Journals
2026 EN
Gkourras Arsenios · Iliopoulos Dimitrios · Gergidis Leonidas N.
+2 more
Abstract The adsorption of methane, ethane, and their equimolar mixture in the mesoporous NIIC‐20‐Bu metal–organic framework (MOF) is investigated utilizing molecular simulations and artificial neural networks. To the best of knowledge, this is the first computational study of small alkanes sorbed in this particular MOF. Grand Canonical Monte Carlo simulations provided the adsorption isotherms of the aforementioned alkanes in NIIC‐20‐Bu at different temperatures. The simulation findings are compared with existing experimental sorption measurements showing reasonable quantitative and qualitative agreement. Predictive models based on artificial neural networks are developed incorporating simulation data and available experimental measurements in the training phase to predict the sorption isotherms of methane, ethane, and their equimolar mixture in NIIC‐20‐Bu mesoporous material with the minimum computational cost. 3D density profiles of sorbed methane and ethane are computed based on their positions in the simulation box of NIIC‐20‐Bu, as obtained from GCMC simulations. Moreover, the analysis of the aforementioned profiles highlighted preferred localization domains, siting motifs and interesting segregation phenomena of the sorbed methane, ethane molecules as pure components or in their equimolar mixture within the mesoporous crystal. The present findings highlight the potential applications of NIIC‐20‐Bu as an efficient adsorbent material.
Journals
2026 EN
Chandra Anirban · Koch Marius · Pawar Suraj
+9 more
Abstract This study aims to develop surrogate models to accelerate decision‐making processes related to porous media flows, using geologic storage of carbon dioxide ( C O 2 $CO_2$ ) as an example. Several engineering problems, including selection of subsurfaceC O 2 $CO_2$ storage sites, often requires costly and complex simulations of flow fields. In this work, a Fourier Neural Operator (FNO) based model is developed for real‐time, high‐resolution simulation ofC O 2 $CO_2$ plume migration. The model is trained on a comprehensive dataset derived from realistic subsurface parameters and achieves a computational speed‐up ofO ( 10 3 to 10 5 ) $(10^3 10^5)$ when compared to numerical simulators used in this work, with only a minimal reduction in predictive accuracy. Super‐resolution experiments are also investigated to reduce the computational cost of training the FNO‐based models. Additionally, various strategies are proposed to enhance the reliability of model predictions, which is crucial for evaluating actual geological storage sites. This framework, based on NVIDIA's PhysicsNeMo library, enables rapid screening of sites for CCS. This work scales data‐driven models to realistic 3D systems that better reflect real‐life subsurface aquifers and reservoirs, paving the way for building next‐generation digital twins for subsurface CCS applications. The workflows and strategies discussed can be easily adapted to other material systems and energy solutions, such as geothermal reservoir modeling, flow batteries, fuel cells, and hydrogen storage.
Journals
2026 EN
Vudumula Keerthana · Yadav Ashish Kumar · Maurya Gyanendra Kumar
+3 more
Abstract Solid‐state batteries offer superior safety, high energy density, and the ability to function effectively across a wide range of temperatures. Sodium‐ion (Na‐ion) solid‐state batteries are a promising alternative to lithium‐ion batteries due to sodium's abundance and low cost. A high‐quality electrode is crucial for achieving high performance in Na‐ion batteries. In this study, structural stability, electronic properties, and performance of functionalized hexagonal boron carbide (BC 3 ) are investigated for ultrathin electrodes using density functional theory (DFT). The effective adsorption of Li, Na, K, and Mg atoms at the BC 3 surface is also investigated. The BC 3 monolayer has a ≈0.8 eV indirect bandgap, which becomes metallic after Na adsorption, making it suitable for electrode applications. Additionally, the Na‐adsorbed BC 3 monolayer shows the lowest adsorption energy (−1.2 eV), which is the most stable lattice structure among others. The Na‐adsorbed BC 3 demonstrated a theoretical capacity of 1152 mAh g −1 , which is comparable with the Li‐adsorbed electrode. Moreover, the Na‐adsorbed BC 3 electrode shows a very small variation (0.18 V) for open circuit voltage (OCV), indicating this electrode is robust in terms of voltage stability. These findings show that the functionalized BC 3 ultrathin electrode is very suitable for the electrode of Na‐ion solid‐state batteries.
Journals
2026 EN
Jin Chengchen · Xiong Kai · Lv Zhongqian
+7 more
Abstract Refractory multi‐principal element alloys (RMPEAs) are promising materials for high‐temperature applications due to their exceptional mechanical stability, yet their vast compositional space poses challenges for efficient design. To address this challenge, this study introduces a novel computational framework integrating high‐throughput Exact Muffin‐Tin Orbitals with Coherent Potential Approximation (EMTO‐CPA) calculations, Copula entropy‐based feature selection, and machine learning (ML) to accelerate RMPEA development. Focusing on V‐Nb‐Ta ternary alloys, a comprehensive dataset of elastic properties for 4485 different compositions is constructed using EMTO‐CPA, achieving accuracy comparable to traditional the special quasi‐random structure (SQS) methods with significantly reduced computational cost. A Random Forest ML model, trained on Copula entropy‐selected features, predicted elastic constants ( C 11 , C 12 , E ) with high accuracy (R 2 > 92%), and C 44 with good accuracy (R 2 ≈ 88%). Experimental synthesis and characterization of selected V‐Nb‐Ta alloys validated the predictions, confirming the trend of elastic modulus with varying Ta content. This integrated approach not only overcomes the limitations of conventional computational methods but also provides a scalable pipeline for designing RMPEAs tailored for extreme environments.
Journals
2026 EN
Palacios Pablo F. · Algora Carlos
Abstract Technology Computer‐Aided Design (TCAD) modeling is a vital tool for the design of complex optoelectronic devices such as III‐V multijunction solar cells. In this work, Bayesian optimization is proposed as a robust framework that is able to tackle difficulties that arise in the optimization of expensive to evaluate black‐box functions, such as TCAD solvers. This method is applied to a lattice‐matched GaInP/Ga(In)As/Ge triple junction solar cell, which incorporates a distributed Bragg reflector for space applications. The results show a path to increase the efficiency of current commercial space triple junction solar cells.
Journals
2026 EN
Krishnan Sangameswaran · Pan Zehua · Zhong Zheng
+1 more
ABSTRACT Protonic ceramic fuel cells (PCFCs) represent a significant advancement in fuel cell technology due to their ability to operate at intermediate temperatures, offering enhanced efficiency and reduced material degradation compared to traditional high‐temperature oxygen ion conducting solid oxide fuel cells (O‐SOFCs). While PCFCs hold immense potential for commercialization, their material innovation remains a critical bottleneck to achieving widespread viability. This comprehensive review explores the synergistic integration of density functional theory (DFT) based calculations with machine learning (ML) methodologies, illuminating their collective impact on accelerating PCFC material development. Combination of DFT's atomic‐level precision in material property prediction with ML's sophisticated predictive algorithms, creates a powerful framework for exploring vast compositional spaces of critical materials, including perovskite oxides, double perovskite oxides, Ruddlesden‐Popper oxides, and other similar systems. The integration not only enhances computational efficiency but also enables the systematic investigation of complex structure–property relationships essential for advancing PCFC technology. The review methodically examines three interconnected themes: First, it delves into the cutting‐edge strategies and material developments that have propelled recent advances in PCFC applications; second, it analyzes DFT's pivotal role in facilitating PCFC progress through accurate atomic‐scale modeling; and third, it elucidates the revolutionary impact of ML integration with DFT methodologies and its implications for PCFC developments. By focusing on seminal contributions within each domain, this work provides a strategic perspective on the convergence of computational chemistry and ML in PCFC's future advancements.
Journals
2026 EN
Kushwaha Aditya · Vardhan Shalini · Singh Ritu Raj
+1 more
Abstract 2D transition metal dichalcogenides (2D TMDs) like WS 2 have shown immense potential for optoelectronic applications but face inherent limitations in spectral range, carrier mobility, and recombination losses. To overcome these challenges, a novel heterostructure combining WS 2 with the semimetal NiTeSe is proposed, leveraging its ultrahigh carrier mobility and near‐zero bandgap for enhanced photodetection. Through first‐principles density functional theory (DFT) calculations and COMSOL Multiphysics simulations, the electronic and optical properties of the NiTeSe–WS 2 heterostructure are systematically investigated. The hybrid system has a Schottky barrier at the interface and a smaller bandgap (0.689 eV in NiTeSe–WS 2 compared to 1.809 eV in pure WS 2 ). This helps separate charges more efficiently and absorb a wider range of light. Optical analyses reveal exceptional performance, including a 48% higher absorption coefficient (2.21 × 10⁵ cm −1 ) and 53% enhanced optical conductivity (3.91 Ω −1 cm −1 ) compared to pristine WS 2 . Device simulations reveal outstanding photoresponse performance, with a peak responsivity of 4.3 × 10 4 A W −1 and an external quantum efficiency of 1.06 × 10 5 %, representing a significant enhancement compared to pristine WS 2 . These results establish the NiTeSe–WS 2 heterostructure as a transformative platform for next‐generation photodetectors, offering unprecedented sensitivity, spectral versatility, and speed for applications in communication, imaging, and sensing technologies.
Journals
2026 EN
Chen Jian · Wei Jianwei · Chen Kexin
+3 more
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 2 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.
Journals
2026 EN
Kumar Kulwinder · Kumar Rajesh · Kumar Ramesh
+2 more
Abstract Ferrimagnetic Heusler alloys show promising applications to thermoelectric and spintronic devices based on anomalous Nernst phenomena led by Berry curvature. The present work shows a computational investigation of the Anomalous Hall Conductivity ( AHC ) and Anomalous Nernst Conductivity ( ANC ) in a ferrimagnetic Cr 2 MnSb Heusler alloy, which exhibits both L2 1 and Xa structures. The spin‐polarized calculations reveal that Cr 2 MnSb is a ferrimagnetic Heusler alloy, with a nearly zero magnetic moment. The nonzero large Berry curvature (Ω z (Å 2 )) along the (001) plane leads to the large AHC value of 567 and 302 S cm −1 for L2 1 and Xa structure, respectively. The ANC value for L2 1 (0.80 Am −1 K −1 ) is significantly greater than Xa structure (0.05 Am −1 K −1 ) at room temperature. This value for L2 1 phase is further enhanced with B2 disorder because of a modification in the overall Berry curvature around the Fermi level. These large values of AHC and ANC in the L2 1 structure may be a consequence of the presence of mirror plane symmetry alongz = 0 $z = 0$ . Therefore, Cr 2 MnSb, a ferrimagnetic material, can be a potential candidate for thermoelectric device applications with and without disorder.
Journals
2026 EN
Barbosa Leonardo S. · Santos Willian O. · Costa Felix S.
+2 more
Abstract Niobium‐based MXenes show promising properties and applications, but have not yet been sufficiently investigated, especially with halogen surface terminations. This study investigates the structural, electronic, optical, vibrational, and thermodynamic properties of the unique trigonalNb 2 CBr 2 ${\rm Nb}_{2}{\rm CBr}_{2}$ (Nb‐MXene) monolayer using the density functional theory (DFT) formalism with the GGA‐PBE functional. The results of the lattice parameters and bond lengths are compared with the theoretical and experimental data for similar structures. The monolayer exhibits structural stability, since the phonon dispersion results do not reveal negative frequencies, with a cohesive energy of 4.36 eV per atom, and a negative formation energy of –3.85 eV, confirming thermodynamic stability. The band structure indicates that Nb‐MXene is a metal with potential applications as a supercapacitor, as well as revealing potential superconductor characteristics. The optical absorption properties reveal that Nb‐MXene is sensitive to the plane of polarization of incident light, absorbs in the visible region (400–700 nm), and has potential applications as a UVC (100–280 nm) optical filter and as an optical fiber sensor. Thermodynamic properties as a function of temperature are calculated up to 1000 K to characterize the stability of Nb‐MXene. Infrared (IR) and Raman spectra are calculated and assigned, serving as a useful theoretical reference for experimental monolayer characterization. The findings suggest that Nb‐MXene is a promising candidate for photonic and biomedical applications.