jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad S. Khorrami | Pawan Goyal | Jaber Rezaei Mianroodi | Bob Svendsen | Peter Benner | Dierk Raabe
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Physics-Encoded Fourier Neural Operator Approach for Surrogate Modeling of Divergence-Free Stress Fields in Solids Divergence (linguistics) ; Operator (biology) ; Fourier transform ; Stress (linguistics) ; Statistical physics ; Physics ; Mathematics ; Applied mathematics ; Computer science ; Biology ; Quantum mechanics Machine Learning in Materials Science
Authors: Hayrullah Gün Kadıoğlu | Aiman Tarıq | Babür Deliktaş | Mustafa Özgür Yaylı
Source: Mechanics Based Design of Structures and Machines (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Investigation of the torsional dynamics of viscoelastic nanorod through Gaussian process-based surrogate modeling and semi-analytical approaches In this study, the torsional vibration behavior of viscoelastic nanorods under elastic boundary conditions is analyzed using nonlocal strain gradient theory. The governing equations are derived and solved using Fourier series and Stokes transforms to obtain a semi-analytical solution. The influence of damping on size-dependent torsional response is examined, with significant effects observed in highly damped systems. Moreover, to overcome the computational cost of the analytical method, a Gaussian Process Regression (GPR) model is proposed as a surrogate for predicting torsional frequencies. The GPR model demonstrates strong predictive capability across varying material and geometric parameters, offering a practical and efficient alternative. To assess the model's robustness, white noise was introduced at 5%, 10%, and 15% levels; the GPR maintained R2 values above 0.96, indicating high accuracy under uncertainty. A Shapley Additive Explanations (SHAP) analysis was also conducted to interpret the model, revealing that viscous damping is the most influential parameter affecting frequency. Overall, the GPR model offers an accurate, noise-resilient, and interpretable approach for analyzing the dynamic behavior of viscoelastic nanorods. Viscoelasticity ; Kriging ; Gaussian process ; Vibration ; Gaussian ; Torsional vibration ; White noise ; Surrogate model Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Force Microscopy Techniques and Applications
Authors: Bockute, Kristina, | Laukaitis, Giedrius,
Source: KTUePubl (Repository of Kaunas University of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A team-based framework for first-year materials physics education / Traditional materials science curricula often prioritize abstract theory over early engagement with applied design challenges. However, recent studies have explored innovative approaches to teaching introductory materials science courses, emphasizing active learning and real-world applications. Project-based learning (PBL) has been implemented in first-year engineering curricula, integrating multidisciplinary projects with traditional lecture content [1]. These projects enable students to apply theoretical concepts to practical challenges and connect abstract concepts with real-world engineering decisions. This study presents a pedagogical model for first-year undergraduate materials physics that integrates active, team-based learning with real-world material selection tasks. Over a half-semester project, student teams analyze applications (e.g., biodegradable packaging, lightweight automotive components) through iterative research, experimental data analysis, and ethical evaluation. The framework employs a structured problem-solving approach, developing through problem identification, experimental validation, and teams’ presentations. This phased method fosters critical thinking, teamwork, and effective technical communication. Key innovations include integrating ethical design principles through case studies on global challenges like material shortage, encouraging participants to balance performance with societal impacts. Failure-driven learning is integrated via reflective reports, promoting resilience and iterative improvement. Authentic assessments, such as peer evaluations and stakeholder pitches, replicate real-world accountability. Data from pre/post surveys indicate that most students improved their ability to connect theoretical concepts with practical constraints, alongside developing stronger collaboration skills. Performance in material selection tasks, assessed through rubric-based grading, also showed notable improvement. This framework illustrates that early exposure to open-ended, ethics-driven design projects strengthens foundational materials science knowledge and enhances professional readiness. Multidisciplinary approach ; Curriculum ; Stakeholder ; Engineering education ; Selection (genetic algorithm) ; Flexibility (engineering) ; Project-based learning ; Educational technology ; Active learning (machine learning) Material Selection and Properties ; Problem and Project Based Learning ; Engineering Education and Curriculum Development
Authors: Njafa, Jean-Pierre Tchapet | Kouam, Steve Cabrel Teguia | Kongo, Patrick Mvoto | Engo, Serge Guy Nana
Source: arXiv (Cornell University) (preprint, 2025-12-04)
Field: Materials Science — Materials Chemistry

From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $ΔE_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection. Intersystem crossing ; Photon upconversion ; Coupling (piping) ; Excited state ; Thermal ; Energy (signal processing) ; Efficient energy use ; Key (lock) Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Advanced Fluorescence Microscopy Techniques
Authors: Njafa, Jean-Pierre Tchapet | Kouam, Steve Cabrel Teguia | Kongo, Patrick Mvoto | Engo, Serge Guy Nana
Source: ArXiv.org (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $ΔE_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection. Intersystem crossing ; Photon upconversion ; Coupling (piping) ; Excited state ; Thermal ; Energy (signal processing) ; Efficient energy use ; Key (lock) Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Advanced Fluorescence Microscopy Techniques
Authors: Pranjal | Gobinda Chandra Mahapatra | Anurag Panda | Sankha Chakrabortty | Shirsendu Banerjee | Amrita Mishra | Ramesh Kumar | Byong‐Hun Jeon | Cecilia Stålsby Lundborg | Suraj K. Tripathy
Source: Journal of environmental chemical engineering (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

AI-driven optimization of the heterogeneous Sono-Fenton process for intensification of bacterial inactivation Process (computing) ; Chemistry ; Fenton reaction ; Chemical engineering ; Environmental chemistry ; Environmental science ; Biochemical engineering ; Computer science ; Biochemistry ; Catalysis ; Engineering Ultrasound and Cavitation Phenomena ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring Technologies
Authors: Yuki Kobayashi | Sanghong Kim | Takuya Nagato | Takuya Oishi | Shota Kato | Manabu Kano
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Mixer-independent soft sensors to predict tablet critical quality attributes in a continuous direct compression process Partial least squares regression ; Process (computing) ; Critical quality attributes ; Compression (physics) ; Regression analysis ; Mixing (physics) ; Quality (philosophy) ; Gaussian process ; Fraction (chemistry) ; Linear regression Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Protein purification and stability
Authors: Fiona Murphy | Marina Navas Bachiller | Deirdre M. D’Arcy | Alessio Benavoli
Source: Journal of the Royal Statistical Society Series C (Applied Statistics) (article, 2025-09-29)
Field: Chemistry — Analytical Chemistry

Gaussian process with dissolution spline kernel for in vitro dissolution testing Abstract In vitro dissolution testing is a critical component in the quality control of manufactured drug products. The f2 statistic is the standard for assessing similarity between two dissolution profiles. However, the f2 statistic has known limitations: it lacks an uncertainty estimate, is a discrete-time metric, and is a biased measure, calculating the differences between profiles at discrete time points. To address these limitations, we propose a Gaussian Process (GP) with a dissolution spline kernel for dissolution profile comparison. The dissolution spline kernel is a new spline kernel using piecewise logistic functions as its feature maps, enabling the GP to capture the expected monotonic increase in dissolution curves. This results in better predictions of dissolution curves. This new GP model reduces bias in the f2 calculation by allowing predictions to be interpolated in time between observed values, and provides uncertainty quantification. We assess the model’s performance through simulations and real datasets, demonstrating its improvement over a previous GP-based model introduced for dissolution testing. We also show that the new model can be adapted to include dissolution-specific covariates. Applying the model to real ibuprofen dissolution data under various conditions, we demonstrate its ability to extrapolate curve shapes across different experimental settings. Dissolution ; Kernel (algebra) ; Piecewise ; Spline (mechanical) ; Gaussian process ; Kernel principal component analysis ; Parametric statistics ; Gaussian Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Junying Zhong | Lei Zhang | Tao Bo
Source: Physical Chemistry Chemical Physics (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Active learning-enhanced neuroevolution potential for predictive modeling of UO <sub>2</sub> thermophysical properties Accurately characterizing the temperature dependence of UO<sub>2</sub> thermal conductivity is crucial for evaluating its performance under nuclear reactor operating conditions. However, experimental measurements are costly, density functional theory (DFT) calculations are constrained by small spatiotemporal scales, and traditional empirical potentials struggle to capture strong anharmonic effects. To this end, we developed a machine-learned neuroevolution potential (NEP) with near-DFT accuracy using an active learning strategy, and we systematically evaluated and cross-validated the thermal conductivity of UO<sub>2</sub> using equilibrium molecular dynamics (EMD), homogeneous nonequilibrium molecular dynamics (HNEMD), and nonequilibrium molecular dynamics (NEMD). The results demonstrate that HNEMD delivers a high signal-to-noise ratio, low uncertainty, and rapid convergence, exhibiting superior computational efficiency and robustness. At 500 K, the spectral phonon mean free path spans approximately one order of magnitude, and heat-transport channel lengths exceeding about 5 µm approach the bulk thermal conductivity limit. In the 800-1500 K range, the NEP reproduces the experimental temperature dependence of UO<sub>2</sub> thermal conductivity, while at lower temperatures (300-800 K), it achieves predictive accuracy comparable to that of DFT+<i>U</i>. Systematic validation of UO<sub>2</sub> fundamental properties including the equation of state, phonon dispersion relations, elastic constants, heat capacity, and linear thermal expansion coefficient demonstrates that the constructed NEP is reliable and broadly applicable. This work provides methodological support for multiscale thermal transport modeling of nuclear fuels and reactor safety assessment. Thermal conductivity ; Non-equilibrium thermodynamics ; Molecular dynamics ; Work (physics) ; Anharmonicity ; Phonon ; Thermal ; Mean free path Nuclear Materials and Properties ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Qingzhen Yang | Yue Shi | Han Zhang | Jianhua Zhang | Haoyuan Li
Source:  (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Intelligent Recommendation and Optimization Algorithms of Experimental Conditions for Robotic Chemists Designing and optimizing experimental conditions is critical for improving material performance and research efficiency. However, traditional trial-and-error methods are ineffective in high-dimensional parameter spaces and lack the ability to perform targeted screening. To address these challenges, we propose an intelligent framework combining HR-CRAG, a retrieval-augmented generation method for initial condition recommendation, and QM-SMBO, a sequential model-based optimization strategy intergrating quantile regression forests with the MeanBAX acquisition function. This framework enables closed-loop integration on automated experimental platforms. Compared with classical RAG, HR-CRAG improves retrieval coverage by 10.9% and semantic matching accuracy by 5%, achieving 89.36% in faithfulness and 83.45% in relevance. In optimization tasks, QM-SMBO attains an $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 9 9}$ across six representative datasets, offering approximately a twofold improvement in optimization performance and nearly a sixfold increase in sampling efficiency over traditional Gaussian processes. Finally, in a case study focused on red photoresist transmittance, the system generated 4860 candidate conditions. With only 40 actual experiments (around $\mathbf{0. 8 \%}$ of the full space), it identified five formulations within the target range of $\mathbf{6 0 \% - 7 0 \%}$ transmittance, with the best reaching 67.42%. These results demonstrate the framework’s efficiency and practicality in guiding performance-oriented experimentation. Design of experiments ; Range (aeronautics) ; Matching (statistics) ; Optimization algorithm ; Kriging ; Automation ; Sampling (signal processing) ; Gaussian process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Xu, Ke | Wang, Gang | Liang, Ting | Xiao, Yang | Ding, Dongliang | Guo, Haichang | Gao, Xiang | Tong, Lei | Wan, Xi | Zhang, Gang | Xu, Jianbin
Source: ArXiv.org (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect transistors. Built upon the neuroevolution potential, the model extends a subset of the OMat24 dataset through an active-learning workflow to generate a chemically diverse, interface-rich reference set. Coupled with the FETMOD structure generator module, NEP-FET can simulate realistic field-effect transistor geometries at sub-micrometer scales containing millions of atoms, and delivers atomistic predictions of temperature fields, per-atom heat flux, and thermal stress in device structures with high fidelity. This framework enables rapid estimation of device-level metrics, including heat-flux density and effective thermal conductivity. Our results reveal pronounced differences in temperature distribution between fin-type and gate-all-around transistor architectures. The framework closes a key gap in multiscale device modeling by combining near-quantum-mechanical accuracy with device-scale throughput, providing a systematic route to explore heat transport and thermo-mechanical coupling in advanced transistors. Transistor ; Coupling (piping) ; Thermal ; Workflow ; Thermal conductivity ; Key (lock) Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Machine Learning in Materials Science
Authors: Xu, Ke | Wang, Gang | Liang, Ting | Xiao, Yang | Ding, Dongliang | Guo, Haichang | Gao, Xiang | Tong, Lei | Wan, Xi | Zhang, Gang | Xu, Jianbin
Source: arXiv (Cornell University) (preprint, 2025-11-24)
Field: Materials Science — Materials Chemistry

Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect transistors. Built upon the neuroevolution potential, the model extends a subset of the OMat24 dataset through an active-learning workflow to generate a chemically diverse, interface-rich reference set. Coupled with the FETMOD structure generator module, NEP-FET can simulate realistic field-effect transistor geometries at sub-micrometer scales containing millions of atoms, and delivers atomistic predictions of temperature fields, per-atom heat flux, and thermal stress in device structures with high fidelity. This framework enables rapid estimation of device-level metrics, including heat-flux density and effective thermal conductivity. Our results reveal pronounced differences in temperature distribution between fin-type and gate-all-around transistor architectures. The framework closes a key gap in multiscale device modeling by combining near-quantum-mechanical accuracy with device-scale throughput, providing a systematic route to explore heat transport and thermo-mechanical coupling in advanced transistors. Transistor ; Coupling (piping) ; Thermal ; Workflow ; Thermal conductivity ; Key (lock) Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Machine Learning in Materials Science
Authors: Jack S. Draney | Athanassios Z. Panagiotopoulos | David B. Graves
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Development and transferability of neural-network models for plasma-surface interactions Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations. Transferability ; Set (abstract data type) ; Interatomic potential ; Field (mathematics) ; Deep learning ; Molecular dynamics ; Diamond ; Development (topology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides