Metal–graphene heterostructures show significant promise in a variety of fields including electronics, catalysis, energy storage, protective coatings and sensors. Their accurate theoretical modeling, however, relies on the construction of lattice‐matched supercells that minimize spurious strain artifacts. This study presents a robust algorithm implemented as a Python tool for generating such supercells. The tool provides a configurable search with user‐defined mismatch, anisotropy, and size constraints, and exports ready‐to‐use structures for simulation. We demonstrate that the abundance of viable supercells strongly depends on the metal surface orientation, with the (111) plane yielding the richest diversity. Density functional theory (DFT) validation on a series of aluminum–graphene supercells reveals a stability plateau for mismatches below 2 and quantifies the pronounced sensitivity of graphene's electronic charge to strain – a critical effect often overlooked in supercell selection. This study provides both a powerful computational tool and guidelines for DFT modeling of strain‐sensitive 2D material interfaces.
{"title":"Building Metal–Graphene Supercells: Python Tool for Lattice Matching and DFT Validation","authors":"Mikhail S. Shilov, Sergey V. Pavlov","doi":"10.1002/adts.202501717","DOIUrl":"https://doi.org/10.1002/adts.202501717","url":null,"abstract":"Metal–graphene heterostructures show significant promise in a variety of fields including electronics, catalysis, energy storage, protective coatings and sensors. Their accurate theoretical modeling, however, relies on the construction of lattice‐matched supercells that minimize spurious strain artifacts. This study presents a robust algorithm implemented as a Python tool for generating such supercells. The tool provides a configurable search with user‐defined mismatch, anisotropy, and size constraints, and exports ready‐to‐use structures for simulation. We demonstrate that the abundance of viable supercells strongly depends on the metal surface orientation, with the (111) plane yielding the richest diversity. Density functional theory (DFT) validation on a series of aluminum–graphene supercells reveals a stability plateau for mismatches below 2 and quantifies the pronounced sensitivity of graphene's electronic charge to strain – a critical effect often overlooked in supercell selection. This study provides both a powerful computational tool and guidelines for DFT modeling of strain‐sensitive 2D material interfaces.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"16 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765386","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 integration of microrobotic stents into biomedical applications has the potential to revolutionize invasive procedures by enabling precise drug delivery, imaging, and vascular interventions. These interventions demand alloys with high radial stiffness for structural integrity and low density for biocompatibility. We developed a machine learning (ML)‐finite element analysis (FEA) framework to optimize titanium (Ti)‐based and Ti‐based high‐entropy alloys (Ti‐HEAs) compositions using a curated database of 238 alloys. Gaussian process regression (GPR) is trained on FEA‐simulated radial stiffness and constrained optimization (interior‐point, sequential quadratic programming (SQP), active‐set) identified high‐performance candidates. The interior‐point algorithm yielded the highest stiffness (483.54 kN/m) with balanced composition (Ti: 76.29 at%, Nb: 6.88%, Zr: 7.34%, Ta: 7.31%), outperforming the dataset maximum (TiSn 20, 472.49 kN/m) by 2.32% and Ti‐6Al‐4 V (368.96 kN/m) by 31%. All algorithms converged to at least 469 kN/m despite compositional diversity, confirming robustness. The framework enables rapid, physics‐informed alloy design for next‐generation biomedical microrobotics.
{"title":"ML‐augmented Ti‐based Microrobotic Stents","authors":"Ahmed Choukri Abdullah, Savas Tasoglu","doi":"10.1002/adts.202502105","DOIUrl":"https://doi.org/10.1002/adts.202502105","url":null,"abstract":"The integration of microrobotic stents into biomedical applications has the potential to revolutionize invasive procedures by enabling precise drug delivery, imaging, and vascular interventions. These interventions demand alloys with high radial stiffness for structural integrity and low density for biocompatibility. We developed a machine learning (ML)‐finite element analysis (FEA) framework to optimize titanium (Ti)‐based and Ti‐based high‐entropy alloys (Ti‐HEAs) compositions using a curated database of 238 alloys. Gaussian process regression (GPR) is trained on FEA‐simulated radial stiffness and constrained optimization (interior‐point, sequential quadratic programming (SQP), active‐set) identified high‐performance candidates. The interior‐point algorithm yielded the highest stiffness (483.54 kN/m) with balanced composition (Ti: 76.29 at%, Nb: 6.88%, Zr: 7.34%, Ta: 7.31%), outperforming the dataset maximum (TiSn 20, 472.49 kN/m) by 2.32% and Ti‐6Al‐4 V (368.96 kN/m) by 31%. All algorithms converged to at least 469 kN/m despite compositional diversity, confirming robustness. The framework enables rapid, physics‐informed alloy design for next‐generation biomedical microrobotics.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"15 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765417","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}
2 + 2 is an experimentally well‐explored reaction. The first measurement of the rate constant was carried out in 1918, and the latest rate constant measurement was done in 2020. Similarly, the first computational study of the rate constant was performed in 1935. In spite of such a long history of experimental and computational results, there are still unanswered questions for this reaction. For example, a long‐standing question regarding this reaction is whether it is a true termolecular reaction or a sequential bimolecular one. In addition, it is known that the rate constant value for this reaction decreases rapidly up to 600 K and remains almost constant with a further increase in temperature. In the present work, using CCSDT(Q)/CBS//CCSD(T)/aug‐cc‐pVDZ level of theory combined with Rice–Ramsperger–Kassel–Marcus theory/master equation kinetics calculations, we have tried to shed light on this almost century old problem.
{"title":"Energetics and Kinetics of 2NO • +O 2 →2NO 2 • Reaction: A 90 Years Old Problem","authors":"Philips Kumar Rai, Pradeep Kumar","doi":"10.1002/adts.202501377","DOIUrl":"https://doi.org/10.1002/adts.202501377","url":null,"abstract":"2 + 2 is an experimentally well‐explored reaction. The first measurement of the rate constant was carried out in 1918, and the latest rate constant measurement was done in 2020. Similarly, the first computational study of the rate constant was performed in 1935. In spite of such a long history of experimental and computational results, there are still unanswered questions for this reaction. For example, a long‐standing question regarding this reaction is whether it is a true termolecular reaction or a sequential bimolecular one. In addition, it is known that the rate constant value for this reaction decreases rapidly up to 600 K and remains almost constant with a further increase in temperature. In the present work, using CCSDT(Q)/CBS//CCSD(T)/aug‐cc‐pVDZ level of theory combined with Rice–Ramsperger–Kassel–Marcus theory/master equation kinetics calculations, we have tried to shed light on this almost century old problem.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"29 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765383","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}
Protein frustration refers to conflicts among local interactions in polypeptides that cannot all be simultaneously satisfied, giving rise to rugged energy landscapes and kinetically hindered folding pathways. Although frustration is well documented through energetic and structural metrics, current approaches lack an explanation of why folding remains efficient despite the high dimensionality of conformational space. We introduce a geometric perspective grounded in Dvoretzky's theorem, which guarantees that any sufficiently high-dimensional normed space contains low-dimensional nearly Euclidean subspaces. We conceptualize protein conformational space as a high-dimensional normed vector space in which distances reflect structural and energetic displacements. Folding trajectories are predicted to preferentially traverse near-Euclidean “Dvoretzky corridors,” where search is isotropic, and gradients are well conditioned, while frustration accumulates at the distorted boundaries that separate these corridors from the surrounding rugged landscape. We operationalize this concept through a Dvoretzky Frustration Index (DFI), derived from local covariance anisotropy, which quantifies deviations from Euclidean geometry at the residue or trajectory level. In both artificial landscapes and a GB1-based protein model, high DFI regions overlapped with areas corresponding to frustration hotspots, allosteric residues, and sites of heightened mutational sensitivity. Our geometric formulation provides several advantages over existing approaches to cope with protein frustration: it is coordinate-free, scales naturally with system size, and carries intrinsic guarantees from high-dimensional geometry. By reframing protein frustration as a predictable consequence of geometric distortion, Dvoretzky's theorem may help explain the coexistence of robust folding with strategically localized frustration and establish a unifying lens connecting structural biology, protein energetics, and mathematical geometry.
{"title":"Dvoretzky's Theorem as a Geometric Framework for Protein Frustration","authors":"Arturo Tozzi","doi":"10.1002/adts.202501780","DOIUrl":"https://doi.org/10.1002/adts.202501780","url":null,"abstract":"Protein frustration refers to conflicts among local interactions in polypeptides that cannot all be simultaneously satisfied, giving rise to rugged energy landscapes and kinetically hindered folding pathways. Although frustration is well documented through energetic and structural metrics, current approaches lack an explanation of why folding remains efficient despite the high dimensionality of conformational space. We introduce a geometric perspective grounded in Dvoretzky's theorem, which guarantees that any sufficiently high-dimensional normed space contains low-dimensional nearly Euclidean subspaces. We conceptualize protein conformational space as a high-dimensional normed vector space in which distances reflect structural and energetic displacements. Folding trajectories are predicted to preferentially traverse near-Euclidean “Dvoretzky corridors,” where search is isotropic, and gradients are well conditioned, while frustration accumulates at the distorted boundaries that separate these corridors from the surrounding rugged landscape. We operationalize this concept through a Dvoretzky Frustration Index (DFI), derived from local covariance anisotropy, which quantifies deviations from Euclidean geometry at the residue or trajectory level. In both artificial landscapes and a GB1-based protein model, high DFI regions overlapped with areas corresponding to frustration hotspots, allosteric residues, and sites of heightened mutational sensitivity. Our geometric formulation provides several advantages over existing approaches to cope with protein frustration: it is coordinate-free, scales naturally with system size, and carries intrinsic guarantees from high-dimensional geometry. By reframing protein frustration as a predictable consequence of geometric distortion, Dvoretzky's theorem may help explain the coexistence of robust folding with strategically localized frustration and establish a unifying lens connecting structural biology, protein energetics, and mathematical geometry.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"7 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753098","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 detailed numerical analysis of Fractional Quantum Hall Effect (FQHE) states in three confined 2D geometries: quantum dots, nanoribbons, and Corbino disks. Using Python-based simulations, with Kwant employed for lattice modeling and spectral analysis, the research examines how confinement geometry affects energy spectra, edge state localization, and topological stability, focusing on fractional filling factors = 1/3 and 5/2. Key parameters such as energy gaps, edge state density, and state degeneracy are analyzed across varying system sizes. The results reveal that Corbino disks exhibit superior topological robustness and stable energy gaps under confinement, while nanoribbons and quantum dots are more susceptible to edge degradation and gap suppression. These findings highlight the critical role of geometry in maintaining FQHE phase stability, offering design guidelines for integrating FQHE-based functionalities into scalable quantum electronic devices.
{"title":"Numerical Modeling of Fractional Quantum Hall Effect States in Finite Geometries for Device Miniaturization","authors":"Lokesh Sharma, Priya Mudgal, Shobha Sharma, Deepti Sharma, Debabrata Sikdar","doi":"10.1002/adts.202501582","DOIUrl":"https://doi.org/10.1002/adts.202501582","url":null,"abstract":"This study presents a detailed numerical analysis of Fractional Quantum Hall Effect (FQHE) states in three confined 2D geometries: quantum dots, nanoribbons, and Corbino disks. Using Python-based simulations, with Kwant employed for lattice modeling and spectral analysis, the research examines how confinement geometry affects energy spectra, edge state localization, and topological stability, focusing on fractional filling factors <span data-altimg=\"/cms/asset/c9fb2bee-a341-4d5b-8fd8-d358b1029d95/adts70275-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/adts70275-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"bold-italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"bold italic nu\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:25130390:media:adts70275:adts70275-math-0001\" display=\"inline\" location=\"graphic/adts70275-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"bold-italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"bold italic nu\" data-semantic-type=\"identifier\" mathvariant=\"bold-italic\">ν</mi></mrow>${bm{nu }}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> = 1/3 and 5/2. Key parameters such as energy gaps, edge state density, and state degeneracy are analyzed across varying system sizes. The results reveal that Corbino disks exhibit superior topological robustness and stable energy gaps under confinement, while nanoribbons and quantum dots are more susceptible to edge degradation and gap suppression. These findings highlight the critical role of geometry in maintaining FQHE phase stability, offering design guidelines for integrating FQHE-based functionalities into scalable quantum electronic devices.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"19 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753137","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 crucial role of organic cation orientation in shaping the optoelectronic properties of hybrid organic–inorganic perovskites (HOIPs) is revealed through a systematic investigation of a diverse set of organic cations, the orientational preferences of many of which are considered for the first time. 22 different organic cations were incorporated as A‐site cations in the cubic, orthorhombic, and tetragonal crystal phases of . To explore a broad configurational space, these cations were randomly rotated, generating over 2500 structural variations, among which approximately 400 exhibited distinct symmetry. Density functional theory (DFT) calculations on these structures revealed that variations in organic A‐cation orientation can induce formation energy and bandgap differences of up to 0.32 and 0.64 eV, respectively. Rather than aiming to reproduce exactly known experimental compounds for each perovskite, we used a unified modeling approach to systematically explore how the size and orientation of organic cations govern lattice distortion and electronic structure in perovskite frameworks. Through rotational screening, a new orthorhombic phase of the well‐known (MA = ) was identified, in which MA cations are aligned along the [102] and [10 directions. Additionally, a novel pseudocubic triclinic perovskite, (TiZ = ), was discovered and validated as a stable perovskite based on its formation energy, bandgap, effective mass, mechanical properties, and dynamic stability.
{"title":"Organic Cation Orientation Preferences in Hybrid Lead Halide Perovskites","authors":"Somayyeh Alidoust, Adem Tekin","doi":"10.1002/adts.202501645","DOIUrl":"https://doi.org/10.1002/adts.202501645","url":null,"abstract":"The crucial role of organic cation orientation in shaping the optoelectronic properties of hybrid organic–inorganic perovskites (HOIPs) is revealed through a systematic investigation of a diverse set of organic cations, the orientational preferences of many of which are considered for the first time. 22 different organic cations were incorporated as A‐site cations in the cubic, orthorhombic, and tetragonal crystal phases of . To explore a broad configurational space, these cations were randomly rotated, generating over 2500 structural variations, among which approximately 400 exhibited distinct symmetry. Density functional theory (DFT) calculations on these structures revealed that variations in organic A‐cation orientation can induce formation energy and bandgap differences of up to 0.32 and 0.64 eV, respectively. Rather than aiming to reproduce exactly known experimental compounds for each perovskite, we used a unified modeling approach to systematically explore how the size and orientation of organic cations govern lattice distortion and electronic structure in perovskite frameworks. Through rotational screening, a new orthorhombic phase of the well‐known (MA = ) was identified, in which MA cations are aligned along the [102] and [10 directions. Additionally, a novel pseudocubic triclinic perovskite, (TiZ = ), was discovered and validated as a stable perovskite based on its formation energy, bandgap, effective mass, mechanical properties, and dynamic stability.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"151 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731396","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 investigates the potential use of naturally occurring TCNQ and its derivatives as non‐fullerene acceptors (NFAs) in organic solar cells (OSCs). The density functional theory (DFT) and time‐dependent density functional theory (TD‐DFT) calculations at the B3LYP/6–311G (d,p) levels are employed to analyze the electronic structures and optical behaviors of these compounds. The optoelectronic properties of these molecules in chloroform are investigated. Electronic properties, including the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, electronic bandgap ( E g ), quantum chemical parameter, excitation energy ( E x ), maximum wavelength ( λ max ), oscillator strength (ƒ), binding energy ( E b ), open‐circuit voltage ( Voc ) and fill factor ( FF ) of the understudy molecules are calculated. Results indicated that the modification in TCNQ reduces the E g , increasing the λ max and the values of Voc and FF , especially for TCNQ11, which has Voc 1.459 V and FF 91.20 % when combined with donor polymer (DP) P3HT. These promising results underscore the potential of our studied molecules for efficient application in organic solar cells and their significant contribution to the advancement of photovoltaic technology.
{"title":"Computational Evaluation of Tetracyano‐p‐quinodimethane TCNQ Based Derivatives as Non‐Fullerene Acceptors for Organic Solar Cells","authors":"Adeel Mubarik, Faiza Shafiq, Xue‐Hai Ju","doi":"10.1002/adts.202501758","DOIUrl":"https://doi.org/10.1002/adts.202501758","url":null,"abstract":"This study investigates the potential use of naturally occurring TCNQ and its derivatives as non‐fullerene acceptors (NFAs) in organic solar cells (OSCs). The density functional theory (DFT) and time‐dependent density functional theory (TD‐DFT) calculations at the B3LYP/6–311G (d,p) levels are employed to analyze the electronic structures and optical behaviors of these compounds. The optoelectronic properties of these molecules in chloroform are investigated. Electronic properties, including the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, electronic bandgap ( <jats:italic> E <jats:sub>g</jats:sub> </jats:italic> ), quantum chemical parameter, excitation energy ( <jats:italic> E <jats:sub>x</jats:sub> </jats:italic> ), maximum wavelength ( <jats:italic> λ <jats:sub>max</jats:sub> </jats:italic> ), oscillator strength (ƒ), binding energy ( <jats:italic> E <jats:sub>b</jats:sub> </jats:italic> ), open‐circuit voltage ( <jats:italic>V</jats:italic> <jats:sub>oc</jats:sub> ) and fill factor ( <jats:italic>FF</jats:italic> ) of the understudy molecules are calculated. Results indicated that the modification in TCNQ reduces the <jats:italic> E <jats:sub>g</jats:sub> </jats:italic> , increasing the <jats:italic> λ <jats:sub>max</jats:sub> </jats:italic> and the values of <jats:italic>V</jats:italic> <jats:sub>oc</jats:sub> and <jats:italic>FF</jats:italic> , especially for TCNQ11, which has <jats:italic>V</jats:italic> <jats:sub>oc</jats:sub> 1.459 V and <jats:italic>FF</jats:italic> 91.20 % when combined with donor polymer (DP) P3HT. These promising results underscore the potential of our studied molecules for efficient application in organic solar cells and their significant contribution to the advancement of photovoltaic technology.","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":"145731659","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 stability, effectiveness, and versatility of perovskite solar cells (PSCs) can only be improved with advanced solar materials, opening the door for future‐oriented green energy solutions. In this study, eight recently developed anthracene‐based triphenylamine hole transporting layers, HTLs, (PEH‐S1‐PEH‐S8), derived from the PEH‐R core with thiophene and acceptor substitutions, are systematically investigated using DFT and TD‐DFT calculations at B3LYP/6‐31G** level. These HTL materials' optical, electrical, and charge‐transport characteristics are thoroughly evaluated to understand the structure‐property relationship. The PEH‐S7 molecule facilitates the efficient transfer of electronic densities from HOMO to LUMO by elucidating the maximum absorbance at 730 nm, the highest oscillator frequency (f = 1.687), the greatest light harvesting efficacy (LHE = 0.979), and the highest electron affinity (EA = 2.96 eV) in tetrahydrofuran (THF) solvent with the highest open circuit voltage (V oc = 1.38 V). This also showed higher solar efficiency (19.89%) than commercial spiro‐OMeTAD. Comparing PEH‐S1‐PEH‐S8 to the PEH‐R, it is discovered that their electron and hole mobilities are higher. These findings show that the energy levels, reorganization energies, optical and charge‐transport properties of HTL materials can be successfully tuned by strategic peripheral substitution with thiophene and electron‐acceptor groups, thereby guiding the design of future high‐performance PSCs and practical device applications.
{"title":"First‐Principles Exploration of Charge Transfer and Optoelectronic Properties in Anthracene‐Based Hole Transporters for Perovskite Solar Cells: Insights Supported by Device‐Level Simulations","authors":"Sidra Manzoor, Faheem Abbas, Muhammad Ishaq, Gadah Albasher, Faiza Shafiq, Mehvish Perveen, Zainab Asif, Saima Noreen","doi":"10.1002/adts.202501774","DOIUrl":"https://doi.org/10.1002/adts.202501774","url":null,"abstract":"The stability, effectiveness, and versatility of perovskite solar cells (PSCs) can only be improved with advanced solar materials, opening the door for future‐oriented green energy solutions. In this study, eight recently developed anthracene‐based triphenylamine hole transporting layers, HTLs, (PEH‐S1‐PEH‐S8), derived from the PEH‐R core with thiophene and acceptor substitutions, are systematically investigated using DFT and TD‐DFT calculations at B3LYP/6‐31G** level. These HTL materials' optical, electrical, and charge‐transport characteristics are thoroughly evaluated to understand the structure‐property relationship. The PEH‐S7 molecule facilitates the efficient transfer of electronic densities from HOMO to LUMO by elucidating the maximum absorbance at 730 nm, the highest oscillator frequency (f = 1.687), the greatest light harvesting efficacy (LHE = 0.979), and the highest electron affinity (EA = 2.96 eV) in tetrahydrofuran (THF) solvent with the highest open circuit voltage (V <jats:sub>oc</jats:sub> = 1.38 V). This also showed higher solar efficiency (19.89%) than commercial spiro‐OMeTAD. Comparing PEH‐S1‐PEH‐S8 to the PEH‐R, it is discovered that their electron and hole mobilities are higher. These findings show that the energy levels, reorganization energies, optical and charge‐transport properties of HTL materials can be successfully tuned by strategic peripheral substitution with thiophene and electron‐acceptor groups, thereby guiding the design of future high‐performance PSCs and practical device applications.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"159 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731674","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}
Water pollution due to heavy metal contamination has been the most challenging area in recent years. The heavy metals introduced into the water through numerous anthropogenic activities such as industrial and agricultural waste, mining etc., accumulate and will lead to adverse health risks to human and aquatic life. Thus, early and efficient detection of these pollutants in water is a growing demand, for which chemiresistive sensors stand as a promising approach due to their simplicity, accuracy, high sensitivity, and real‐time detection capability. To meet the increasing demand, this work investigates the transition metal (TM = Cu, Ni, and Zn) functionalized graphene for their sensing potential toward the toxic heavy metals: Arsenic (As), Chromium (Cr), Mercury (Hg), and Lead (Pb). This work integrates density functional theory (DFT) with Non‐Equilibrium Green's Function (NEGF) to evaluate the adsorption energetics, electronic, and transport properties. The findings reveal that functionalization of graphene with TM improves the adsorption energetics of heavy metals with significant variations in electronic and transport properties. The computed sensor parameters demonstrate a high sensitivity, ranging from 80% to 254% toward the studied heavy metal, and fast recovery, particularly toward the heavy metal mercury. Thus, the observed findings confirm the suitability of TM functionalized graphene for detecting heavy metal contaminants in water, in a real‐time environment.
{"title":"Suitability of Transition Metal Decorated Graphene for Carcinogenic Water Pollutants Detection: Computational Insight","authors":"Monika Srivastava, Anurag Srivastava","doi":"10.1002/adts.202501751","DOIUrl":"https://doi.org/10.1002/adts.202501751","url":null,"abstract":"Water pollution due to heavy metal contamination has been the most challenging area in recent years. The heavy metals introduced into the water through numerous anthropogenic activities such as industrial and agricultural waste, mining etc., accumulate and will lead to adverse health risks to human and aquatic life. Thus, early and efficient detection of these pollutants in water is a growing demand, for which chemiresistive sensors stand as a promising approach due to their simplicity, accuracy, high sensitivity, and real‐time detection capability. To meet the increasing demand, this work investigates the transition metal (TM = Cu, Ni, and Zn) functionalized graphene for their sensing potential toward the toxic heavy metals: Arsenic (As), Chromium (Cr), Mercury (Hg), and Lead (Pb). This work integrates density functional theory (DFT) with Non‐Equilibrium Green's Function (NEGF) to evaluate the adsorption energetics, electronic, and transport properties. The findings reveal that functionalization of graphene with TM improves the adsorption energetics of heavy metals with significant variations in electronic and transport properties. The computed sensor parameters demonstrate a high sensitivity, ranging from 80% to 254% toward the studied heavy metal, and fast recovery, particularly toward the heavy metal mercury. Thus, the observed findings confirm the suitability of TM functionalized graphene for detecting heavy metal contaminants in water, in a real‐time environment.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731453","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}
Lung cancer, with more than 50 approved drugs, is still the deadliest cancer, with 1.80 million annual deaths, necessitating rapid drug development, which can be accelerated by AI‐driven prediction of potent candidates. In this study, we downloaded the lung cancer BioAssay data from ChEMBL and PubChem and filtered at a 5.0 µ m threshold, yielding 4,537 and 8,661 unique active compounds, respectively, and equal inactive molecules are extracted from the big inactive compound library, totalling 26,396 unique, balanced compounds are taken for descriptor computations with QikProp and AlvaDesc software. Mean imputations and standard scaling with PCA for feature sorting, followed by three Deep Learning Models—Residual Neural Network, Feed Forward Neural Network, and Recurrent Neural Network—with an 80:20 split, 50–100 epochs, Adam optimizer, 0.001 learning rate, 32 batch size, early stopping, and ensembled (majority voting, averaging, and stacking) to enhance robustness, accuracy, generalization, stability, and confidence in predicting Activity scores from 1 to 10. A user interface is built to deploy the trained models (h5) for scoring unlabeled compounds (scores 5–10 as highly active), achieving 0.99–1.0 accuracy and F1 scores. The top predicted compound library is docked (HTVS, SP, XP, MM‐GBSA) against ALK, HSP5, KRas, MMP‐8, and tRNA DHDS2, identifying the top three multitargeted hits (PubChem CIDs: 144074375, 440810382, and 48426893) with docking scores from –10.8 to –5.6 kcal/mol and MM‐GBSA energies from –67.7 to –10.4 kcal/mol. Pharmacokinetics and DFT analyses confirmed the drug‐likeness of the compound, while 5 ns WaterMap simulations revealed implicit water roles in interactions, and 100 ns MD simulations showed deviations and fluctuations within 2 Å, with numerous intermolecular interactions. The entire in‐silico study supported and validated the deep learning predictions, identifying the computational potency of compounds against lung cancer proteins—warranting experimental validation.
{"title":"DrLungker: A Deep Ensemble Learning Framework for Predicting Anti‐Lung Cancer Compound Activity and Validating Multitarget Potency through WaterMap, DFT, MD Simulations, and MM‐GBSA Analysis","authors":"Shaban Ahmad, Khalid Raza","doi":"10.1002/adts.202501550","DOIUrl":"https://doi.org/10.1002/adts.202501550","url":null,"abstract":"Lung cancer, with more than 50 approved drugs, is still the deadliest cancer, with 1.80 million annual deaths, necessitating rapid drug development, which can be accelerated by AI‐driven prediction of potent candidates. In this study, we downloaded the lung cancer BioAssay data from ChEMBL and PubChem and filtered at a 5.0 µ <jats:sc>m</jats:sc> threshold, yielding 4,537 and 8,661 unique active compounds, respectively, and equal inactive molecules are extracted from the big inactive compound library, totalling 26,396 unique, balanced compounds are taken for descriptor computations with QikProp and AlvaDesc software. Mean imputations and standard scaling with PCA for feature sorting, followed by three Deep Learning Models—Residual Neural Network, Feed Forward Neural Network, and Recurrent Neural Network—with an 80:20 split, 50–100 epochs, Adam optimizer, 0.001 learning rate, 32 batch size, early stopping, and ensembled (majority voting, averaging, and stacking) to enhance robustness, accuracy, generalization, stability, and confidence in predicting Activity scores from 1 to 10. A user interface is built to deploy the trained models (h5) for scoring unlabeled compounds (scores 5–10 as highly active), achieving 0.99–1.0 accuracy and F1 scores. The top predicted compound library is docked (HTVS, SP, XP, MM‐GBSA) against ALK, HSP5, KRas, MMP‐8, and tRNA DHDS2, identifying the top three multitargeted hits (PubChem CIDs: 144074375, 440810382, and 48426893) with docking scores from –10.8 to –5.6 kcal/mol and MM‐GBSA energies from –67.7 to –10.4 kcal/mol. Pharmacokinetics and DFT analyses confirmed the drug‐likeness of the compound, while 5 ns WaterMap simulations revealed implicit water roles in interactions, and 100 ns MD simulations showed deviations and fluctuations within 2 Å, with numerous intermolecular interactions. The entire in‐silico study supported and validated the deep learning predictions, identifying the computational potency of compounds against lung cancer proteins—warranting experimental validation.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"112 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731454","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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