jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hadi Bakhshan | Sima Farshbaf | Junior Ramirez Machado | Fernando Rastellini Canela | Josep Maria Carbonell
Source: Open MIND (preprint, 2026-02-01)
Field: Materials Science — Materials Chemistry

AI Meets Plasticity: A Comprehensive Survey Artificial intelligence (AI) is rapidly emerging as a new paradigm of scientific discovery, namely data-driven science, across nearly all scientific disciplines. In materials science and engineering, AI has already begun to exert a transformative influence, making it both timely and necessary to examine its interaction with materials plasticity. In this study, we present a holistic survey of the convergence between AI and plasticity, highlighting state-of-the-art AI methodologies employed to discover, construct surrogate models for, and emulate the plastic behavior of materials. From a materials science perspective, we examine cause-and-effect relationships governing plastic deformation, including microstructural characterization and macroscopic responses described through plasticity constitutive models. From the perspective of AI methodology, we review a broad spectrum of applied approaches, ranging from frequentist techniques such as classical machine learning (ML), deep learning (DL), and physics-informed models to probabilistic frameworks that incorporate uncertainty quantification and generative AI methods. These data-driven approaches are discussed in the context of materials characterization and plasticity-related applications. The primary objective of this survey is to develop a comprehensive and well-organized taxonomy grounded in AI methodologies, with particular emphasis on distinguishing critical aspects of these techniques, including model architectures, data requirements, and predictive performance within the specific domain of materials plasticity. By doing so, this work aims to provide a clear road map for researchers and practitioners in the materials community, while offering deeper physical insight and intuition into the role of AI in advancing materials plasticity and characterization, an area of growing importance in the emerging AI-driven era. Transformative learning ; Generative grammar ; Context (archaeology) ; Probabilistic logic ; Intuition ; Taxonomy (biology) ; Deep learning ; Big data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Yesen Zhu | Yang Zhang | Xuan Zhao | Meilun Zhang | Ke Wang | Hua Cai | Xiang Li | Kaiming Li | Hui Liu
Source: Journal of Non-Crystalline Solids (article, 2026-04-06)
Field: Materials Science — Ceramics and Composites

A high-throughput experiment and machine learning-integrated approach for rapid delineation of the glass-forming range: a case study of the B2O3–La2O3–BaO system • High-throughput experiments combined with machine learning for glass-forming region mapping. • RF model accurately predicts glass-forming region with small datasets. • Experimental validation confirms ±2 mol% boundary prediction reliability. • SHAP analysis reveals opposite roles of B₂O₃ and La₂O₃ in glass formation. • A data-driven strategy replaces trial-and-error experiments to support glass composition design. Accurate and efficient determination of the glass-forming region is a fundamental prerequisite for the design and performance optimization of novel glass systems. However, conventional trial-and-error approaches remain time-consuming and costly. In this work, a rapid method for identifying glass-forming regions is developed by integrating high-throughput experiments with machine learning, and the approach is validated using the well-studied B₂O₃–La₂O₃–BaO ternary system. High-throughput melt-quenching experiments were first employed to identify the approximate glass-forming region, and the resulting experimental data were used as a training dataset to develop six machine learning classification models, including Support Vector Machine, Random Forest, Extreme Gradient Boosting, Gaussian Process Classification, Artificial Neural Network, and K-nearest Neighbors. A systematic comparison of model performance indicates that the random forest model exhibits the best overall predictive capability. To assess the reliability of the predicted glass-forming boundary, twenty compositions located within ±2 mol% of the predicted boundary were selected for experimental validation, showing good agreement between model predictions and experimental results and confirming the effectiveness of the proposed method for rapid delineation of glass-forming regions. SHapley Additive exPlanations (SHAP) interpretability analysis was further employed to interpret the prediction results of the random forest model. The results indicate that La₂O₃ exerts a strong inhibitory effect on glass formation, whereas B₂O₃ shows a pronounced promoting effect. This study not only provides a reliable reference for compositional design in the B₂O₃–La₂O₃–BaO system, but also demonstrates the potential of data-driven approaches to accelerate the exploration and development of complex glass materials. Automation ; Feature (linguistics) ; Identification (biology) ; Noise (video) Glass properties and applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Hadi Bakhshan | Sima Farshbaf | Junior Ramirez Machado | Fernando Rastellini Canela | Josep Maria Carbonell
Source: ArXiv.org (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

AI Meets Plasticity: A Comprehensive Survey Artificial intelligence (AI) is rapidly emerging as a new paradigm of scientific discovery, namely data-driven science, across nearly all scientific disciplines. In materials science and engineering, AI has already begun to exert a transformative influence, making it both timely and necessary to examine its interaction with materials plasticity. In this study, we present a holistic survey of the convergence between AI and plasticity, highlighting state-of-the-art AI methodologies employed to discover, construct surrogate models for, and emulate the plastic behavior of materials. From a materials science perspective, we examine cause-and-effect relationships governing plastic deformation, including microstructural characterization and macroscopic responses described through plasticity constitutive models. From the perspective of AI methodology, we review a broad spectrum of applied approaches, ranging from frequentist techniques such as classical machine learning (ML), deep learning (DL), and physics-informed models to probabilistic frameworks that incorporate uncertainty quantification and generative AI methods. These data-driven approaches are discussed in the context of materials characterization and plasticity-related applications. The primary objective of this survey is to develop a comprehensive and well-organized taxonomy grounded in AI methodologies, with particular emphasis on distinguishing critical aspects of these techniques, including model architectures, data requirements, and predictive performance within the specific domain of materials plasticity. By doing so, this work aims to provide a clear road map for researchers and practitioners in the materials community, while offering deeper physical insight and intuition into the role of AI in advancing materials plasticity and characterization, an area of growing importance in the emerging AI-driven era. Transformative learning ; Generative grammar ; Context (archaeology) ; Probabilistic logic ; Intuition ; Taxonomy (biology) ; Deep learning ; Big data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Chandra Bhusan Chowdhury
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Bayesian Optimization in the Analysis of Van der Waals Bilayers ABSTRACT Van der Waals homobilayers, comprising two identical layers of a 2D material, are significant due to their tunable electronic properties and potential applications in advanced electronic and optoelectronic devices, such as transistors, photodetectors, and solar cells. This study utilizes machine learning (ML) and optimization methods to predict the bandgaps of these homobilayers. The Extra Tree Regressor (ETR), which features 48 attributes, provides reasonable accuracy. To enhance predictive performance, we integrate three optimization approaches evaluated across 1000 experiments. The ETR model achieves precise results while necessitating a reduced number of density functional theory (DFT) calculations. The Gaussian Process (GP) model, in conjunction with Bayesian optimization, improves prediction by utilizing probabilistic outputs and uncertainty assessments. We subsequently employed a hybrid methodology that combines ETR and GP models: initially utilizing ETR for fast exploration and then shifting to GP for enhanced optimization. The suggested hybrid method exhibits efficiency and accuracy, attaining robust prediction ability while utilizing fewer computer resources ‐ showing its applicability in future research. Bayesian optimization ; van der Waals force ; Gaussian process ; Process (computing) ; Bayesian probability ; Probabilistic logic ; Gaussian ; Bayesian network Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Maitreyo Biswas | Rushik Desai | Gavin Bidna | Arun Mannodi‐Kanakkithodi
Source: Journal of Chemical Information and Modeling (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization Halide perovskites (HaPs) hold immense potential for applications such as optoelectronics and catalysis. Their vast compositional space, spanning bulk alloys, defects, impurities, surfaces, and surface defects, poses significant challenges for efficient exploration and optimization. To address this, we trained a unified graph-based deep learned interatomic potential capable of optimizing and predicting energetics across these diverse structural motifs and navigating the complex potential energy surface (PES). Using a comprehensive density functional theory data set of HaP structures, which includes bulk alloys, native and impurity defects, and surface slabs, we rigorously trained and benchmarked the M3GNet-based machine learning interatomic potential (IAP). The M3GNet-IAP framework, trained on DFT-calculated energies, forces, and stresses, enables gradient-based optimization and efficient exploration of the PES. Our models, trained on a data set of ∼12,000 HaP structures across diverse structural domains, demonstrated robust generalizability across the complex PES, achieving low errors (energies (<i>E</i>): 3.7 meV/atom; forces (<b>f</b>): 16.5 meV/Å; stresses (<b>σ</b>): 5.5 MPa), and accurately predicting formation energies, decomposition energies, defect energies, and surface energies. Our unified surrogate model provides a holistic approach to geometry optimization across different structural variations in HaPs and will be transformative for the discovery of promising new compositions, important defects and dopants, and surface properties. Interatomic potential ; Energy minimization ; Surface (topology) ; Density functional theory ; Generalizability theory ; Perovskite (structure) ; Set (abstract data type) ; Decomposition ; Impurity Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Adam Hložný | Jozef Haniš | M. Gmitra | Marko Milivojević
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors Identifying the pairing symmetry in unconventional superconductors is essential for reliably characterizing their superconducting states and for enabling their integration into realistic quantum devices. Here, we introduce a machine-learning-guided strategy to determine pairing symmetry from quasiparticle interference (QPI) data, which integrates first-principles calculations, tight-binding modeling, and symmetry-based classification of the superconducting pairing function. We demonstrate the approach on monolayer NbSe2 as an experimentally accessible probe of superconductivity in real materials, within a single scalar-impurity Bogoliubov-de Gennes framework. Our analysis shows that the QPI-to-parameter inverse problem can be solved with high accuracy for most superconducting pairing channels in this setting, indicating that QPI carries rich, learnable information about the superconducting gap structure. Taken together, these results demonstrate that machine-learning-assisted QPI analysis provides a promising pathway for precise learning of superconducting pairing functions in quantum materials. Pairing ; Superconductivity ; Quasiparticle ; Symmetry (geometry) ; Homogeneous space ; Ising model ; Topology (electrical circuits) Machine Learning in Materials Science ; Iron-based superconductors research ; Physics of Superconductivity and Magnetism
Authors: Iván Garzón | Guillermo Gomez-Trenado | Sergio Llorente Damas
Source: Engineering Applications of Artificial Intelligence (article, 2026-01-29)
Field: Chemistry — Analytical Chemistry

A task-oriented few-shot spectroscopy benchmark: Empirical evaluation of meta-learning approaches Near- and mid-infrared (NIR/MIR) spectroscopy, crucial for rapid, noninvasive chemical analysis in industrial applications, is increasingly leveraged by machine learning techniques to extract meaningful information from complex spectral data. Nevertheless, the constraints imposed by limited sample sizes and the occurrence of domain shifts present substantial obstacles to the effectiveness of both deep learning and transfer learning methodologies. In this work, we introduce a unified, task-oriented benchmark that consolidates nine open-access spectral datasets, providing a consistent preprocessing pipeline and clearly defined few-shot tasks. The benchmark is segmented into multiple tests exhibiting varying degrees of heterogeneity, thereby facilitating the evaluation of diverse methodologies across a range of contexts characterized by varying levels of complexity. To the best of our knowledge, meta-learning has not previously been applied in the context of spectrometric analysis. In this study, we confirm that it offers superior performance to traditional transfer learning approaches. A systematic comparison of transfer learning and meta-learning techniques reveals that meta-learning consistently outperforms transfer learning—achieving lower errors in both heterogeneous and homogeneous tasks, consistent with our latent-space analysis showing a more structured cross-task embedding geometry that helps explain these gains. Furthermore, we empirically demonstrate that this benchmark can serve as a starting point for pre-training models on new industrial problems, accelerating adaptation and improving final performance using meta-learning. We complement our analysis with established, model-agnostic interpretability tools, enhancing transparency and industrial relevance in regulated domains. Interpretability ; Benchmark (surveying) ; Transfer of learning ; Preprocessor ; Context (archaeology) ; Pipeline (software) ; Complement (music) ; Embedding ; Domain adaptation Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning
Authors: Vanessa Ferreira | António  C. Silva Ferreira
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Comparative Evaluation of Machine Learning and Deep Learning Strategies for Port Wine Authentication and Age Prediction by FT-IR Spectroscopy and Chemometric Modelling Deep learning ; Support vector machine ; Random forest ; Chemometrics ; Autoencoder ; Residual ; Wine ; Pattern recognition (psychology) ; Discriminative model Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Edible Oils Quality and Analysis
Authors: Jae Gyeong Jung | Jin-Hyun Kim | Changsoo Kim | Donghwan Shim
Source: Journal of Agriculture and Food Research (article, 2026-01-18)
Field: Chemistry — Analytical Chemistry

Assessment of crude protein in wheat kernels using SWIR hyperspectral imaging combined with deep learning-based segmentation Wheat crude protein content is a critical quality parameter for food processing, traditionally assessed using destructive chemical methods such as Kjeldahl or Dumas assays. While near-infrared (NIR) spectroscopy provides non-destructive analysis, its localized measurement approach limits throughput in large-scale applications. This study presents an integrated platform combining deep learning-based segmentation (YOLOv11) with hyperspectral imaging and Partial Least Squares Regression (PLSR) for rapid, non-destructive prediction of crude protein content in individual wheat kernels. We compared Visible and Near-Infrared (VNIR, 397-1004 nm) and Short-Wave Infrared (SWIR, 982-2577 nm) spectral regions for protein prediction. The YOLOv11 segmentation model achieved exceptional accuracy (F1 score > 0.99) across both spectral ranges, enabling automated extraction of individual kernel spectra. Three preprocessing methods (SNV, MSC, and Savitzky-Golay) were systematically evaluated. The SWIR-based PLSR models consistently demonstrated superior generalization (all methods Q 2 > 0.84), achieving a peak performance of Test R 2 = 0.8725, RMSE = 0.7479, and Q 2 = 0.8738 (using MSC). Notably, Savitzky-Golay preprocessing offered the optimal model simplicity, reducing PLS components from 10 to 3 while maintaining robust generalization (Test R 2 = 0.8449, Q 2 = 0.8487). In sharp contrast, the best VNIR model only achieved a Test R 2 = 0.5750 (Q 2 = 0.5793). YOLOv11 exhibited approximately 2.57 times faster inference speed compared to YOLOv8, substantially improving applicability for high-throughput analysis. The integration of high-speed deep learning segmentation with SWIR hyperspectral imaging establishes a practical framework for non-destructive quality assessment in grain processing facilities, quality control laboratories, and automated raw material sorting systems. • Image augmentation significantly improved kernel segmentation performance. • Fast YOLOv11 model (25ms/image) enables real-time high-throughput analysis. • Integrated deep and machine learning accurately predicted kernel crude protein. • SWIR data outperforms VNIR for predicting kernel crude protein (R 2 =0.84). Hyperspectral imaging ; VNIR ; Partial least squares regression ; Preprocessor ; Pattern recognition (psychology) ; Segmentation ; Kernel (algebra) ; Sorting ; Image processing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Xin Wei | Weixiao Sun | Xiashi Wang | Jing Zhang | Weizhen Sun | Zhenhao Xi | Pingwei Liu | Ling Zhao | Weizhong Zheng
Source: AIChE Journal (article, 2026-03-24)
Field: Materials Science — Polymers and Plastics

Two‐step coupled methanolysis and hydrogenation process for upcycling waste <scp>PET</scp> into high‐value <scp>1,4‐cyclohexanedicarboxylate</scp> Abstract Waste polyethylene terephthalate (PET) methanolysis into dimethyl terephthalate (DMT) is a promising route for industrial application but still faces harsh methanolysis conditions and low economic effectiveness. Herein, we proposed a strategy for the upcycling waste PET into the high‐value dimethyl 1,4‐cyclohexanedicarboxylate (DMCD) via two‐step coupled PET methanolysis and DMT hydrogenation at mild reaction conditions. density functional theory‐based various quantum chemical descriptors of deep eutectic solvents and corresponding DMT yields were correlated using machine learning algorithms, and found that metal‐free 1,5‐diazabicyclo[4.3.0]non‐5‐ene/4‐methylimidazole possesses the best catalytic performance for PET methanolysis with DMT and ethylene glycol yields reaching 95% and 89%, respectively, under mild conditions, and exhibits good applicability to various real waste PET and other polyesters. The novel Ru/Al 2 O 3 catalyst for resultant DMT hydrogenation was developed to yield 99.1% DMCD at 160°C, 4 MPa H 2 with superb catalytic activity and stability. This work provides a two‐step integrated upcycling route for waste PET. Catalysis ; Dimethyl terephthalate ; Polyethylene terephthalate ; Ethylene glycol ; Eutectic system ; Yield (engineering) ; Ethylene Polymer crystallization and properties ; biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis
Authors: Marcel Wentzien | Jerome Ingber | Jörg Schlötterer | Thomas Friedrich | Henning Kempka | Dirk Schmalzried | Maik Kunert
Source: Journal of Materials Science (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Steel microstructure analysis using transfer learning: a study on data leakage Abstract Accurate prediction of material properties is essential in steel manufacturing, as it enables optimized production processes. The scarcity of microscopy images in steel microstructure analysis has led to the adoption of pre-trained deep learning models for property prediction on smaller datasets. This study investigates prediction capabilities of a fine-tuned VisionTransformer for five mechanical properties on the microstructures of 47 steel specimen over six magnifications. While the model achieves competitive predictions for upper yield strength, yield strength, ultimate tensile strength and elongation at fracture, while the Charpy V-notch impact energy is predicted less accurate. Lower magnifications tend to improve the prediction of all properties. Our analysis of different popular data splitting strategies indicates that improper splitting of micrographs can lead to data leakage, resulting in an overestimation of model performance by up to threefold. Our findings underscore the need for reliable evaluation procedures and highlight the critical challenges of applying deep learning to steel microstructure analysis. Charpy impact test ; Microstructure ; Ultimate tensile strength ; Elongation ; Solid mechanics ; Yield (engineering) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Material Properties and Failure Mechanisms
Authors: Yongkang Sun | Yunxuan Zhou | Wenhui Tao | Junjie Wu | Yu Xiu | Huiyu Ji | Yuling Liu | Anping Dong | Jie Wang | Jun Wang | Gang Wang | Qi Liu | Mengmeng Wang
Source: Journal of Materials Science (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Inverse design of electrical conductivity in AlSi8 alloy using Bayesian optimization Electrical resistivity and conductivity ; Multilayer perceptron ; Alloy ; Microstructure ; Conductivity ; Bayesian optimization ; Gradient boosting ; Support vector machine Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Aluminum Alloy Microstructure Properties