedance bandwidth () BW, excellent axial ratio bandwidth (AR BW) at all operating frequencies, and a high spectrum utilization efficiency of 50%.
{"title":"A Simple Reconfigurable Antenna with Polarization Agility in Three Frequency Bands: Design, Simulation and Numerical Validation","authors":"Eqab Almajali, Razan Alhamad, Anwar Jarndal, Soliman Mahmoud","doi":"10.1002/adts.202501651","DOIUrl":"https://doi.org/10.1002/adts.202501651","url":null,"abstract":"In this paper, a simple reconfigurable antenna with polarization agility across three switchable frequency bands is proposed and rigorously modeled using two full‐wave electromagnetic solvers. The simulation results confirm that the antenna successfully tunes to the selected center frequencies of 4.4, 4.7, and 5.18 GHz, with corresponding impedance bandwidths of 4.3–4.52, 4.65–4.83, and 5.12–5.2 GHz, respectively, while achieving reconfigurable circular polarization (CP) in all bands. Notably, the proposed design uses only two varactor diodes for both frequency tuning and polarization control, making it one of the simplest and most cost‐effective implementations reported in the open literature. Despite this simplicity, it achieves a wide tuning range (TR) of 16.28%, an acceptable –10 dB impedance bandwidth () BW, excellent axial ratio bandwidth (AR BW) at all operating frequencies, and a high spectrum utilization efficiency of 50%.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"49 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619634","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}
Tumor protein p53 (TP53) and mouse double minute two homolog (MDM2) regulate each other via an autoregulatory feedback loop that is frequently disrupted by MDM2 overexpression or mutation, a hallmark in sarcomas, glioblastomas, and breast carcinomas. In the absence of FDA-approved MDM2 inhibitors, a multi-stage in silico strategy is applied to identify novel candidates from COCONUT, a comprehensive natural product library. Using experimentally validated ChEMBL data, 40 machine-learning models are trained and evaluated; the best RandomForestClassifier selects 116 compounds from approximately 700,000 after sequential PAINS, Brenk, and Lipinski filtering. Docking-based screening prioritizes two leads with binding energies of <span data-altimg="/cms/asset/eca4fbde-6496-4321-807f-d4463d600bc3/adts70246-math-0001.png"></span><mjx-container ctxtmenu_counter="9" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/adts70246-math-0001.png"><mjx-semantics><mjx-mrow data-semantic-annotation="clearspeak:simple" data-semantic-children="1" data-semantic-content="0" data-semantic- data-semantic-role="negative" data-semantic-speech="negative 10.0" data-semantic-type="prefixop"><mjx-mo data-semantic- data-semantic-operator="prefixop,−" data-semantic-parent="2" data-semantic-role="subtraction" data-semantic-type="operator" rspace="1" style="margin-left: 0.056em;"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="float" data-semantic-type="number"><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:25130390:media:adts70246:adts70246-math-0001" display="inline" location="graphic/adts70246-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-children="1" data-semantic-content="0" data-semantic-role="negative" data-semantic-speech="negative 10.0" data-semantic-type="prefixop"><mo data-semantic-="" data-semantic-operator="prefixop,−" data-semantic-parent="2" data-semantic-role="subtraction" data-semantic-type="operator">−</mo><mn data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic-parent="2" data-semantic-role="float" data-semantic-type="number">10.0</mn></mrow>$-10.0$</annotation></semantics></math></mjx-assistive-mml></mjx-container> <span data-altimg="/cms/asset/cd493ee6-fa3c-4b4a-906c-6907f4111a9b/adts70246-math-0002.png"></span><mjx-container ctxtmenu_counter="10" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/adts70246-math
{"title":"Machine Learning-Guided Discovery of Natural MDM2 Inhibitors: A Multistage In Silico Pipeline from Screening to ADMET Profiling","authors":"Bishal Budha, Mohamed Mohyeldin, Ali Raza Ayub, Madan Khanal, Arjun Acharya","doi":"10.1002/adts.202501502","DOIUrl":"https://doi.org/10.1002/adts.202501502","url":null,"abstract":"Tumor protein p53 (TP53) and mouse double minute two homolog (MDM2) regulate each other via an autoregulatory feedback loop that is frequently disrupted by MDM2 overexpression or mutation, a hallmark in sarcomas, glioblastomas, and breast carcinomas. In the absence of FDA-approved MDM2 inhibitors, a multi-stage in silico strategy is applied to identify novel candidates from COCONUT, a comprehensive natural product library. Using experimentally validated ChEMBL data, 40 machine-learning models are trained and evaluated; the best RandomForestClassifier selects 116 compounds from approximately 700,000 after sequential PAINS, Brenk, and Lipinski filtering. Docking-based screening prioritizes two leads with binding energies of <span data-altimg=\"/cms/asset/eca4fbde-6496-4321-807f-d4463d600bc3/adts70246-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"9\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/adts70246-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"1\" data-semantic-content=\"0\" data-semantic- data-semantic-role=\"negative\" data-semantic-speech=\"negative 10.0\" data-semantic-type=\"prefixop\"><mjx-mo data-semantic- data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"2\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" rspace=\"1\" style=\"margin-left: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"float\" data-semantic-type=\"number\"><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:25130390:media:adts70246:adts70246-math-0001\" display=\"inline\" location=\"graphic/adts70246-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"1\" data-semantic-content=\"0\" data-semantic-role=\"negative\" data-semantic-speech=\"negative 10.0\" data-semantic-type=\"prefixop\"><mo data-semantic-=\"\" data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"2\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\">−</mo><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"float\" data-semantic-type=\"number\">10.0</mn></mrow>$-10.0$</annotation></semantics></math></mjx-assistive-mml></mjx-container> <span data-altimg=\"/cms/asset/cd493ee6-fa3c-4b4a-906c-6907f4111a9b/adts70246-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"10\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/adts70246-math","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"52 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651004","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 work studies four-wave mixing (FWM) in a double quantum dot (DQD)-metal nanoparticle (MNP) system. Two control optical waves and a weak probe are applied. The probe is characterized by its orbital angular momentum (OAM) light optical properties. An analytical form of the probe and the generated FWM signal is obtained using spatial-temporal equations. A high second control field reduces efficiency, thereby increasing the FWM signal. At weak coupling DQD-MNP, the first coupling field increases the efficiency, and a near-complete conversion is attained. Such a result is unprecedented and arises from the DQD's properties, where the manipulation between DQD states is high and the DQD behaves as a whole system. Weak coupling gives high efficiency. Such a result refers to the direct effect of the controlling fields on the FWM conversion. The OAM number increases the probe and FWM fields. A fast light is obtained, and the group-velocity peak is shifted under a strong control field. While both complete conversion and fast light are observed at the earliest, other results are within the range reported in the literature. The results obtained are essential for many critical applications.
{"title":"Complete Conversion and Fast Light From Double Quantum Dot-Metal Nanoparticle System Under the Orbital Angular Momentum Light","authors":"Mohanad Ahmed Abdulmahdi, Amin Habbeb Al-Khursan","doi":"10.1002/adts.202501150","DOIUrl":"https://doi.org/10.1002/adts.202501150","url":null,"abstract":"This work studies four-wave mixing (FWM) in a double quantum dot (DQD)-metal nanoparticle (MNP) system. Two control optical waves and a weak probe are applied. The probe is characterized by its orbital angular momentum (OAM) light optical properties. An analytical form of the probe and the generated FWM signal is obtained using spatial-temporal equations. A high second control field reduces efficiency, thereby increasing the FWM signal. At weak coupling DQD-MNP, the first coupling field increases the efficiency, and a near-complete conversion is attained. Such a result is unprecedented and arises from the DQD's properties, where the manipulation between DQD states is high and the DQD behaves as a whole system. Weak coupling gives high efficiency. Such a result refers to the direct effect of the controlling fields on the FWM conversion. The OAM number increases the probe and FWM fields. A fast light is obtained, and the group-velocity peak is shifted under a strong control field. While both complete conversion and fast light are observed at the earliest, other results are within the range reported in the literature. The results obtained are essential for many critical applications.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613950","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}
In response to pressing environmental priorities, the development of nontoxic and stable alternatives to lead-based Perovskite solar cells is critical. This study focuses on Cs2AuScI6, a lead-free Perovskite, as a promising photovoltaic material. Through density functional theory (DFT) calculations using Wien2k, a bandgap of 1.30 eV is revealed, with Au-d and Sc-d orbitals playing key roles in electronic properties and Au atoms dominating charge distribution. The material exhibits visible absorption peaks of the 105 order, indicating its potential for solar applications. Conducted by DFT, 36 configurations combining various electron transport layers and hole transport layers (HTLs) are investigated. Copper Barium Tin Sulfide (CBTS) is identified as the optimal HTL due to its alignment with the absorber material. Five standout device architectures of ITO/WS2/Cs2AuScI6/CBTS/Ni, ITO/ZnO/Cs2AuScI6/CBTS/Ni, ITO/TiO2/Cs2AuScI6/CBTS/Ni, ITO/PCBM/Cs2AuScI6/CBTS/Ni, and ITO/IGZO/Cs2AuScI6/CBTS/Ni (Where ITO means Indium Tin Oxide) achieved exceptional power conversion efficiencies of 31.48%, 31.46%, 29.44%, 28.75%, and 31.82%, respectively, surpassing the 18.61% efficiency of the ITO/C60/Cs2AuScI6/CBTS/Ni structure. The study further examines practical performance factors, including resistances, temperature effects, current–voltage (J–V) characteristics, and quantum efficiency, thereby enhancing its real-world applicability. These findings highlight the potential of Cs2AuScI6 as a nontoxic, inorganic alternative for perovskite solar technology, contributing to the sustainable development of photovoltaics.
{"title":"Performance Engineering of Cs2AuScI6 Double Halide Perovskite Solar Cell: A DFT and SCAPS-1D Approach to 31.82% Efficiency","authors":"Shuaib Mahmud, Md. Mainol Islam, Md. Mukter Hossain, Md. Mohi Uddin, Md. Ashraf Ali","doi":"10.1002/adts.202501693","DOIUrl":"https://doi.org/10.1002/adts.202501693","url":null,"abstract":"In response to pressing environmental priorities, the development of nontoxic and stable alternatives to lead-based Perovskite solar cells is critical. This study focuses on Cs<sub>2</sub>AuScI<sub>6</sub>, a lead-free Perovskite, as a promising photovoltaic material. Through density functional theory (DFT) calculations using Wien2k, a bandgap of 1.30 eV is revealed, with Au-<i>d</i> and Sc-<i>d</i> orbitals playing key roles in electronic properties and Au atoms dominating charge distribution. The material exhibits visible absorption peaks of the 10<sup>5</sup> order, indicating its potential for solar applications. Conducted by DFT, 36 configurations combining various electron transport layers and hole transport layers (HTLs) are investigated. Copper Barium Tin Sulfide (CBTS) is identified as the optimal HTL due to its alignment with the absorber material. Five standout device architectures of ITO/WS<sub>2</sub>/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni, ITO/ZnO/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni, ITO/TiO<sub>2</sub>/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni, ITO/PCBM/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni, and ITO/IGZO/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni (Where ITO means Indium Tin Oxide) achieved exceptional power conversion efficiencies of 31.48%, 31.46%, 29.44%, 28.75%, and 31.82%, respectively, surpassing the 18.61% efficiency of the ITO/C<sub>60</sub>/Cs<sub>2</sub>AuScI<sub>6</sub>/CBTS/Ni structure. The study further examines practical performance factors, including resistances, temperature effects, current–voltage (<i>J</i>–<i>V</i>) characteristics, and quantum efficiency, thereby enhancing its real-world applicability. These findings highlight the potential of Cs<sub>2</sub>AuScI<sub>6</sub> as a nontoxic, inorganic alternative for perovskite solar technology, contributing to the sustainable development of photovoltaics.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"144 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613952","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}
Organic Photovoltaic (OPV) Devices Have Emerged as a Promising Alternative to Conventional Solar Cells due to Their Flexibility, Lightweight Nature, and Potential for Low-cost Production. However, Optimizing OPV Performance Remains a Complex Challenge, Traditionally Requiring Extensive Experimental Trials or Computational Chemistry Approaches Based on Molecular Descriptors. To Accelerate the Development of High-efficiency OPVs, Artificial Intelligence (AI) Has Been Increasingly Utilized, Particularly Machine Learning Models That Rely on Chemical Descriptors. While these Methods Have Shown Success, They Are Often Limited by the Quality and Completeness of the Selected Descriptors, Potentially Overlooking Key Structural and Morphological Information. In this Work, We Propose a Novel Deep Learning Framework Leveraging Convolutional Neural Networks (CNNs) to Predict OPV Performance Directly from 2D Images of Donor and Acceptor Materials. By Employing a Customized Representation of Molecular Structures, Our Approach Captures Spatial and Hierarchical Patterns That Traditional Descriptors Based ML Models May Miss. We Compare Our Model's Predictive Capability to Conventional Machine Learning Techniques and Demonstrate Its Potential for Improving Prediction Accuracy and Generalization without Need to Add the Frontier Molecular Orbitals (FMOs) to Enhance Predictions. Our Findings Highlight the Power of Deep Learning in Accelerating the Discovery of Efficient Organic Photovoltaic Materials, Paving the Way for a Data-driven Approach to Materials Science and Device Optimization.
{"title":"Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs","authors":"Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir","doi":"10.1002/adts.202500822","DOIUrl":"https://doi.org/10.1002/adts.202500822","url":null,"abstract":"Organic Photovoltaic (OPV) Devices Have Emerged as a Promising Alternative to Conventional Solar Cells due to Their Flexibility, Lightweight Nature, and Potential for Low-cost Production. However, Optimizing OPV Performance Remains a Complex Challenge, Traditionally Requiring Extensive Experimental Trials or Computational Chemistry Approaches Based on Molecular Descriptors. To Accelerate the Development of High-efficiency OPVs, Artificial Intelligence (AI) Has Been Increasingly Utilized, Particularly Machine Learning Models That Rely on Chemical Descriptors. While these Methods Have Shown Success, They Are Often Limited by the Quality and Completeness of the Selected Descriptors, Potentially Overlooking Key Structural and Morphological Information. In this Work, We Propose a Novel Deep Learning Framework Leveraging Convolutional Neural Networks (CNNs) to Predict OPV Performance Directly from 2D Images of Donor and Acceptor Materials. By Employing a Customized Representation of Molecular Structures, Our Approach Captures Spatial and Hierarchical Patterns That Traditional Descriptors Based ML Models May Miss. We Compare Our Model's Predictive Capability to Conventional Machine Learning Techniques and Demonstrate Its Potential for Improving Prediction Accuracy and Generalization without Need to Add the Frontier Molecular Orbitals (FMOs) to Enhance Predictions. Our Findings Highlight the Power of Deep Learning in Accelerating the Discovery of Efficient Organic Photovoltaic Materials, Paving the Way for a Data-driven Approach to Materials Science and Device Optimization.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"203 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613953","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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