jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shaima Alhosani | Sanan H. Khan | Rauda Saeed | Miyra Aljabri | Wadima Ali | Afsar Husain
Source: Defence Technology (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Ballistic performance of spaced multi-layer aluminum armor under fixed dimensional envelope constraints: FEM simulations with ANN-based design analysis Multi-layered armor is conventionally assumed to outperform monolithic plates of equivalent mass. This study challenges this paradigm for dimensionally constrained systems by investigating AA2024-T3 aluminum configurations where a fixed 1.74 mm envelope is progressively subdivided into two to five layers, necessarily reducing total material mass from 1.64 mm to 1.04 mm as interface count increases. Using validated Abaqus/Explicit simulations with three double-nosed projectiles, we demonstrate that the two-layer configuration (L1) consistently delivers superior ballistic resistance, achieving limits of 45.7–61.4 m/s depending on projectile geometry. Performance systematically degrades with increasing layer count: the five-layer configuration (L4) exhibits 32% lower ballistic limits despite possessing four interfaces. A critical thickness threshold of 0.34–0.51 mm was identified, below which layers fail through low-energy shear rather than energy-intensive plugging. An artificial neural network trained on 249 simulations ( R 2 = 0.98, 0.92, 0.90 for residual velocity, absorbed energy, and impact time) independently confirmed through feature importance analysis that total material mass and individual layer thickness dominate performance over interface count. For thickness-constrained applications, maintaining fewer, thicker layers proves more effective than maximizing interfaces—a finding with direct implications for lightweight armor design in aerospace and defense sectors. Armour ; Projectile ; Envelope (radar) ; Finite element method ; Ballistic limit ; Layer (electronics) ; Ballistic impact ; Residual ; Aerospace High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Energetic Materials and Combustion
Authors: Behzad Hashemi Soudmand | Saad G. Alshammari | Jaber M. Asiri | Fawwaz Hazzazi | Hassan Zohair Hassan | Ghada Atteia | Amirhossein Najafi
Source: Applied Thermal Engineering (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning integrated with finite-element modeling for microstructure-driven thermal conductivity prediction in epoxy/AlN nanocomposites Thermal conductivity ; Finite element method ; Thermal contact conductance ; Nanocomposite ; Thermal ; Polymer nanocomposite ; Percolation (cognitive psychology) ; Gaussian ; Particle (ecology) ; Gaussian process Thermal properties of materials ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Shouwei Gao | Xu Zheng | Dongsheng Luo | Sheng Di | Wenqian Dong
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation The rapid growth of scientific machine learning (SciML) has accelerated discovery across diverse domains, yet designing effective SciML models remains a challenging task. In practice, building such models often requires substantial prior knowledge and manual expertise, particularly in determining which input features to use and how large the model should be. We introduce LUMOS, an end-to-end framework based on L0-regularized learning that unifies feature selection and model pruning to democratize SciML model design. By employing semi-stochastic gating and reparameterization techniques, LUMOS dynamically selects informative features and prunes redundant parameters during training, reducing the reliance on manual tuning while maintaining predictive accuracy. We evaluate LUMOS across 13 diverse SciML workloads, including cosmology and molecular sciences, and demonstrate its effectiveness and generalizability. Experiments on 13 SciML models show that LUMOS achieves 71.45% parameter reduction and a 6.4x inference speedup on average. Furthermore, Distributed Data Parallel (DDP) training on up to eight GPUs confirms the scalability of Scalability ; Workflow ; Pruning ; Feature (linguistics) ; Inference ; Adaptation (eye) ; Speedup ; Feature selection Machine Learning in Materials Science ; Machine Learning and Data Classification ; Domain Adaptation and Few-Shot Learning
Authors: R.J. Gadha | R. D. Eithiraj
Source: Computational Condensed Matter (article, 2026-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ab initio study of the K3FTe anti-perovskite using machine learning Gradient boosting ; Band gap ; Figure of merit ; Ab initio ; Thermoelectric effect ; Boosting (machine learning) ; Antiperovskite ; Electronic band structure Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Dillon G. Frost | Johann Bouchet | Mihai‐Cosmin Marinica | Clovis Lapointe | Jean-Bernard Maillet | Luca Messina
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Fully anharmonic calculations of the free energy of migration of point defects in UO2 and PuO2 Calculating diffusion rates of point defects in materials typically relies on the harmonic approximation to estimate migration free energies. However, anharmonic effects can have a large impact on diffusion properties, and explicitly accounting for them is usually computationally demanding and difficult to achieve in practice. In this work, we investigate the role of anharmonic effects on defect migration in UO2 and PuO2 using the potential of average force integration (PAFI) method. Fully anharmonic migration free energies are computed for several cation and anion defect types, using the Cooper-Rushton-Grimes (CRG) potential and a recently developed machine learning spectral neighbour analysis potential (SNAP) for UO2. Results are systematically compared to harmonic estimates based on attempt frequencies and the Debye approximation. Our results reveal that the validity of the harmonic approximation strongly depends on the defect type and the underlying potential, with significant deviations observed in several cases. In particular, defect migration barriers are found to decrease strongly with increasing temperature (up to 1 eV between 0 and 1200 K), and anharmonic contributions can substantially modify migration entropies and, consequently, diffusion coefficients. Comparing defect migration in UO2 and PuO2 using the CRG potential reveals that PuO2 has lower migration enthalpies at 0~K for all considered defects, but this is compensated by higher attempt frequencies, resulting in similar overall jump frequencies in UO2 and PuO2. These findings provide insight into the limitations of commonly used approximations and highlight the importance of anharmonic effects for predictive modeling of diffusion in nuclear fuels as well as in other classes of materials. Anharmonicity ; Diffusion ; Crystallographic defect ; Harmonic ; Point (geometry) ; Debye ; Jump Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; High Temperature Alloys and Creep
Authors: Martí Raya-Moreno | Buccheri, Alexander | Dasch, Noah Alexy | Farahani, Nasrin | Oliva, Ignacio Gonzalez | Gulans, Andris | Hossain, Manoar | Kleine, Hannah | S. Lübeck | Lubeck, Sven | P. Pavone | Fabian Peschel | Peschel, Fabian | Popova-Gorelova, Daria | Qiao, Lu | Richter, Elias | Rigamonti, Santiago | Pela, Ronaldo Rodrigues | Daniel T. Speckhard | Sinha, Kshitij | D. A. Tumakov | Stutz, Jan | Tillack, Sebastian | Tumakov, Dmitry | Hong, Seokhyun | Užulis, Jānis | Voiculescu, Mara | Cecilia Vona | Mao Yang | Claudia Draxl
Source: arXiv (Cornell University) (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

An exciting approach to theoretical spectroscopy Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a variety of methods that can capture all aspects, from ground-state properties to lattice excitations to different types of light-matter interaction, including time-resolved variants. Modern electronic-structure codes implement either a few or several of these methods. Among them, exciting is an all-electron full-potential package that has a very rich portfolio of all levels of theory, with a particular focus on excitations. It implements the linearized augmented planewave plus local orbital (LAPW+LO) basis, which is known as the gold standard for solving the Kohn-Sham equations of density-functional theory (DFT). Based on this, it also offers benchmark-quality results for a wide range of excited-state methods. In this review, we provide a comprehensive overview of the features implemented in exciting in recent years, accompanied by short summaries on the state of the art of the underlying methodologies. They comprise DFT and time-dependent DFT (TDDFT), density-functional perturbation theory (DFPT) for phonons and electron-phonon coupling, and many-body perturbation theory in terms of the $GW$ approach and the Bethe-Salpeter equation (BSE). Moreover, exciting can handle resonant inelastic x-ray scattering (RIXS), pump-probe spectroscopy as well as exciton-phonon coupling (EXPC). Finally, we cover workflows and a view on data and machine learning (ML). All aspects are demonstrated with examples for scientifically relevant materials. Perturbation theory (quantum mechanics) ; Phonon ; Spectroscopy ; Variety (cybernetics) ; Lattice (music) ; Workflow ; Eigenvalues and eigenvectors ; Coupling (piping) Machine Learning in Materials Science ; Strong Light-Matter Interactions ; 2D Materials and Applications
Authors: Shad Mohammad Shohel | Fahima Lokman Niha | Sheikh Abdul Munim
Source: World Journal of Engineering (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Enhancing the elastic properties of carbon fiber/epoxy composites reinforced with CNT: a finite element and ANN-based approach Purpose The purpose of this study is to evaluate the effective elastic properties of carbon fiber/epoxy by adding carbon nanotubes and understand their mechanical performance using analytical and simulation approaches based on finite element analysis. Design/methodology/approach In Ansys material design modular, 3D RVE is designed to evaluate the effective elastic property of carbon fiber/epoxy by adding carbon nanotubes (CNT). A micromechanical approach has been implemented to validate the simulation-based effective elastic property of carbon fiber/epoxy +CNT. Thereafter, the artificial neural network (ANN) model is trained to predict the elastic properties of carbon fiber/epoxy + CNT, such as longitudinal Young’s modulus, transverse Young’s modulus and shear modulus. For further study, carbon fiber/epoxy + CNT composite laminate is created in Ansys ACP. In Ansys, tensile and bending tests have been performed, and a classical laminate theory (CLT) is developed to validate the simulation result. Findings The study demonstrates that incorporating carbon nanotubes (CNT) into carbon fiber/epoxy composites significantly enhances their elastic properties. In comparison to 1% CNT (Carbon fiber/epoxy + CNT1%), this study showed that 2.5% CNT (Carbon fiber/epoxy + CNT 2.5%) increased the longitudinal Young’s modulus by 8% and the shear modulus by 3%. Moreover, adding carbon nanotube content increased the bending and tensile strength, and the ANN model accurately predicted elastic characteristics with an R-squared of 0.99. Originality/value This study provides a valuable contribution to understanding that adding carbon nanotube to the carbon fiber could enhance mechanical performance through analytical and simulation approaches. By using 3D RVE, micromechanical validation and classical laminate theory, this work investigates how adding carbon nanotube could improve the effective elastic properties of carbon fiber. The work obtains a remarkable R-squared of 0.99 by combining artificial neural networks (ANN) with finite element analysis in a new way that generates accurate predictions of elastic characteristics. Carbon nanotube ; Finite element method ; Ultimate tensile strength ; Bending ; Elastic modulus ; Carbon fibers ; Modulus ; Shear (geology) ; Composite number Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Martí Raya-Moreno | Buccheri, Alexander | Dasch, Noah Alexy | Farahani, Nasrin | Oliva, Ignacio Gonzalez | Gulans, Andris | Hossain, Manoar | Kleine, Hannah | S. Lübeck | Lubeck, Sven | P. Pavone | Fabian Peschel | Peschel, Fabian | Popova-Gorelova, Daria | Qiao, Lu | Richter, Elias | Rigamonti, Santiago | Pela, Ronaldo Rodrigues | Daniel T. Speckhard | Sinha, Kshitij | D. A. Tumakov | Stutz, Jan | Tillack, Sebastian | Tumakov, Dmitry | Hong, Seokhyun | Užulis, Jānis | Voiculescu, Mara | Cecilia Vona | Mao Yang | Claudia Draxl
Source: Open MIND (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

An exciting approach to theoretical spectroscopy Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a variety of methods that can capture all aspects, from ground-state properties to lattice excitations to different types of light-matter interaction, including time-resolved variants. Modern electronic-structure codes implement either a few or several of these methods. Among them, exciting is an all-electron full-potential package that has a very rich portfolio of all levels of theory, with a particular focus on excitations. It implements the linearized augmented planewave plus local orbital (LAPW+LO) basis, which is known as the gold standard for solving the Kohn-Sham equations of density-functional theory (DFT). Based on this, it also offers benchmark-quality results for a wide range of excited-state methods. In this review, we provide a comprehensive overview of the features implemented in exciting in recent years, accompanied by short summaries on the state of the art of the underlying methodologies. They comprise DFT and time-dependent DFT (TDDFT), density-functional perturbation theory (DFPT) for phonons and electron-phonon coupling, and many-body perturbation theory in terms of the $GW$ approach and the Bethe-Salpeter equation (BSE). Moreover, exciting can handle resonant inelastic x-ray scattering (RIXS), pump-probe spectroscopy as well as exciton-phonon coupling (EXPC). Finally, we cover workflows and a view on data and machine learning (ML). All aspects are demonstrated with examples for scientifically relevant materials. Perturbation theory (quantum mechanics) ; Phonon ; Spectroscopy ; Variety (cybernetics) ; Lattice (music) ; Workflow ; Eigenvalues and eigenvectors ; Coupling (piping) Machine Learning in Materials Science ; Strong Light-Matter Interactions ; 2D Materials and Applications
Authors: Pungponhavoan Tep | Marc Bernacki
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based on Partial Differential Equations (PDEs) remain computationally expensive. Our prior Deep Learning (DL) framework using Convolutional Long Short-Term Memory (ConvLSTM) has proven effective in accelerating grain growth prediction, though its applicability was limited to constant-temperature or single-rate thermal profiles. As the model was trained exclusively under constant thermal conditions, it cannot account for the thermal history dependence of grain boundary kinetics, fundamentally limiting its applicability to the time-varying thermal profiles characteristic of industrial heat treatment processes. This study extends the previous framework by incorporating Feature-wise Linear Modulation (FiLM) for thermal conditioning to predict grain growth under complex, time-varying thermal profiles. The model was trained on a large dataset of grain growth evolution under thermal profiles with heating and cooling rates ranging from 0.01 kelvin per second to 10 kelvin per second. The results demonstrate that the proposed thermal conditioning mechanism enables the model to capture the influence of variable thermal profiles on grain boundary migration kinetics. Across the three test scenarios of increasing complexity, the model achieved a Structural Similarity Index Measure (SSIM) of up to 0.93 and mean grain size error below 3.2%. Despite the architectural extensions, inference time remains on the order of seconds per prediction sequence, preserving the computational advantage over PDE-based simulations. Thermal ; Grain growth ; Grain boundary ; Grain size ; Heat transfer ; Thermal resistance ; Deep learning ; Thermal conduction Machine Learning in Materials Science ; Metallurgy and Material Forming ; Solidification and crystal growth phenomena
Authors: Jianfeng Li | Soud Khalil Ibrahim | Ramdevsinh Jhala | Anupama Yadav | Ramachandran T | Aman Shankhyan | Karthikeyan A | Dhirendra Nath Thatoi | Rafid Jihad Albadr | Waam Mohammed Taher | Mariem Alwan | Mahmood Jasem Jawad | Hiba Mushtaq | Samim Sherzod | Mohammad Mahtab Alam
Source: Polymers for Advanced Technologies (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Hybrid Gradient Boosting Decision Tree to Accurately Compute Polyethylene Glycol Polymer Density ABSTRACT Accurate prediction of polyethylene glycol (PEG) density is critical for its growing use as an eco‐friendly solvent in chemical separations and industrial processes, yet experimental measurements are time‐intensive and costly. This study addresses the challenge by developing a hybrid gradient boosting decision tree (GBDT) machine learning model to predict PEG density with high precision. The model's hyperparameters were optimized using four evolutionary algorithms including evolutionary strategies (ES), Bayesian probability improvement (BPI), batch Bayesian optimization (BBO), and self‐adaptive differential evolution (SADE) on a dataset of 293 points compiled from existing literature, with K ‐fold cross‐validation applied to prevent overfitting. Model performance was assessed via optimization runtime and metrics including R ‐squared ( R 2 ), mean squared error (MSE), and average absolute relative error percentage (AARE%). Key findings indicate that temperature has the strongest influence on PEG density (relevance factor: −0.87), followed by weaker correlations with pressure (0.39) and molecular weight (−0.17). The ES algorithm achieved the highest accuracy ( R 2 : 0.996, MSE: 1.149, AARE%: 0.078% on the test dataset), closely followed by BPI ( R 2 : 0.994, MSE: 1.663, AARE%: 0.092%), while SADE performed least effectively ( R 2 : 0.989, MSE: 1.939, AARE%: 0.091%) with the longest runtime (~2000 s). Sensitivity and SHAP analyses confirmed temperature's dominant role. These hybrid models offer a novel, computationally efficient alternative to experimental methods, advancing predictive modeling for PEG's physicochemical properties by integrating evolutionary optimization with GBDT, achieving superior accuracy compared to prior approaches. Differential evolution ; Hyperparameter ; Decision tree ; Sensitivity (control systems) ; Boosting (machine learning) ; Gradient boosting ; Bayesian optimization ; Evolutionary algorithm ; Bayesian probability Machine Learning in Materials Science ; Catalysis for Biomass Conversion ; Phase Equilibria and Thermodynamics
Authors: Enrico Trizio | Giorgia Rossi | Michele Parrinello
Source: The Journal of Chemical Physics (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Ceci n’est pas un committor, yet it samples like one: Efficient sampling via approximated committor functions Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanced sampling approach based on the committor function, machine-learned following a variational principle. This method combines a transition-state-oriented bias potential, expressed as a functional of the committor, with a metadynamics-like bias along a committor-based collective variable, enabling uniform exploration of reaction pathways. In its original formulation, the committor is represented by a neural network that takes physical descriptors as input and is trained by minimizing a functional involving gradients with respect to atomic coordinates, which can be computationally demanding in some cases. Here, we propose a simplified learning criterion formulated entirely in the descriptor space, which bypasses the need for explicit and costly coordinate gradients and provides a relaxed upper bound to the original variational principle. Although this approach does not formally target the exact committor, we show that it retains robust sampling performance while significantly reducing computational costs, thus enabling the study of processes that would be practically unfeasible using the original formulation. Sampling (signal processing) ; Upper and lower bounds ; Artificial neural network ; Event (particle physics) ; Function (biology) ; Importance sampling Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Enrico Trizio | Giorgia Rossi | Michele Parrinello
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Ceci n'est pas un committor, yet it samples like one: efficient sampling via approximated committor functions Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanced sampling approach based on the committor function, machine-learned following a variational principle. This method combines a transition-state-oriented bias potential, expressed as a functional of the committor, with a metadynamics-like bias along a committor-based collective variable, enabling uniform exploration of reaction pathways. In its original formulation, the committor is represented by a neural network that takes physical descriptors as input and is trained by minimizing a functional involving gradients with respect to atomic coordinates, which can be computationally demanding in some cases. Here, we propose a simplified learning criterion formulated entirely in the descriptor space, which bypasses the need for explicit and costly coordinate gradients and provides a relaxed upper bound to the original variational principle. Although this approach does not formally target the exact committor, we show that it retains robust sampling performance while significantly reducing computational costs, thus enabling the study of processes that would be practically unfeasible using the original formulation. Sampling (signal processing) ; Upper and lower bounds ; Artificial neural network ; Event (particle physics) ; Function (biology) ; Importance sampling Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference