jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yusuke Ono | Takumi Sato | Kenji Yasuoka | Linyu Peng
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Analysis of molecular dynamics simulation data via statistical distances between covariance matrices Molecular dynamics (MD) simulations are powerful tools for elucidating the macroscopic physical properties of materials from microscopic atomic behaviors. However, the massive, high-dimensional datasets generated by MD simulations pose a significant challenge for analysis, necessitating efficient dimensionality reduction and feature extraction techniques. While existing methods such as principal component analysis and unsupervised learning have been utilized, issues regarding data efficiency and computational cost remain. In this study, we propose a statistical analysis framework focusing on the analysis of the particle data distributions through their covariance matrices, corresponding to the second-order moments of MD trajectory data. Discrepancies between system states are quantified using statistical distances between these covariance matrices. By applying dimensionality reduction to the resulting distance matrix, we extract lower-dimensional features that characterize the systems' dynamics. We validate the proposed method using Lennard-Jones (LJ) particle systems under different temperature conditions, as well as separate bulk systems of ice and liquid water. The results of LJ particles demonstrate an approximately linear correlation between the first principal component obtained through dimensionality reduction of the distance matrix and the diffusion coefficient. This suggests that global physical properties can be effectively inferred from local statistical information, such as covariance matrices, offering a data-efficient alternative for analyzing complex molecular systems. Furthermore, in the case of separate bulk systems of ice and liquid water, the method successfully distinguishes between the two phases, highlighting its potential for characterizing phase transitions and structural differences in molecular systems. Principal component analysis ; Curse of dimensionality ; Dimensionality reduction ; Covariance ; Covariance matrix ; Diffusion map ; Molecular dynamics ; Feature (linguistics) ; Component (thermodynamics) ; Random matrix Material Dynamics and Properties ; Block Copolymer Self-Assembly ; Machine Learning in Materials Science
Authors: Josef Jampilek
Source: Molecules (article, 2026-02-14)
Field: Chemistry — Physical and Theoretical Chemistry

Heterocycles in Medicinal Chemistry III The rapid development of organic chemistry in the early 19th century also saw the development of heterocyclic chemistry [...]. Green chemistry ; Chemical synthesis ; Organic synthesis ; Water chemistry Chemical Reactions and Mechanisms ; Synthesis and Reactions of Organic Compounds ; Synthesis and Reactivity of Heterocycles
Authors: Hyeonah Kim | Minsu Kim | Céline Roget | Dionessa C. Biton | Louis Vaillancourt | Yves V. Brun | Yoshua Bengio | Alex Hernandez-Garcia
Source: arXiv (Cornell University) (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice. Previous works have leveraged Generative Flow Networks (GFlowNets) to impose hard synthesizability constraints through the design of state and action spaces based on predefined reaction templates and building blocks. Despite the promising prospects of this approach, it currently lacks flexibility and scalability. As an alternative, we propose S3-GFN, which generates synthesizable SMILES molecules via simple soft regularization of a sequence-based GFlowNet. Our approach leverages rich molecular priors learned from large-scale SMILES corpora to steer molecular generation towards high-reward, synthesizable chemical spaces. The model induces constraints through off-policy replay training with a contrastive learning signal based on separate buffers of synthesizable and unsynthesizable samples. Our experiments show that S3-GFN learns to generate synthesizable molecules ($\geq 95\%$) with higher rewards in diverse tasks. Prior probability ; Generative model ; Generative grammar ; Flexibility (engineering) ; Template ; Regularization (linguistics) ; Action (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sijia Ke | Yusuf Shaidu | Jeffrey B. Neaton
Source: ArXiv.org (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Enhanced Interlayer Coupling and Excitons in Twin-Stacked Two-Dimensional Magnetic CrSBr Bilayers The degree of electronic coupling between individual layers in few-layer van der Waals heterostructures offers a route to engineer their magnetic, electronic, and optical functionalities. Using state-of-the-art first-principles calculations, we demonstrate that the electronic coupling between two monolayers of CrSBr, an anisotropic two-dimensional magnetic semiconductor, is highly nonlinear and nonmonotonic with respect to their relative twist angle, exhibiting a pronounced maximum at the twin-stacking configuration. The coupling strength scales with both the degree of overlap of Br orbitals adjacent to the van der Waals gap and the cosine of half of the interlayer spin angle. This enhanced interlayer electronic coupling gives rise to excitons delocalized across the two layers with a strong polarization dependence that reflects the details of the interlayer spin alignment. Our results reveal a sensitive interplay between twist angle, magnetism, and excitonic properties in twin-stacked CrSBr bilayers, and they establish twin stacking as an effective route to engineering interlayer coupling and optical response in anisotropic two-dimensional magnets with rectangular lattices. Exciton ; van der Waals force ; Coupling (piping) ; Stacking ; Anisotropy ; Delocalized electron ; Intermolecular force ; Permalloy ; Electronic structure 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Iron-based superconductors research
Authors: Marta Loletti | Alejandro Molina-Sánchez | J. S. Reparaz | Xavier Cartoixà | Riccardo Rurali
Source: arXiv (Cornell University) (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Thermal conductivity and tunable thermal anisotropy of magnetic CrSBr monolayer We present first-principles calculations of the thermal conductivity, ${\bm κ}$, of monolayer CrSBr, a van der Waals magnetic 2D material. We find a considerable thermal anisotropy, with a ratio $κ_{xx}/κ_{yy}$ of around 1.8. The anisotropy stems from a combined effect of phonon velocities and lifetimes and can be tuned by controlling the flake size by suppressing long mean path phonons. Monolayer ; Thermal conductivity ; Anisotropy ; Thermal ; van der Waals force ; Phonon Thermal properties of materials ; 2D Materials and Applications ; Thermal Radiation and Cooling Technologies
Authors: Susumu Minami | Masaya Kono | Yuki Sobue | Takahiro SHIMADA
Source: Journal of the Society of Materials Science Japan (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

First-principles Study of Anomalous Electromechanical Properties Hidden in Paraelectric SrTiO&lt;sub&gt;3&lt;/sub&gt;: Simultaneous Emergence of High-stiffness and Superelasticity via Strain-induced Ferroelectric Phase Transition Since the recent discovery of superelastic behavior in ferroelectric BaTiO3, understanding the mechanical and electromechanical properties of ferroelectric ceramics under high loading conditions has become crucial for engineering applications such as advanced functional nanodevices. Here, we shed light on paraelectric SrTiO3 in contrast ot ferroelectric BaTiO3, and investigate the mechanical strength, deformation behavior, and electromechanical response of the paraelectric ceramics SrTiO3 under high stress conditions with ferroelectric phase transition. Under low-strain conditions, that is, in the paraelectric phase, SrTiO3 shows mechanical properties that maintain the characteristic high stiffness of ceramics. On the other hand, after the strain-induced ferroelectric phase transition, we find that SrTiO3 demonstrates superelastic-like nonlinear deformation behavior. Therefore, SrTiO3 exhibits both high stiffness and deformability simultaneously due to the strain-induced ferroelectric phase transition. Additionally, we clarify the emergence of hysteresis loops in both mechanical deformation and electromechanical responses without accompanying structural phase transitions. We also reveal that giant piezoelectric coefficients manifest at discontinuous points of polarization curves. We conclude that these unique properties in SrTiO3 originate from the displacement of oxygen atoms and the chemical bonding network due to the ferroelectric phase transition, based on analysis of the electronic structure and interatomic distances of each atom with respect to strain. Our results advance fundamental understanding of SrTiO3 behavior under extreme loading conditions and establish a foundation for advanced engineering applications. Ferroelectricity ; Dielectric ; Phase transition ; Hysteresis ; Piezoelectricity ; Phase (matter) ; Deformation (meteorology) Ferroelectric and Piezoelectric Materials ; Nonlocal and gradient elasticity in micro/nano structures ; Chemical and Physical Properties of Materials
Authors: YingLian Qin | Shili Li | Javed Iqbal | Saeed Shirazian
Source: Frontiers in Chemistry (article, 2026-03-05)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence modeling and investigation of metal organic frameworks in drug delivery: modeling of loading capacity and toxicity behavior In this work, our aim was to develop predictive models of Drug Loading Capacity (g/g) and Cell Viability (%) in MOFs (Metal Organic Frameworks) for evaluation of these materials in drug delivery applications and assess their performance. We employed Gaussian Process Regression (GPR) and its advanced variants: Sparse Gaussian Process Regression (SGPR) and Deep Gaussian Process Regression (DGPR) as the base of our modeling framework to estimate the target values. The dataset was carefully preprocessed, involving outlier detection using the z-score method and normalization with Min-Max scaling approach. Dimensionality reduction was executed using Principal Component Analysis (PCA), while hyperparameter optimization was performed with the Cheetah Optimizer (CO), a metaheuristic method. Among the models evaluated, DGPR demonstrated superior performance, achieving mean cross-validation <i>R</i> <sup>2</sup> scores of 0.99878 ± 0.000092 for Drug Loading Capacity and 0.99911 ± 0.000127 for Cell Viability. Explainable AI techniques, especially SHAP, were employed to elucidate the model's predictions, offering essential insights into the contributions of various features to the outcomes. Normalization (sociology) ; Kriging ; Hyperparameter ; Dimensionality reduction ; Principal component analysis ; Partial least squares regression ; Gaussian process ; Outlier ; Regression Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mingxi Cai | Lijun Shao | Jiawei Zhang | Yong Zheng | Chaohui Gong | Xinhao Sun | Penggang Li | Rongling Zhang
Source: Buildings (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Performance Evaluation of CFRP-Wrapped Concrete Made Using Magnesium Phosphate Cement (MPC) Under Sulfate Exposure Long-term deterioration of concrete due to synergistic dry–wet cycles and sulfate attack significantly compromises the structural durability and integrity. CFRP-reinforced concrete offers a promising solution for repair and performance enhancement under such adverse conditions. In addition, binder selection is critical to the reinforcement efficacy. However, traditional epoxy (EP) binders have certain limitations in environmental adaptability and sustainability, necessitating alternative approaches. Thus, inorganic magnesium phosphate cement (MPC) binders have garnered attention due to their rapid hardening, early strength, interface compatibility, and environmental benefits. Nevertheless, the durability of MPC under combined sulfate exposure and dry–wet cycles, as well as the performance of CFRP-bonded concrete using MPC, has remained underexplored. In this study, the chemical, microstructural, pore, and mechanical properties of MPC mortar and MPC–CFRP concrete columns subjected to sulfate dry–wet cycles were systematically investigated and compared to ordinary and EP–CFRP concretes. The results indicate that MPC mortar forms struvite during sulfate dry–wet cycles, though the struvite content decreases with increasing cycles. In addition, the porosity of MPC mortar initially decreases and then increases, reaching a maximum of 1.52. The resulting compressive and flexural strengths of MPC mortar decline over cycles, reaching 28.4 MPa and 8.4 MPa, respectively, after 240 cycles. For concrete columns, sulfate dry–wet cycles result in significant sulfate crystallization on EP–CFRP surfaces, whereas MPC–CFRP columns show reduced crystallization and improved ductility. After 240-day cycles, compressive strengths of ordinary, EP–CFRP, and MPC–CFRP columns were 17.64 MPa, 53.87 MPa, and 72.41 MPa, respectively, with the strength retention rates of 56.89%, 65.57%, and 86.87%. The MPC–CFRP columns exhibited the best mechanical properties, strength retention, and ductility. Further, the degradation mechanisms of MPC and MPC/EP–CFRP concretes under sulfate dry–wet cycles were clarified, offering a reference for enhanced durability and service life of concrete structures in saline soil regions. Durability ; Mortar ; Sulfate ; Compressive strength ; Magnesium phosphate ; Flexural strength ; Efflorescence ; Cement Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Layered Double Hydroxides Synthesis and Applications
Authors: Siska Anraeni | Harlinda Lahuddin | Ramdaniah Ramdaniah | Erika Riski Melani | Andi Cici Amalia | Tazkirah Amaliah
Source: International Journal of Advances in Intelligent Informatics (article, 2026-02-28)
Field: Chemistry — Analytical Chemistry

A comprehensive comparative analysis of chicken meat classification techniques through machine learning models This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken and the Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification. Pattern recognition (psychology) ; Feature extraction ; Feature (linguistics) ; Local binary patterns ; Euclidean distance ; Purchasing ; Metric (unit) ; Texture (cosmology) Spectroscopy and Chemometric Analyses ; Computer Science and Engineering ; Food Supply Chain Traceability
Authors: Aida Aguilera Infante-Neta | Alan Portal D’Almeida | Raissa Saraiva Lima | Cecilia Juan Antonio | Ivanildo José da Silva | Luciana Barros Gonçalves | Tiago Lima de Albuquerque
Source: Foods (article, 2026-03-05)
Field: Materials Science — Biomaterials

Waste Valorization of Passion Fruit Peel Hydrolysate for Bacterial Cellulose Production: Influence of Nitrogen Source on Yield and Functional Properties for Food Packaging The valorization of agro-industrial residues represents a strategic approach to advancing sustainability and circular bioeconomy principles in the food sector. Although bacterial cellulose (BC) production from waste substrates has been widely explored, limited attention has been given to the role of nitrogen source modulation in complex fermentation systems. This study evaluated passion fruit peel hydrolysate (PFPH), a cellulose- and hemicellulose-rich by-product, as an alternative carbon source for BC production using a symbiotic culture of bacteria and yeast (SCOBY) under static conditions. Acid hydrolysis and detoxification were performed to obtain fermentable sugars while minimizing inhibitory compounds. Different nitrogen sources and purification strategies were comparatively assessed. The highest purified BC yield (81 g L<sup>-1</sup> of culture medium) was obtained using ammonium sulfate, whereas sodium nitrate promoted greater impurity removal (77.51% mass reduction). Structural and chemical analyses (FTIR, XPS, and XRD) confirmed effective delignification, enhanced surface purity, and increased crystallinity. SEM revealed a homogeneous nanofibrillar network, and thermogravimetric analysis indicated thermal stability up to approximately 300 °C. Soil burial assays showed 26% mass loss after 42 days, demonstrating controlled biodegradation consistent with food packaging requirements. Overall, PFPH proved to be an efficient and sustainable substrate for BC biosynthesis. The modulation of nitrogen source significantly influenced both production yield and structural properties, highlighting the potential of this system for developing environmentally responsible biopolymer materials for food packaging applications. Hydrolysate ; Fermentation ; Biopolymer ; Bacterial cellulose ; Yeast extract ; Food packaging ; Cellulose ; Cellulase ; Nitrogen Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Polysaccharides and Plant Cell Walls
Authors: Sanggyu Chong | Tong Jiang | Michelangelo Domina | Filippo Bigi | Federico Grasselli | Joonho Lee | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Resolving the body-order paradox of machine learning interatomic potentials In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is directly built on neighbor density correlation descriptors and is implicit when the model captures the correlations through the non-linear functions of low body-order terms. In both cases, the "effective body-orderedness" of MLIPs remains largely unexplained: how do the models decompose the total energy into body-ordered contributions, and how does their body-orderedness affect the accuracy and learning behavior? In answering these questions, we first discuss the complexities in imposing the many-body expansion on ab initio calculations at the atomic limit. Next, we train a curated set of MLIPs on datasets of hydrogen clusters and reveal the inherent tendency of the ML models to deduce their own, effective body-order trends, which are dependent on the model type and dataset makeup. Finally, we present different trends in the convergence of the body-orders and generalizability of the models, providing useful insights into the development of future MLIPs. Generalizability theory ; Convergence (economics) ; Set (abstract data type) ; Interatomic potential ; Ab initio ; Energy (signal processing) ; Development (topology) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Bo Chen | Mohammad Umair Rafiq | Muhammad Usman | Sarmad Frogh Arshad | Akhtar Hameed | Manzar Abbas
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Smart nanosystems for disease-resistant potatoes: a new Frontier in nanobiotechnology Microbial diseases in potato crops pose significant threats to production quality and crop protection. Yield losses due to these diseases are unsustainable for a world that is increasingly reliant on potato-based diets. Conventional management strategies, including chemical and biological controls, have been employed, but they often disrupt biodiversity conservation. The resulting ecological degradation has driven researchers to seek sustainable alternatives. Emerging technologies now address phytopathogens-viruses, fungi, and bacteria-while minimizing the adverse effects of traditional methods. Among these, nanotechnology, utilizing materials with at least one dimension between 1-100 nm, has revolutionized plant health through nanopesticides and targeted pesticide delivery systems. These innovations exhibit minimal ecological impact while demonstrating potent antimicrobial activity against key potato diseases such as early blight, late blight, common scab, soft rot, and blackleg. Nanoparticles (NPs) generate reactive oxygen species (ROS), which lyse microbial cells while simultaneously activating defense-related signaling pathways (salicylic acid and jasmonic acid pathways). These pathways upregulate pathogenesis-related (PR) genes, enhancing PR protein synthesis to combat microbial invasion. Nanotechnology has enabled the design of advanced nano-biosensors for disease detection. By leveraging nanoparticle properties such as a high surface-area-to-volume ratio, photoluminescence, electrical conductivity, and biomolecular interaction, these sensors precisely identify microbial biomarkers. Additionally, the small size, surface charge, controlled release, and tunable surface chemistry of nanoparticles help in optimizing targeted gene and drug deliveries. Nanotechnology further enhances genome-editing tools, like CRISPR/Cas9 and RNA interference (RNAi), facilitating the development of disease-resistant transgenic potato varieties. It also induces the production of antioxidant enzymes, osmolytes, stress-responsive genes, and structural barriers to mitigate abiotic stresses. In summary, nanotechnology offers a multidisciplinary approach for combating phytopathogens, ensuring sustainable potato cultivation with minimal ecological disruption. Nanobiotechnology ; Frontier ; Quality (philosophy) ; Crop ; Applications of nanotechnology ; Crop production Nanoparticles: synthesis and applications ; Nanocomposite Films for Food Packaging ; Potato Plant Research