jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulia Pimonova | Michael G. Taylor | Alice Allen | Ping Yang | Nicholas Lubbers
Source: ArXiv.org (preprint, 2025-09-16)
Field: Materials Science — Materials Chemistry

Meta-Learning Linear Models for Molecular Property Prediction Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced predictive capabilities in chemical sciences, but these modern data-driven approaches have increased the demand for data. In response to the growing demand for explainable AI (XAI) and to bridge the gap between predictive accuracy and human comprehensibility, we introduce LAMeL - a Linear Algorithm for Meta-Learning that preserves interpretability while improving the prediction accuracy across multiple properties. While most approaches treat each chemical prediction task in isolation, LAMeL leverages a meta-learning framework to identify shared model parameters across related tasks, even if those tasks do not share data, allowing it to learn a common functional manifold that serves as a more informed starting point for new unseen tasks. Our method delivers performance improvements ranging from 1.1- to 25-fold over standard ridge regression, depending on the domain of the dataset. While the degree of performance enhancement varies across tasks, LAMeL consistently outperforms or matches traditional linear methods, making it a reliable tool for chemical property prediction where both accuracy and interpretability are critical. Interpretability ; Property (philosophy) ; Task (project management) ; Linear model ; Domain (mathematical analysis) ; Face (sociological concept) ; Bridge (graph theory) ; Applicability domain Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Koyeli Girigoswami | Agnishwar Girigoswami
Source: Biopharmaceutics & Drug Disposition (review, 2025-09-16)
Field: Materials Science — Materials Chemistry

Integration of Data‐Driven Techniques in Nanomedicines to Address Diagnosis and Drug Delivery Strategy for Therapy The progress of drug designing, drug delivery systems (DDS), and disease diagnostic systems has significantly advanced pharmaceutical development, as evidenced by the FDA-approved nanomedicines with enhanced selectivity, controlled release, and synergistic therapeutic effects. However, the design and large-scale development of nanomaterial-based DDS remain challenging due to difficulties in managing and analyzing complex experimental data. The integration of data-driven techniques, high-throughput experimental networks and protocols, automation, artificial intelligence (AI), and machine learning (ML)-a framework known as the fourth paradigm of scientific research that offers a promising solution. This review article highlights milestones in applying these technologies to biomarker-based diagnosis and DDS, including nanomaterial design, and explores their potential to accelerate drug development and clinical translation. It also outlines the future prospects or directions for leveraging these approaches to create highly efficient, customizable nanomedicines and smart materials with defined physicochemical properties, aiming to benefit researchers in materials science, nanotechnology, and biomedical DDS development. Drug delivery ; Nanomedicine ; Drug ; Targeted drug delivery ; Disease treatment ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Saibo She | Xiaochu Pang | Jun Liu | Xinnan Zheng | Kuohai Yu | Xun Zou | Ruoxuan Zhu | Rui Guo | Wuliang Yin
Source: Engineering Applications of Artificial Intelligence (article, 2025-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast inversion of parameters on Jiles–Atherton hysteresis model based on physics-guided deep learning network Computer science ; Inversion (geology) ; Artificial intelligence ; Geology ; Seismology Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Edwin A. Bedolla-Montiel | Susana Marín-Aguilar | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Relationship between structure and dynamics of an icosahedral quasicrystal using unsupervised machine learning We present a comprehensive study of the structure, formation, and dynamics of a one-component model system that self-assembles into an icosahedral quasicrystal (IQC). Using molecular dynamics simulations combined with unsupervised machine learning techniques, we identify and characterize the unique structural motifs of IQCs, including icosahedral and dodecahedral arrangements, and quantify the evolution of local environments during the IQC formation process. Our analysis reveals that the formation of the IQC is driven by the emergence of distinct local clusters that serve as precursors to the fully developed quasicrystalline phase. In addition, we examine the dynamics of the system across a range of temperatures, identifying transitions from vibrationally restricted motion to activated diffusion and uncovering signatures of dynamic heterogeneity inherent to the quasicrystalline state. To directly connect structure and dynamics, we use a machine-learning-based order parameter to quantify the presence of distinct local environments across temperatures. We find that regions with high structural order, as captured by specific machine-learned classes, correlate with suppressed self-diffusion and minimal dynamical heterogeneity, consistent with phason-like motion within the IQC. In contrast, regions with lower structural order exhibit enhanced collective motion and increased dynamical heterogeneity. These results establish a quantitative framework for understanding the coupling between structural organization and dynamical processes in quasicrystals, providing new insights into the mechanisms governing IQC stability and dynamics. Icosahedral symmetry ; Quasicrystal ; Coupling (piping) ; Dynamics (music) ; Motion (physics) ; Stability (learning theory) ; Molecular dynamics ; Unsupervised learning Quasicrystal Structures and Properties ; Crystallography and molecular interactions ; Material Dynamics and Properties
Authors: Zhenkai Qin | Qining Luo | Weiqi Qin | Xiaolong Chen | Hongfeng Zhang | Cora Un In Wong
Source: Materials (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

GCN-Based Framework for Materials Screening and Phase Identification This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.990 and a recall of 0.872. This performance is attained with minimal hyperparameter tuning, making it scalable for large-scale material discovery applications. Data augmentation, including synthetic data generation and noise injection, enhances the model's robustness by simulating real-world experimental variations. However, the model's reliance on synthetic data and the computational cost of graph construction and inference remain limitations. Future work will focus on integrating real experimental data, optimizing computational efficiency, and exploring lightweight architectures to improve scalability for high-throughput applications. Scalability ; Computer science ; Robustness (evolution) ; Inference ; Hyperparameter ; Graph ; Identification (biology) ; Machine learning ; Data mining ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Cultural Heritage Materials Analysis
Authors: Khaled SharafEldin | Bryan D. Miller | Wenjun Liu | J. Z. Tischler | Benjamin Anglin | Anter El‐Azab
Source: Acta Materialia (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Informed unsupervised machine learning analysis of dislocation microstructure from high-resolution differential aperture X-ray structural microscopy data Dislocation ; Microscopy ; Cauchy distribution ; Rotation (mathematics) ; Microstructure ; Optical microscope ; Crystallite ; Lattice (music) Microstructure and mechanical properties ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Geeta Sharma | Sarathkumar Loganathan | Evangelos Daskalakis | Eric Kumi‐Barimah | S.M. Strafford | Animesh Jha
Source: Advanced Intelligent Systems (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Phase Stability in Rare‐Earth‐Doped Apatites: A Machine Learning Approach The study examines thermal behavior and phase stability of rare‐earth‐doped apatites (cerium, samarium, and holmium ions) between 25 °C and 1200 °C. Decomposition and thermal‐induced phase transitions are analyzed by in situ high‐temperature X‐ray diffraction (HT‐XRD) and thermogravimetric analysis (TGA). Decision tree machine learning (ML) model is developed to predict phase stability and decomposition products as a function of composition and temperature. The feature importance analysis identifies temperature as the determining factor for phase stability. The correlation heatmap shows strong correlation between temperatures above 600 °C and phase instability. The model achieves an accuracy of ≈86%, classified into three thermal regimes: 1) single‐phase stable behavior below 500 °C, 2) partial decomposition between 500 °C and 800 °C with formation of β‐tricalcium phosphate (β‐TCP) and oxyapatite, and 3) complete transformation above 800 °C into α‐TCP and Sr/Ca‐apatite phases. Cerium and samarium ion doping improve stability up to 600 °C by reducing vacancy formation, whereas co‐doping with holmium ion triggers earlier decomposition (≈500 °C) due to increased lattice strain. ML‐based framework reduces need for large‐scale experimental screening, enhances research efficiency and offers predictive in silico tool to design thermally stable apatite‐based materials for biomedical and catalytic applications. Rare earth ; Doping ; Earth (classical element) ; Materials science ; Stability (learning theory) ; Phase (matter) ; Computer science ; Geology ; Chemistry ; Metallurgy ; Physics ; Optoelectronics ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Yue Liu | Feng Zihao | Qiang Cao | Baolin Wang | K.F. Wang
Source: Applied Thermal Engineering (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Physics-informed neural network for transient behavioral modeling of thermoelectric generators Transient (computer programming) ; Artificial neural network ; Thermoelectric effect ; Behavioral modeling ; Systems engineering ; Mechanical engineering ; Engineering ; Computer science ; Physics ; Artificial intelligence ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Heat Transfer and Optimization ; Neural Networks and Applications
Authors: Doug Crane | Lon E. Bell
Source: Solid State Sciences (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Advancements in distributed transport property thermoelectrics: Enhancing performance through material selection and innovative manufacturing Thermoelectric materials ; Selection (genetic algorithm) ; Property (philosophy) ; Material selection ; Nanotechnology ; Manufacturing engineering ; Materials science ; Systems engineering ; Engineering physics ; Engineering ; Computer science ; Composite material ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Dzaki Asari Surya Putra | Nibras Bahy Ardyansyah | Nicholaus Verdhy Putranto | Gustina Alfa Trisnapradika | Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono
Source: Journal of Bio- and Tribo-Corrosion (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application Corrosion ; Biochemical engineering ; Computer science ; Process engineering ; Artificial intelligence ; Materials science ; Metallurgy ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Austin Houston | Sumner B. Harris | Hao Wang | Yu‐Chuan Lin | David B. Geohegan | Kai Xiao | Gerd Duscher
Source: Nano Letters (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Atom Identification in Bilayer Moiré Materials with Gomb-Net Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS<sub>2</sub>-WS<sub>2(1-<i>x</i>)</sub>Se<sub>2<i>x</i></sub> heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics. Bilayer ; Moiré pattern ; Atom (system on chip) ; Heterojunction ; Atomic units ; van der Waals force ; Dopant ; Materials science ; Scanning transmission electron microscopy ; Molecular physics ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Transmission electron microscopy ; Chemistry ; Physics ; Optics ; Optoelectronics ; Doping ; Molecule ; Membrane Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Masaya Matamura | Takumi Sekoguchi | Yoshimasa NISHIKAWA | Hirotaka Naito | Atsushi Hashimoto | Makoto Kondo
Source: Computers and Electronics in Agriculture (article, 2025-01-24)
Field: Chemistry — Analytical Chemistry

Prediction of fecal starch content of fattening cattle using near-infrared spectroscopy and machine learning • NIRS with machine learning can predict fecal starch content (FSC) in beef cattle. • Measuring FSC is crucial for optimizing feed utilization and minimizing feed loss. • Preprocessing methods and regression models for FSC were systematically optimized. • Lasso.R identified key starch absorption peaks, enhancing prediction accuracy. • The method improves feed efficiency by accurately predicting FSC. Fecal starch content (FSC) is an important indicator of feed efficiency and productivity of beef cattle, as it reflects the digestibility of starch in the diet. High FSC indicates inefficient feed utilization, lower productivity, and environmental risks. However, chemical analyses for measuring FSC are time-consuming, making it challenging to conduct large-scale surveys on farms. This study aimed to develop a rapid and accurate method for predicting FSC in beef cattle using near-infrared spectroscopy (NIRS) and machine learning, with a focus on optimizing preprocessing methods and regression models. Fecal samples were collected from 196 fattening cattle, and near-infrared (NIR) spectra and actual FSC were obtained. The preprocessing and modeling were optimized using two different methods. Method 1 focused on minimizing the root mean square error (RMSE) for the calibration set, while Method 2 aimed to reduce the impact of neutral detergent fiber (NDF), a common interfering substance, by minimizing RMSE in a preprocessing selection set with similar starch content but different NDF levels. The Lasso regression (Lasso.R) with the preprocessing selected by Method 2 showed the best performance on the external validation set, with the coefficient of determination of 0.90, the RMSE of 1.54 % for dry matter, and the RPD of 3.09. The regression coefficients of Lasso.R revealed that the wavelength ranges (1770.5–1773.0 nm and 1827.5–1833.5 nm), which correspond to the absorption peaks of starch, were important for prediction. These results demonstrate that NIRS can provide a rapid alternative to chemical analysis, enabling large-scale monitoring for sustainable livestock management. Starch ; Content (measure theory) ; Artificial intelligence ; Infrared ; Feces ; Near-infrared spectroscopy ; Infrared spectroscopy ; Water content ; Food science ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Materials science ; Engineering ; Chemistry ; Biology ; Physics ; Optics ; Ecology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Health
Authors: Min‐Seok Park | Mohammad Akbar Faqeerzada | Sung Hyuk Jang | Hangi Kim | Hoonsoo Lee | Geonwoo Kim | Young-Son Cho | Woon-Ha Hwang | Moon S. Kim | Insuck Baek | Byoung–Kwan Cho
Source: Plants (article, 2025-10-02)
Field: Chemistry — Analytical Chemistry

Detection of Abiotic Stress in Potato and Sweet Potato Plants Using Hyperspectral Imaging and Machine Learning As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (<i>Solanum tuberosum</i> L.) and sweet potato (<i>Ipomoea batatas</i> L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar's leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90-98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90-96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Hyperspectral imaging ; Principal component analysis ; Support vector machine ; Linear discriminant analysis ; Abiotic stress ; Pattern recognition (psychology) ; Cultivar ; Abiotic component ; Crop Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Kooffreh Okon | Ikechukwu Ignatius Ayogu | Taofik Oladimeji Azeez | C. O. Akalezi
Source: World Journal of Materials Science and Technology (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Modelling of Corrosion Inhibition of Mild Steel in Marine Environment Using Epoxy-Nickel Oxide Nanocomposite Coatings The corrosion inhibition of mild steel in 3.5 wt. % NaCl in the absence and presence of epoxy coatings containing NiO nanoparticles with concentrations of 1.0, 2.0, 3.0 and 5.0 wt. % respectively was studied using the gravimetric technique for a duration of 60 days at room temperature and varying temperatures ranging from 30 to 60°C for 5 hours. The Nickel oxide nanoparticles with average particle size was 23 nm were synthesized by the chemical precipitation technique followed by calcination in a muffle furnace for 3 hours at a temperature of 300°C. Results from the study reveal that epoxy-Nickel oxide nanocomposite coatings are effective green corrosion inhibitors for mild steel in 3.5 wt. % NaCl under different operating conditions and at temperatures within the range of 30 to 60°C. A predictive model based on the Artificial Neural Network (ANN) was developed to study the relationship between the input variables (exposure time, inhibitor concentration and Temperature) and output variables (Corrosion Rate and Inhibition Efficiency). The ANN model was based on the Multilayer Perceptron algorithm with input layer comprising of 3 factors and 23 units. Hyperbolic tangent was used as the activation function for the hidden layer which was made up of 3 units. The output layer with two dependent variables was made up of 2 units. Corrosion test data obtained from 80 experimental runs were successfully modelled using ANN with minimal errors. 56 cases corresponding to 70% of test data were used for training the network and 24 cases corresponding to 30% of test data was used for testing the efficacy of the network. The model had sum of squares error of 0.981, average overall relative error of 0.018 for the training component and values of 3.190 and 0.043 for the sum of squares error and average overall relative error respectively for the testing component. Epoxy ; Nanocomposite ; Materials science ; Corrosion ; Nickel ; Oxide ; Metallurgy ; Composite material Corrosion Behavior and Inhibition ; Gas Sensing Nanomaterials and Sensors ; Concrete Corrosion and Durability