jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aniruddha Bora | Isabel K. Alvarez | Julie Chalfant | Chryssostomos Chryssostomidis
Source: ArXiv.org (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a given heat flux in a multilayered metal-oxide-semiconductor field-effect transistor (MOSFET). MOSFETs are integral components of Power Electronic Building Blocks (PEBBs) and experiences the majority of the thermal load. Effective cooling of MOSFETs is therefore essential to prevent overheating and potential burnout. Determining the required velocity for the purpose of effective cooling is of importance but is an ill-posed inverse problem and difficult to solve using traditional methods. MOSFET consists of multiple layers with different thermal conductivities, including aluminum, pyrolytic graphite sheets (PGS), and stainless steel pipes containing flowing water. We propose an algorithm that employs sequential training of the MOSFET layers in PINNs. Mathematically, the sequential training method decouples the optimization of each layer by treating the parameters of other layers as constants during its training phase. This reduces the dimensionality of the optimization landscape, making it easier to find the global minimum for each layer's parameters and avoid poor local minima. Convergence of the PINNs solution to the analytical solution is theoretically analyzed. Finally we show the prediction of our proposed methodology to be in good agreement with experimental results. Coolant ; Artificial neural network ; Heat sink ; MOSFET ; Curse of dimensionality ; Overheating (electricity) ; Thermal ; Heat flux ; Inverse Thermal properties of materials ; Silicon Carbide Semiconductor Technologies ; Heat Transfer and Optimization
Authors: Novana Hutasoit
Source: Materials Letters (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of thermoelectric properties using high-entropy descriptors This study integrates high-entropy (HE)–based descriptors with tree-based machine-learning models to predict electrical conductivity, Seebeck coefficient, and thermal conductivity in multicomponent thermoelectric materials. A curated dataset of 880 compositions was used to train Decision Tree, Random Forest, and Extreme Gradient Boosting (XGB) models across multiple train–test splits. XGB consistently achieved the best performance, with R 2 test values exceeding 0.90 for all target properties. Feature-importance analysis identifies atomic-size mismatch (δ) as the dominant descriptor governing transport behavior, while entropy of mixing (ΔS mix ) and mixing enthalpy (ΔH mix ) contribute secondary, mechanistically consistent effects. The proposed HE–ML framework offers a rapid approach for predicting thermoelectric properties and enabling data-driven compositional screening. • ML with HEA descriptors predicts EC, SC, and TC with high accuracy. • XGB yields the lowest RMSE test and highest R 2 test (>0.9) across all properties. • δ dominates transport behavior, with ΔS mix and ΔH mix providing secondary effects. • HEA–ML framework enables rapid and interpretable screening of TE materials. Thermoelectric effect ; Mixing (physics) ; Gradient boosting ; Boosting (machine learning) ; Thermal conductivity ; Entropy (arrow of time) ; Thermal ; Predictive modelling ; Thermoelectric materials Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Daniel Yoes
Source: Open MIND (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

Harmonic Pattern Discovery Engine: The Key/Lock/Harmony Framework for Systematic Gap Identification in Physical Phenomena Scientific discovery often depends on exploring the right combinations of physical phenomena, material substrates, and control parameters. However, the space of possible combinations vastly exceeds what human researchers can explore, and systematic blind spots — single-variable experimental bias, disciplinary silos, publication bias against negative results, and cognitive limits in high-dimensional reasoning — leave significant regions unexplored. This paper proposes the Harmonic Pattern Discovery Engine: a machine learning system trained on the corpus of verified physical effects, encoded as structured combinations of phenomena (Keys), materials (Locks), and orchestration parameters (Harmony). The system learns an empirical grammar of successful combinations, then identifies gaps — regions where the grammar predicts high-payoff investigation but no experimental data exists. The approach is validated through retrodiction (identifying historical discoveries before their date), near-miss resurfacing (flagging failed experiments close to successes), and variation ranking on known systems. The system outputs prioritized investigation targets filtered by experimental feasibility, enabling efficient allocation of research attention. This is not a replacement for physical theory or a prediction engine for guaranteed discoveries. It is a research-direction tool that exploits exactly the blind spots that limit human exploration. Identification (biology) ; Ranking (information retrieval) ; Limit (mathematics) ; Orchestration ; Exploit ; Physical system ; Generative grammar ; Experimental data Machine Learning in Materials Science ; History and advancements in chemistry ; Scientific Computing and Data Management
Authors: Dmytro Tymoshchuk | Oleh Yasniy | Iryna Didych | Pavlo Orestovych Maruschak | Yuri Lapusta
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Prediction of SMA Hysteresis Behavior: A Deep Learning Approach with Explainable AI This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain ε (%). The data were divided into groups based on the cycle number, ensuring no overlap between samples and correctly assessing the model’s generalization ability. TCN network hyperparameters are optimized using the Hyperband algorithm. The model’s accuracy was evaluated using mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). On the test dataset, the models achieved MSE ≤ 0.0002, MAE ≤ 0.0055, MAPE ≤ 0.0038, and R2 ≥ 0.9997, confirming the high accuracy of reproducing the stress-strain loop shape. The extrapolation ability was evaluated on additional cycles not used during training. For a frequency of 1 Hz, the following results were obtained: for cycle 260, MSE = 0.0001, MAE = 0.0081, MAPE = 0.0032, R2 = 0.9997; for cycle 350, MSE = 0.0004, MAE = 0.0178, MAPE = 0.0073, R2 = 0.9989, for cycle 450 MSE = 0.0017, MAE = 0.0315, MAPE = 0.0124, R2 = 0.9964. This dynamic reflects a physically justified increase in errors with increasing cycle number, due to the accumulation of functional fatigue effects. To interpret the model’s performance, the SHapley Additive exPlanations (SHAP) method was used, which made it possible to quantitatively assess the contributions of input features to the prediction. Stress is the dominant factor, and the significance of the Cycle parameter increases with increasing cycle distance, s consistent with the evolution of the hysteresis loop under the influence of functional fatigue of the material. Mean absolute percentage error ; Mean squared error ; Artificial neural network ; Hyperparameter ; Extrapolation ; Generalization ; Test data Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Elasticity and Material Modeling
Authors: Minhee Kim | Yong Yang
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Quantifying Uncertainty in Void Swelling Prediction: A Conformal Prediction Framework for Reactor Safety Margins Irradiation-induced void swelling is a critical degradation mechanism for structural materials in nuclear reactors, dictating component operational lifespan and safety. While recent machine learning (ML) approaches have improved the accuracy of swelling rate predictions, they often fail to account for the inherent stochasticity of radiation damage, providing point estimates without rigorous uncertainty quantification. This lack of probabilistic context limits their applications in materials qualification, reactor licensing and risk assessment. In this work, we develop a framework that integrates ensemble ML models with Conformal Prediction (CP) to generate statistically calibrated prediction intervals. Unlike standard error estimation or Bayesian methods that often rely on rigid distributional assumptions, this approach specifically addresses the physical heteroscedasticity of swelling data, where variance transitions from the nucleation-dominated incubation regime to the growth-dominated steady-state regime. We demonstrate that log-transformed conformal prediction inference provides valid empirical coverage consistent with target confidence levels even in sparse data regimes. This framework offers a pathway to replace overly conservative upper-bound curves with Probabilistic Risk Assessment (PRA) tools for high-dose reactor core internals. Uncertainty quantification ; Probabilistic logic ; Bayesian probability ; Inference ; Context (archaeology) ; Bayesian inference ; Conformal map ; Point estimation Machine Learning in Materials Science ; Nuclear Materials and Properties ; Model Reduction and Neural Networks
Authors: Said A. Saeid | Makungu M. Madirisha | Bolanle Deborah Ikotun
Source: Materials Circular Economy (article, 2026-03-19)
Field: Materials Science — Ceramics and Composites

Nanomaterials in Geopolymers for Refractory Applications: Trends, Mechanisms, and Challenges Abstract Geopolymer-based refractories enhanced with engineered nanomaterials are emerging as sustainable alternatives to energy-intensive conventional ceramics. Their ability to develop dense, thermally stable aluminosilicate networks at low processing temperatures provides significant reductions in embodied energy and CO 2 emissions while enabling high-temperature performance suitable for demanding industrial environments. This review provides a critical, integrated assessment of how nanosilica, nanoalumina, TiO 2 , ZnO, carbon nanotubes, graphene derivatives, and layered nanoclays modify geopolymer chemistry, microstructure, and thermomechanical behavior. The analysis reveals that nanomaterials act as nucleation centers, crack-bridging reinforcements, pore refiners, and gel-structure stabilizers, leading to enhanced strength retention (70–85% above 1000 − 1200 ο C), reduced thermal shrinkage, improved thermal shock resistance, and refined ITZ densification. The review also evaluates synthesis routes including sol–gel, hydrothermal, co-precipitation, and mechanical milling, and advanced dispersion techniques such as ultrasonication, surfactants, zeta-potential control, and hybrid multi-step strategies, highlighting their influence on nanoparticle reactivity, phase evolution, and long-term stability under cyclic heating. Despite these advances, large-scale implementation remains constrained by nanoparticle agglomeration, precursor variability, curing sensitivities, high cost of nanomaterials, inconsistent mix designs, safety concerns, and the absence of geopolymer-specific high-temperature testing standards. A forward-looking roadmap is developed to bridge these gaps, emphasizing hybrid nanocomposite engineering, AI- and machine-learning-driven mix optimization, digitally controlled additive manufacturing, and comprehensive life-cycle and techno-economic assessments to balance performance benefits with environmental and economic impacts. Collectively, this review establishes the mechanistic foundations, methodological requirements, and industrial considerations necessary to transition nano-enhanced geopolymers from laboratory-scale innovation to reliable, energy-efficient, and industrially viable refractory solutions for next-generation high-temperature applications. Geopolymer ; Nanomaterials ; Aluminosilicate ; Curing (chemistry) ; Nanocomposite ; Nanoparticle Advanced ceramic materials synthesis ; Concrete and Cement Materials Research ; Nuclear materials and radiation effects
Authors: Daniel Yoes
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

Harmonic Pattern Discovery Engine: The Key/Lock/Harmony Framework for Systematic Gap Identification in Physical Phenomena Scientific discovery often depends on exploring the right combinations of physical phenomena, material substrates, and control parameters. However, the space of possible combinations vastly exceeds what human researchers can explore, and systematic blind spots — single-variable experimental bias, disciplinary silos, publication bias against negative results, and cognitive limits in high-dimensional reasoning — leave significant regions unexplored. This paper proposes the Harmonic Pattern Discovery Engine: a machine learning system trained on the corpus of verified physical effects, encoded as structured combinations of phenomena (Keys), materials (Locks), and orchestration parameters (Harmony). The system learns an empirical grammar of successful combinations, then identifies gaps — regions where the grammar predicts high-payoff investigation but no experimental data exists. The approach is validated through retrodiction (identifying historical discoveries before their date), near-miss resurfacing (flagging failed experiments close to successes), and variation ranking on known systems. The system outputs prioritized investigation targets filtered by experimental feasibility, enabling efficient allocation of research attention. This is not a replacement for physical theory or a prediction engine for guaranteed discoveries. It is a research-direction tool that exploits exactly the blind spots that limit human exploration. Identification (biology) ; Ranking (information retrieval) ; Limit (mathematics) ; Orchestration ; Exploit ; Physical system ; Generative grammar ; Experimental data Machine Learning in Materials Science ; History and advancements in chemistry ; Scientific Computing and Data Management
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: The Journal of Physical Chemistry C (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Interatomic Potentials on Zeolite Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the development of multiple new universal IPs that cover wide ranges of the periodic table, their applicability to target chemical systems should be carefully investigated. We benchmark several universal IPs using equilibrium zeolite structures as testbeds by evaluating geometric parameters, energies, and relative stability. We select a diverse set of universal IPs encompassing two major categories: (i) universal analytic IPs, including GFN-FF, UFF, and Dreiding; (ii) pretrained universal machine learning IPs (MLIPs), comprising CHGNet, ORB-v3, MatterSim, eSEN-30M-OAM, PFP-v7, and EquiformerV2-lE4-lF100-S2EFS-OC22. We compare them with established tailor-made IPs, SLC, ClayFF, and BSFF using experimental data and density functional theory (DFT) calculations with dispersion correction as the reference. The tested zeolite structures comprise pure silica frameworks and aluminosilicates containing copper species, potassium, and an organic cation. We found that GFN-FF is the best among the tested universal analytic IPs, but its good performance is limited in silica zeolites without highly strained rings. Some universal MLIPs achieve the energies with a root mean squared error between MLIP and DFT energies below a previously tailor-made MLIP. Among the universal MLIPs, the eSEN-30M-OAM model outperformed the other universal IPs in predicting structures and energies close to experiments and DFT across all zeolite structures studied. These findings show that the modern pretrained universal MLIPs are practical tools in replacing high-throughput DFT calculations against equilibrium zeolite structures, although they inherit the inherent errors of DFT. Zeolite ; Benchmark (surveying) ; Density functional theory ; Benchmarking ; Set (abstract data type) ; Aluminosilicate Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: B. Srinivasa Karthik | J. Rajanithi | Namasivaya Naveen Shanmuga Sundaram | Ragavan Veerarajan
Source: International Journal of Computational Materials Science and Surface Engineering (article, 2026-01-01)
Field: Materials Science — Biomaterials

Machine learning-based fibre diameter prediction using electrospinning parameters Electrospinning is a technique used to create fibres, where the electrical force is applied to the needle so that the polymer solution flow is converted to thread fibres and collected in a flat collector. These fibre sizes typically range from a few micrometers to tens of nanometers. Electrospinning is widely used in biomedical applications, including drug delivery systems, tissue engineering, and wound dressings. These electrospun fibres are used in textiles and garments as they offer improved comfort, moisture control, and breathability. Additionally, fibres can be functionalised to give fabrics qualities like antibacterial characteristics, water resistance, and UV protection. Currently, the fibres produced in this technique involve various parameter tuning to get the desired fibre diameter. In this work, we employed machine learning algorithms like support vector machines (SVMs), random forests, XGBoost, gradient boosting regressors, and k nearest neighbour to better predict the fibre diameter using the various electrospinning parameters. Comparing these model algorithms, we found that the Gradient Boosting algorithm gives the best accuracy of 94.8% and minimum error. Electrospinning ; Thread (computing) ; Gradient boosting ; Polymer ; Boosting (machine learning) ; Yarn Electrospun Nanofibers in Biomedical Applications ; Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials
Authors: Elisa Roldan | Kirstie Andrews | Stephen M. Richardson | Reyhaneh Fatahian | Glen Cooper | Rasool Erfani | Tasneem Sabir | Neil D. Reeves
Source: Open MIND (preprint, 2026-01-08)
Field: Materials Science — Biomaterials

FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions Electrospinning is a scalable technique for producing fibrous scaffolds with tunable micro- and nanoscale architectures for applications in tissue engineering, drug delivery, and wound care. While machine learning (ML) has been used to support electrospinning process optimisation, most existing approaches predict only mean fibre diameters, neglecting the full diameter distribution that governs scaffold performance. This work presents FibreCastML, an open, distribution-aware ML framework that predicts complete fibre diameter spectra from routinely reported electrospinning parameters and provides interpretable insights into process structure relationships. A meta-dataset comprising 68538 individual fibre diameter measurements extracted from 1778 studies across 16 biomedical polymers was curated. Six standard processing parameters, namely solution concentration, applied voltage, flow rate, tip to collector distance, needle diameter, and collector rotation speed, were used to train seven ML models using nested cross validation with leave one study out external folds. Model interpretability was achieved using variable importance analysis, SHapley Additive exPlanations, correlation matrices, and three dimensional parameter maps. Non linear models consistently outperformed linear baselines, achieving coefficients of determination above 0.91 for several widely used polymers. Solution concentration emerged as the dominant global driver of fibre diameter distributions. Experimental validation across different electrospinning systems demonstrated close agreement between predicted and measured distributions. FibreCastML enables more reproducible and data driven optimisation of electrospun scaffold architectures. Electrospinning ; Interpretability ; Scalability ; Process (computing) ; Fiber ; Nanoscopic scale ; Scaffold ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science
Authors: Amin Sakic | Ronald Schnitzer | David Holec
Source: arXiv (Cornell University) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe$_3$C interfaces Solute segregation to interfaces significantly impacts material behavior. A large majority of theoretical works focus on grain boundaries and coherent interfaces. Studies on semi-coherent interfaces are usually prohibited by the structural complexity, yielding models beyond the practical capability of density functional theory (DFT), or chemical complexity, restricted by the availability of (classical) interatomic potentials. This work investigates solute segregation to the coherent and semi-coherent $α$-Fe/Fe$_3$C interface in pearlite and its effect on mechanical properties using novel universal machine learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and changes in cohesion at the coherent interface are used to benchmark several state-of-the-art uMLIPs. We find that the GRACE-2L-OAM and GRACE-2L-OMAT models most accurately reproduce the quantum-mechanical predictions. While Cu has the strongest segregation energy of $\approx$ -0.3 eV to the coherent interface among the investigated tramp and trace elements, all of them, As, Cr, Cu, Mo, Ni, P, Sb, and Sn, exhibit significantly more negative segregation values reaching below $\approx$ -1.5 eV in the presence of the misfit dislocation at the semi-coherent interface. The deepest traps are identified in the vicinity of the dislocation core, although the spatial distribution of segregation energies differs markedly among the solute species. The cohesion of the coherent interface is strongly reduced by Sb, Sn, P, and As, and only mildly by Cu, whereas Ni shows a negligible effect, and Cr and Mo slightly enhance cohesion. In contrast, all investigated solutes (except for P) tend to embrittle the semi-coherent interface, with Sn and, especially, Sb having the strongest impact in tensile tests performed in the out-of-plane direction. Abstract shortened for ArXiv. Grain boundary ; Dislocation ; Work (physics) ; Cohesion (chemistry) ; Density functional theory ; Coherent potential approximation Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels