jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: João Rafael Prudêncio dos Santos | Andréia Márcia Santos de Souza David | Hemilly Kariny Cardoso Freitas | Michelle de Oliveira Santos
Source: Journal of Experimental Agriculture International (article, 2026-01-08)
Field: Chemistry — Analytical Chemistry

Seed Quality from a New Perspective: A Review on Artificial Intelligence and Multispectral Imaging with VideometerLab Seed quality evaluation is undergoing a rapid transition from sample-based, time-intensive assays toward data-rich, non-destructive phenotyping capable of supporting faster, more objective decisions in modern seed systems. Multispectral imaging (MSI) provides a practical bridge between conventional visual inspection and chemical sensing by capturing spatial features together with wavelength-dependent reflectance and fluorescence responses from individual seeds. When integrated with artificial intelligence (AI), MSI enables automated classification, prediction, and localisation of quality-related traits, including viability and vigour differences, varietal purity signals, mechanical or processing damage, dormancy-linked hard seeds, stress history, and visible indicators of seed-borne health problems. This review synthesises recent advances in AI-driven MSI for seed quality assessment, with particular emphasis on standardised workflows enabled by VideometerLab platforms. Key elements covered include principles of MSI acquisition and calibration, segmentation and feature extraction, classical machine learning and ensemble approaches, deep learning for spatial–spectral representation learning, and explainable or human-in-the-loop methods that increase transparency and usability. The study discussed how model performance depends strongly on label quality, lot-wise validation, and robustness to domain shift caused by cultivar, season, moisture, and instrument variability. Practical deployment pathways are highlighted, positioning MSI+AI as a high-throughput screening layer that can prioritise confirmatory testing, reduce destructive sampling, and improve traceability in seed quality management. Finally, the review outlines future directions, including multimodal fusion with X-ray or spectroscopy, self-supervised learning for limited labels, and governance strategies for long-term model maintenance in operational seed laboratories. Multispectral image ; Robustness (evolution) ; Hyperspectral imaging ; Traceability ; Segmentation ; Workflow ; Software deployment ; Fidelity Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Seed Germination and Physiology
Authors: Zhicheng Li | Xingsong Jiang | Lijun Zhang
Source: Advanced Powder Materials (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Progress and opportunities for AI in additively manufactured NiTi-based shape memory alloys Additively manufactured NiTi-based shape memory alloys (SMAs) offer transformative potential for aerospace and biomedical devices, combining unique functional properties with unparalleled design freedom. However, the intrinsic multi-physics complexity of additive manufacturing often leads to inconsistent microstructures, phase transformation behaviors, and thermomechanical properties across different batches and equipment, hindering reliable applications. This review comprehensively examines how artificial intelligence (AI) is overcoming these barriers. We analyze the usage of machine learning for optimizing process parameters, predicting phase transformation temperatures, and migrating defect mitigation. Furthermore, we explore AI-driven strategies for alloy design, highlighting how transfer learning leverages conventional processing data to accelerate the development of novel NiTi-based compositions with tailored transformation temperatures and functional characteristics. The discussion also covers AI-enabled multiscale structure optimization, including the use of computer vision and generative modeling to design complex lattice architectures with programmable and application-specific mechanical responses. Finally, we identify emerging opportunities, including multi-source data fusion, CALPHAD-integrated thermodynamic databases, and large language models, which promise to revolutionize the development paradigm for additively manufactured NiTi SMAs. These AI-enhanced approaches collectively address the critical challenges in transitioning laboratory innovations to industrial-scale applications while enabling unprecedented control over process-structure-property relationships in the multifunctional NiTi-based alloys. This review shows how artificial intelligence is accelerating the development of additively manufactured NiTi shape memory alloys. By optimizing processing, guiding alloy design, and enabling multiscale structural control, AI overcomes major challenges in consistency and performance, paving the way for reliable, high-precision NiTi components in aerospace and biomedical applications. Shape-memory alloy ; Aerospace ; Transformative learning ; Consistency (knowledge bases) ; Process (computing) ; Generative grammar ; Nickel titanium Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Shun Muroga | Hideaki Nakajima | Taiyo Shimizu | Kazufumi Kobashi | Kenji Hata
Source: Open MIND (preprint, 2026-01-31)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a general strategy for data-driven, explainable characterization of complex materials. Intersection (aeronautics) ; Cluster analysis ; Visualization ; Surface (topology) ; Artificial neural network ; Pattern recognition (psychology) ; Unsupervised learning ; Feature (linguistics) ; Feature extraction Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Zengrui Yi | Hua Meng | Zhengchun Zhou
Source: IEEE Transactions on Power Electronics (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Magnetic Core Loss Modeling with Multiple Variables Crossing Materials The generalized modeling of core loss aims to predict the power loss of magnetic components, which is crucial in fields such as electronic industry design. However, the influence relationships of operating condition variables are complex. Empirical models, such as the Steinmetz Equation, cannot fully characterise such nonlinear relationships, because the used priori equation has insufficient variables and their coefficients are all fixed. Although deep learning alleviates this issue, most existing methods still only consider limited variables and are not applicable to cross-material modeling. Therefore, we combine the prior knowledge of empirical models and the powerful fitting ability of deep learning, and propose a Deep Self-Representation (DSR) module that can either function as an independent model or be integrated into existing empirical formulas. Specifically, the integrated model can dynamically adjust the coefficients according to the input variables, resulting in better accuracy and generalization. In addition, based on the log-linear property provided by Steinmetz, we propose a flexible approach that can conveniently add more variables. To evaluate the effectiveness of the proposed method, we conducted a series of comparative and simulation experiments on the open-source dataset MagNet. The experimental results show that DSR, used alone as the core loss model, outperforms baseline methods in accuracy, with 0.875% and 1.229% errors in single-material and multi-material cases, respectively. Furthermore, the integrated model has even lower errors of 0.657% and 1.01% and better robustness. Moreover, our model can perform training and prediction on an inexpensive GPUor CPU, providing an efficient solution. We release our code online: https://github.com/. A priori and a posteriori ; Property (philosophy) ; Nonlinear system ; Core (optical fiber) ; Function (biology) ; Code (set theory) ; Power (physics) ; Series (stratigraphy) ; Variable (mathematics) Magnetic Properties and Applications ; Magnetic properties of thin films ; Electric Motor Design and Analysis
Authors: Lucas B. Pena | Felipe V. Calderan | Priscilla Felício-Sousa | Karla F. Andriani | Marcos G. Quiles | Juarez L. F. Da Silva | Breno R. L. Galvão
Source: ACS Omega (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters Efficient catalytic processes are crucial for converting pollutant molecules into valuable products. Transition-metal nanoclusters show promise as a result of their tunable properties, but identifying active catalysts requires costly studies of the adsorption energetics. Machine learning offers a faster alternative, predicting adsorption energies when trained on suitable descriptors and relatively large density functional theory (DFT) data sets. This study evaluates the predictive power and transferability of two structural descriptors, the Coulomb matrix and the many-body tensor representation, on a diverse nanocluster-adsorbate data set using the random forest regression algorithm. Both descriptors achieved a mean absolute error of 0.05 eV in test data, but performance dropped significantly on an external generated set with unprecedented examples. Adding a simple electronic feature, the number of unpaired electrons of the adsorbate improved generalizability, even though with higher mean absolute errors compared to the original data set, highlighting the dependence on the training data. Nanoclusters ; Density functional theory ; Random forest ; Adsorption ; Test set ; Molecule ; Tensor (intrinsic definition) ; Molecular descriptor ; Transition metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanocluster Synthesis and Applications
Authors: Denis Mušić | Bálint Hajas | P.H. Mayrhofer | Sana Khayyamifar | Grzegorz Artur Sadowski | Johan Hektor | Pär A.T. Olsson
Source: Journal of Physics Condensed Matter (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Elastic properties of amorphous TiNiSn The elastic properties of amorphous TiNiSn, a promising half-Heusler system for flexible and wearable devices, were investigated using experimental and theoretical methods. Nanoindentation measurements performed on amorphous TiNiSn thin film grown by magnetron sputtering yielded an elastic (Young's) modulus value of 132 GPa. To corroborate this result, density functional theory (DFT) calculations and two machine learning models were employed, where the latter were trained on available literature data. The DFT-derived elastic modulus of amorphous TiNiSn is 113 GPa (stress-free conditions), which is 15% lower than the experimental value. However, when hydrostatic stress is considered, arising from possible thermal loads and ion bombardment during thin film synthesis, the difference is reduced to 5%. Electronic structure analysis reveals that amorphous TiNiSn exhibits predominantly covalent bonding with a minor metallic contribution, which is consistent with the measured elastic modulus. Although both machine learning models underestimate the experimental modulus more than DFT, the theoretical results enhance understanding of the elastic behaviour of amorphous TiNiSn and highlight its potential for future applications in flexible microelectronic systems. Nanoindentation ; Amorphous solid ; Elastic modulus ; Sputter deposition ; Thin film ; Hydrostatic pressure ; Amorphous metal ; Sputtering Shape Memory Alloy Transformations ; Heusler alloys: electronic and magnetic properties ; Metallic Glasses and Amorphous Alloys
Authors: Abdulrahman Y. Zamani | Barbaro Zulueta | Andrew Ricciuti | John A. Keith | Kevin Carter-Fenk
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Kohn-Sham density encoding rescues coupled cluster theory for strongly correlated molecules Coupled cluster theory with a Kohn-Sham reference (KS-CC) can dramatically outperform its Hartree-Fock counterpart for strongly correlated systems, but the origin of these improvements has remained unclear. Here we demonstrate that these improvements arise from differences in the one-particle density matrix that are encoded into the non-canonical Fock matrix and not from the nature of the KS orbitals, as is commonly assumed. Equipped with this insight, KS-CCSD(T) can be leveraged to achieve near-chemical-accuracy for electronic and thermochemical properties of transition-metal dimers and main-group compounds. Most strikingly, KS-CCSD(T) qualitatively recovers the entire Cr$_2$ potential energy surface, a notorious failure case for HF-CCSD(T) and single-reference density functional theory. We further introduce a density difference diagnostic that identifies multireference character and guides practitioners toward rational selections of optimal references at mean-field cost. These results establish KS-CCSD(T) as a practical route to treat strong correlation within the "gold standard" framework, and this has immediate implications for machine learning potential development and materials research, areas that heavily rely on KS-DFT for model-parameter fitting. Density functional theory ; Cluster (spacecraft) ; Character (mathematics) ; Density matrix ; Coupled cluster ; Energy (signal processing) ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Claudia Barile | Vimalathithan Paramsamy Kannan
Source: Procedia Structural Integrity (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Fibre-Reinforced Self-Healing Composites: Mechanical Characterisation using Acoustic Emission Technique The autonomous healing of polymeric matrices in the fibre-reinforced composites has received significant attention in recent years. This research investigates the ability of an intrinsically healable polymer resin to heal internal defects in a fibre-dense composite. Unstable internal defects are created in a self-healing Carbon Fibre-Reinforced Polymer composites (CFRPs) using a three-point bending setup. Mild heat is applied as an external stimulus to heal the damaged composites. Mechanical performance recovery is evaluated by testing the virgin, damaged, and healed composites under flexural loads. The test results reveal a marginal recovery of flexural properties after healing. The damage progression in the composites, as well as the rationale behind this marginal recovery of the flexural properties are evaluated using Acoustic Emission (AE) tests. Machine Learning (ML)-based unsupervised data clustering and Continuous Wavelet Transform (CWT) are employed to analyse the parameter-based and signal-based AE data, respectively. The results are further validated using microscopic analysis. These results suggest that the healing process has altered the damage progression path in the self-healing CFRPs and is responsible for the recovery of flexural properties. Flexural strength ; Acoustic emission ; Bending ; Polymer ; Three point flexural test ; Flexural rigidity Polymer composites and self-healing ; Smart Materials for Construction ; Fiber-reinforced polymer composites
Authors: Karmen Čondić‐Jurkić | Irfan Alibay | Woody Sherman | Mallory R. Tollefson | W. Patrick Walters | Zachary Baker | Lillian Chong | Jennifer N. Wei | Jeffrey J. Gray | Brian D. Weitzner | Julia Koehler Leman | Daniel G. A. Smith | Chris Bahl | David Mobley
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

The growing role of open source software in molecular modeling The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence. Additionally, it results in taxpayer-funded research being left siloed in commercial tools where it cannot have as much impact as if it were returned to the general public. Software ; Limit (mathematics) ; Open source ; Open source software ; Data modeling ; Software system ; Architecture Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Abdulrahman Y. Zamani | Barbaro Zulueta | Andrew Ricciuti | John A. Keith | Kevin Carter-Fenk
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Kohn-Sham density encoding rescues coupled cluster theory for strongly correlated molecules Coupled cluster theory with a Kohn-Sham reference (KS-CC) can dramatically outperform its Hartree-Fock counterpart for strongly correlated systems, but the origin of these improvements has remained unclear. Here we demonstrate that these improvements arise from differences in the one-particle density matrix that are encoded into the non-canonical Fock matrix and not from the nature of the KS orbitals, as is commonly assumed. Equipped with this insight, KS-CCSD(T) can be leveraged to achieve near-chemical-accuracy for electronic and thermochemical properties of transition-metal dimers and main-group compounds. Most strikingly, KS-CCSD(T) qualitatively recovers the entire Cr$_2$ potential energy surface, a notorious failure case for HF-CCSD(T) and single-reference density functional theory. We further introduce a density difference diagnostic that identifies multireference character and guides practitioners toward rational selections of optimal references at mean-field cost. These results establish KS-CCSD(T) as a practical route to treat strong correlation within the "gold standard" framework, and this has immediate implications for machine learning potential development and materials research, areas that heavily rely on KS-DFT for model-parameter fitting. Density functional theory ; Cluster (spacecraft) ; Character (mathematics) ; Density matrix ; Coupled cluster ; Energy (signal processing) ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Wuqi Gao | Yinan Ren | Zhidong Yang
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Creep Prediction of Fluoroplastics Based on Physics Informed Neural Networks Fluoroplastics are key sealing materials in rocket engines. They can easily fail due to creep during long-term storage, which may threaten the engines’ safety during operation. Accurate prediction of their time-dependent deformation under varying temperature and pressure conditions remains a critical challenge. Existing methods for predicting polymer creep, such as empirical methods and physically-based constitutive models, often depend on fixed working conditions and require material composition analysis. To address these issues, this paper proposes a physics-informed two-stage framework for predicting the extrapolative creep behavior of fluoroplastics. An Extended Burgers Model (EBM) with adaptive parameters is employed to represent viscoelastic behavior under coupled temperature–pressure variations. A hybrid PSO–NLS strategy is developed to robustly identify EBM parameters from 18 experimental creep curves, overcoming initialization sensitivity and ensuring high-quality training labels. Subsequently, a neural network learns the mapping from service conditions (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T,F</i>) to EBM parameters, guided by material-specific physical constraints that enforce long-term consistency and extrapolation capabilities. The resulting model generates full creep strain curves ε(<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i>) that are both data-driven and physically plausible. Experimental results demonstrate that the proposed method surpasses conventional empirical models in extrapolation accuracy, adherence to physical principles, and ease of practical implementation. It combines the strong fitting ability of data-driven models with the interpretability of physics-based models to improve both prediction accuracy and generalization performance. Creep ; Extrapolation ; Interpretability ; Artificial neural network ; Consistency (knowledge bases) ; Initialization ; Generalization ; Viscoplasticity ; Overfitting Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Mingxuan Deng | Yang Yu | Yunhao Wang | Zhuangzhuang Ma | Linyuan Lu | Tianhao Rui | Yulin Lan | Jiajun Linghu | Nannan Han | Yiyan Li | Zhipeng Li | Haibin Zhang
Source: Crystals (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Screening of Perovskite Cathodes for Low-Temperature Solid Oxide Fuel Cell Operation The discovery of cathode materials that simultaneously exhibit high oxygen-reduction activity, robust stability, and low cost is pivotal to moving solid oxide fuel cells (SOFCs) from the laboratory into commercial deployment. To address this challenge, we compile the largest perovskite dataset to date parameterized by the oxygen tracer surface exchange coefficient (k*). Using only readily obtainable elemental and structural descriptors, we develop machine-learning models that surpass existing approaches in both accuracy and computational efficiency. Specifically, by integrating Mahalanobis-distance-based applicability-domain analysis with random forest-enhanced property descriptors and support vector regression, we high-throughput-screen 1.3 million ABO3 compositions and curate a candidate list that balances thermodynamic stability, cost, and oxygen-reduction activity. Beyond prediction accuracy, SHAP interpretation reveals strong physical correlations between the enhanced descriptors and k*, highlighting the coefficient of thermal expansion, O p-band center, and A-site ionic radius as the dominant factors governing oxygen exchange kinetics. Finally, we identify 209 promising perovskite cathodes predicted to outperform LSC in the low-temperature regime, offering promising directions for experimental realization of practical low-temperature SOFCs. Perovskite (structure) ; Oxide ; Cathode ; Solid oxide fuel cell ; Realization (probability) ; Component (thermodynamics) ; Fuel cells ; Thermal Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion