jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deyun Sun | Shangqing Chen | Haonan Wu | Mingyue Qiu | Youluan Lu | Ningyuan Wang | Qian Ma | Lijuan Shi | Qun Yi
Source: Molecules (article, 2026-03-12)
Field: Chemistry — Inorganic Chemistry

Core–Shell Metal–Organic Framework Composites: A Review of Synthetic Strategies and Applications in Catalysis and Adsorption Core-shell metal-organic framework (MOF) composites, owing to their unique structural advantages, have emerged as a prominent research focus in the field of chemistry, advanced materials and chemical engineering. By integrating MOFs with other functional components such as MOFs, covalent organic frameworks (COFs), metal oxides, carbon materials, ionic liquids or polymers into synergistic heterogeneous architectures, coreshell MOFs can markedly enhance physicochemical stability and enable diversified functional performances. This work provides a systematic overview of the major construction strategies for these materials, including in situ growth, self-templating, seed-mediated methods, one-pot synthesis and post-synthetic modification. It also summarizes recent applications in catalysis (thermal, electrocatalytic and photocatalytic processes) as well as gas adsorption and separation (such as CO<sub>2</sub> capture from flue gas, natural gas purification and acetylene separation). The final section discusses future research directions, including a deeper understanding of interfacial growth mechanisms, the development of green and scalable synthesis routes, the validation of engineering-oriented applications, and the integration of machine learning with high-throughput computation for structural prediction and accelerated materials screening, thereby providing important guidance for the development of high-performance core-shell MOFs. Adsorption ; Flue gas ; Catalysis ; Metal-organic framework ; Acetylene ; Ionic liquid ; Heterogeneous catalysis ; Covalent bond Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Neelam Mishra | Surjayan Biswas | Rajamanickam V S | Phani Aluru | Yi Liu | Meysam Akbari | Jason Coryell
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2026-04-07)
Field: Materials Science — Metals and Alloys

Machine Learning-Based Prediction of Tensile Properties in Cast Stainless Steel Influenced by Chemical Composition Variation &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In the category of cast stainless steels, there are several variants per different level of addition of chromium, vanadium along with some minor elements, such as molybdenum, niobium, tungsten to meet the requirement of corrosion and oxidation resistance. However, the influence of chemical composition variations on the mechanical properties of cast SS continues to lack a clear understanding. In the present study, via machine learning, the effects of each element on the tensile properties of the selected cast stainless steel are studied. The machine learning model is then used to predict how variations in elements affect tensile behavior, with the predictions validated through physical testing.&lt;/div&gt;&lt;/div&gt; Ultimate tensile strength ; Chemical composition ; Vanadium ; Tungsten ; Corrosion ; Base metal Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Changcheng Chen | Ruixin Tu | ChunLian XIONG | Jingwen Deng | Meng Bao | Yunpu Wen | Yaxin Xu | Xinhui Zhang | Chunling Zhang | Jiangzhou Xie | Gang Liu | Xiaoning Guan | Pengfei Lu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Study on the Material Properties of AB2C4 (A=Mg; B=In, Ga; C=Te, Se) Near Infrared Materials Based on Machine Learning and Density Functional Theory Photodetection ; Ternary operation ; Infrared ; Density functional theory ; Transmittance ; Absorption (acoustics) ; Band gap ; Chalcogenide Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties
Authors: Dr.Srinagalakshmi Nammi Sangat Naik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Physics-Guided Machine Learning Framework for Defect Prediction and Mechanical Property Modeling in WAAM of Nitinol Shape Memory Alloy Wire Arc Additive Manufacturing (WAAM) has emerged as a promising technique for fabricating large-scale metallic components; however, process instability and defect formation remain critical challenges, particularly for thermally sensitive materials such as Nitinol shape memory alloys (SMAs). In this study, a physics-guided machine learning framework is developed to predict defect occurrence and mechanical performance in WAAM-fabricated Nitinol. Ten experimental cases were produced under varying current, voltage, and torch travel speed conditions, and the corresponding heat input was calculated using thermodynamic energy balance principles. Defect observations, hardness values (before and after heat treatment), and ultimate tensile strength (UTS) were experimentally evaluated. A hybrid modeling strategy integrating process parameters with physics-derived heat input was implemented using classification and regression approaches. Leave-One-Out Cross Validation (LOOCV) was employed to ensure robust evaluation given the limited dataset. Results indicate that inclusion of heat input as a derived physical feature significantly improves prediction consistency and interpretability. An optimal process stability window was identified around intermediate heat input levels, corresponding to defect-free builds and stable mechanical response. The proposed physics-guided learning framework demonstrates the potential of integrating metallurgical understanding with data-driven modeling for intelligent process control in additive manufacturing of shape memory alloys. Process (computing) ; Shape-memory alloy ; Consistency (knowledge bases) ; Stability (learning theory) ; Ultimate tensile strength ; Process window ; Energy (signal processing) ; Titanium alloy ; Property (philosophy) Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Zekun Lou | Alan M. Lewis | Mariana Rossi
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials Moiré superlattices in two-dimensional (2D) materials exhibit rich quantum phenomena, but ab initio modelling of these systems remains computationally prohibitive. Existing machine learning methods for accelerating density-functional theory (DFT) can target the prediction of different quantities and often rely on the locality assumption. Here we train a Gaussian process regression SALTED model exclusively on the electron densities of small displaced bilayer structures and then extrapolate electron density prediction to the large supercells required to describe small twist angles between these bilayers. We show the necessity of long-range descriptors to yield reliable band structures and electrostatic properties of large twisted bilayer structures, when these are derived from predicted densities. We demonstrate that the choice of descriptor determines the distribution of residual density errors, which in turn affects the downstream electronic properties. We apply our models to twisted bilayer graphene, hexagonal boron nitride, and transition metal dichalcogenides, focusing on the model's capacity to predict complex phenomena, including flat band formation, bandwidth narrowing, domain-wall electric fields, and spin-orbit coupling effects. Beyond moiré materials, this approach provides a general methodology for electronic structure prediction in large-scale systems with substantial long-range phenomena related to non-local geometric information. Bilayer ; Bilayer graphene ; Ab initio ; Coupling (piping) ; Electron density ; Electronic structure ; Density of states ; Electronic band structure Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Zekun Lou | Alan M. Lewis | Mariana Rossi
Source: Open MIND (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials Moiré superlattices in two-dimensional (2D) materials exhibit rich quantum phenomena, but ab initio modelling of these systems remains computationally prohibitive. Existing machine learning methods for accelerating density-functional theory (DFT) can target the prediction of different quantities and often rely on the locality assumption. Here we train a Gaussian process regression SALTED model exclusively on the electron densities of small displaced bilayer structures and then extrapolate electron density prediction to the large supercells required to describe small twist angles between these bilayers. We show the necessity of long-range descriptors to yield reliable band structures and electrostatic properties of large twisted bilayer structures, when these are derived from predicted densities. We demonstrate that the choice of descriptor determines the distribution of residual density errors, which in turn affects the downstream electronic properties. We apply our models to twisted bilayer graphene, hexagonal boron nitride, and transition metal dichalcogenides, focusing on the model's capacity to predict complex phenomena, including flat band formation, bandwidth narrowing, domain-wall electric fields, and spin-orbit coupling effects. Beyond moiré materials, this approach provides a general methodology for electronic structure prediction in large-scale systems with substantial long-range phenomena related to non-local geometric information. Bilayer ; Bilayer graphene ; Ab initio ; Coupling (piping) ; Electron density ; Electronic structure ; Density of states ; Electronic band structure Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: A. El Alouani | Mohamed Al Khalfioui | A. Michon | S. Vézian | P. Boucaud | M-T. Dau
Source: Materials Today Physics (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Defect formation energy of impurities in 2D materials: How does data engineering shape machine learning model selection? Regressive Machine learning (ML) has emerged as an alternative method for theoretically evaluating materials properties. Most of ML-based study of materials properties including dataset handling, feature spaces, transformers and estimators have been reported without questioning the ML foundation of these components and their interactions with the outcomes because of the availability of homogeneous datasets, standardized training conditions and technical implementation challenges. In this paper, a database of defect impurities in 2D materials was used to assess the impact of ML workflow’s components on the training-inference process. By investigating descriptor engineering (vectorized matrix properties) and model algorithms (statistical tree-based and artificial neural network - ANN models) on two sub-datasets derived from the database (interstitial-int and adsorbate-ads impurities), we report a comprehensive study of the ML-based prediction of the energy formation of the impurities in 2D materials. Quantitatively, for the statistical models, the training errors lower than 1.4 eV and 1.1 eV were found thanks to the descriptor engineering for the int and the ads datasets, respectively. Regarding the ANN models, these values are 2.1 eV and 1.3 eV. The prediction errors on the unseen data (test sets) were found lower than the ones obtained without descriptor engineering for all models. However, the overfitting effect remains visible but less pronounced for the ads dataset than for the int dataset. This finding reveals the impact of the dataset characteristics on the performance of the ML ecosystem involving data engineering and model algorithms. Beyond the search of best performances in regressive ML prediction of 2D materials properties, our work demonstrates a full-scale study of the ML process starting from the data engineering to model evaluation and selection, allowing to benchmark the criteria for further ML assessment in terms of training, models and prediction. Our results could be reference for further works in ML-led prediction physics of materials science. Overfitting ; Artificial neural network ; Feature engineering ; Experimental data ; Impurity ; Estimator ; Energy (signal processing) ; Dropout (neural networks) ; Robustness (evolution) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: JianTao Liu | Ni Jin | Yiyuan Zhang | Jun Cheng | Aofei Xu | Xingda Huang | Xudong Liu | Yixuan He
Source: Journal of Materials Science (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Machine learning-assisted exploration and experimental assessment of β-type Ti54-xZr15Nb14Mo17Cux alloy with an ultra-low modulus for orthopedic applications Biocompatibility ; Alloy ; Solid mechanics ; Elastic modulus ; Titanium alloy ; Modulus ; Orthopedic surgery ; Antibacterial activity Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Magnesium Alloys: Properties and Applications
Authors: Z.Y. Wu | Tetsuya Tohei | Yusuke Hayashi | Shigeyoshi Usami | Masayuki Imanishi | Yusuke Mori | Junichi Takino | Kazushi Sumitani | Yasuhiko Imai | Shigeru Kimura | Akira Sakai
Source: Materials & Design (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Uncovering crystal structure evolution via nanobeam X-ray diffraction with a continuity-driven machine learning approach • Robust and generalized unsupervised continuity-driven machine learning framework for nanoXRD-based SXDM data. • Novel metric for detecting structural discontinuities without labels, usually overlooked by conventional methods. • Visualization of structural change at interfaces/boundaries in bulk GaN. • New insights linking crystal processing to microstructural defects. Nanobeam X-ray diffraction (nanoXRD) enables nanoscale mapping of crystal structures across wafer-scale crystals, offering unique insight into microstructural evolution during crystal growth. However, the resulting large and complex diffraction datasets make it challenging to quantitatively resolve local structural transitions and their connection to growth processes using conventional analysis. Here, we present a continuity-driven, unsupervised, and generalized analysis framework, referred to as the neighborhood-based similarity metric , which integrates spatial coordinates with nanoXRD data to reveal structural variations across growth sectors, interfaces, and defect-related regions without requiring prior knowledge or labels. By introducing Jaccard similarity scores to compare local neighborhoods in spatial and diffraction domains, the method quantitatively detects discontinuities where structural evolution disrupts the local continuity of diffraction patterns. Our unsupervised approach, validated with synthetic data and nanoXRD measurements of bulk GaN crystals, successfully identified both known defects and previously hidden structural discontinuities. The results provide new insights into the relationship between growth conditions, local strain evolution, and defect formation, establishing a robust and interpretable approach for linking processing and structural characteristics in complex crystalline materials. Classification of discontinuities ; Diffraction ; Metric (unit) ; Similarity (geometry) ; Jaccard index ; Unsupervised learning ; Structural similarity Machine Learning in Materials Science ; GaN-based semiconductor devices and materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Arko Mohari | Soumya Mondal | Debashis Sing Mura | Ayan Datta
Source: Chemistry - An Asian Journal (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Prediction of Negative Thermal Expansion in 2D‐Materials by Multistep Machine Learning Using Structural Descriptors The discovery of new negative thermal expansion (NTE) metamaterials can be substantially difficult in experiments and computationally expensive in theoretical investigations. In this study, a multistep machine learning (ML) approach was employed to predict NTE maxima (α<sub>max</sub>) and thermal expansion coefficient (TEC) in 2D materials. Our predicted target attributes show high correlation with first-principles calculations within quasi-harmonic approximation (QHA). The key idea is to take structural and experimentally tunable features as input variables, which can predict NTE efficiently. Out of the 234 investigated 2D materials, 194 of them can be labeled as NTE materials, as they show NTE within the considered temperature range, T = 0-1000 K. Blind tests were performed using a set of popular materials selected from the 2DMatPedia database to validate the robustness of the model. This work presents a systematic approach toward the rapid screening and prediction of thermal expansion. The identified important features provide guidance for the future design of new 2D NTE materials. Robustness (evolution) ; Thermal expansion ; Negative thermal expansion ; Thermal ; Set (abstract data type) ; Image (mathematics) ; Correlation coefficient ; Work (physics) Thermal Expansion and Ionic Conductivity ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Carolina L. Recio-Colmenares | Roxana Recio-Colmenares | César García-García
Source: Journal of Composites Science (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Cross-Study Machine Learning Analysis of Structure–Property–Performance Relationships in Macroporous PolyHIPE-Based Magnetic Polymer Composites Elucidating the complex structure–property–performance relationships in multifunctional polymer matrix composites (PMCs) remains a formidable challenge. This difficulty stems from the intricate coupling between formulation variables, porous morphology, physicochemical attributes, and functional outcomes, particularly under the “small-data” constraints inherent to experimental materials research. This study introduces a robust, interpretable machine learning (ML) framework tailored for the analysis of macroporous polyHIPE-based magnetic composites. All analyses were conducted exclusively on curated experimental data reported in the literature. By leveraging a curated dataset synthesized from multiple independent studies with harmonized characterization protocols, we integrated processing parameters and quantitative morphological descriptors to predict two critical engineering outputs: dye removal efficiency (%) and saturation magnetization (Ms). Nonlinear ensemble ML models were rigorously trained and evaluated using repeated cross-validation and cross-study validation strategies to ensure predictive robustness and domain transferability. The superior performance of nonlinear models over linear baselines underscores that composite functionality is governed by synergistic, non-additive interactions. Model-agnostic interpretability analyses further revealed that pore interconnectivity and accessible surface area are the primary determinants of adsorption performance. Conversely, while increased magnetic nanoparticle loading enhances magnetic responsiveness, it induces a significant trade-off with adsorption efficiency. These findings demonstrate that uncertainty-aware ML can extract generalizable, physically grounded design insights from heterogeneous experimental literature, providing a streamlined, data-driven pathway for the rational design and screening of multifunctional porous materials without necessitating additional experimental overhead. Interpretability ; Robustness (evolution) ; Interconnectivity ; Nonlinear system ; Characterization (materials science) ; Experimental data ; Adsorption ; Porosity Machine Learning in Materials Science ; Electromagnetic wave absorption materials ; Fiber-reinforced polymer composites
Authors: Niranjan Pattar | Subhas F. Patil | Reena Y A | Manjiri Ulhas Karande | M. Zubairuddin | Sonal Borase | Shahid Tamboli
Source: Discover Applied Sciences (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Application of machine learning and genetic algorithm framework for optimization of shape memory alloy embedded smart composites The growing demand for intelligent system in soft robotics, biomedical devices, and aerospace systems has created an opportunity to build smart composite structures. However, addressing the shape memory properties remains a great challenge, due to its mutual dependency of material and processing parameters. The parameters considered includes composite type (unmodified, and modified nanoclay), number of shape memory alloy wire (1, 2, 3), glass fiber layers (2 and 4), training cycles (1, 2, 3, 4), and nanoclay loading (1wt%, 3wt%, 5wt%). A powerful optimization framework that blends genetic algorithms (GAs) and machine learning (ML) is presented in this study. Experimental dataset from design of experiments is used to train the ML models. Gaussian Process Regression performs exceptionally better with high prediction accuracy of R2 = 94. 34%, 91.98% for shape recovery and fixity followed by other ML models. Combined contribution of parametric combination on shape memory properties were evaluated via surface response plots by highlighting that higher cycles improve fixity, while higher nanoclay and SMA levels enhance recovery. GA optimization revealed that the modified nanoclay with 5wt% loading, 3 SMA wire, 4 glass fiber layers, and 2 training cycles was the optimal configuration. This setup achieved superior performance with 86.7% and 88.4% for shape recovery and fixity respectively. Pareto front plot is used to visualize the trade-off relationship between the shape recovery and fixity presents the inverse relationship. The proposed ML-GA approach offers interpretable insights into structure–property links. This paradigm offers a broadly applicable method for data-driven functional material design. Genetic algorithm ; Shape-memory alloy ; Evolutionary algorithm ; Optimization algorithm ; Artificial neural network Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Aeroelasticity and Vibration Control