jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tom Pan | Evan Dramko | Mitchell D. Miller | Anastasios Kyrillidis | G.N. Phillips
Source: Acta Crystallographica Section D Structural Biology (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Completion of partial structures using Patterson maps with the <i>CrysFormer</i> machine-learning model Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not directly incorporate the experimental measurements, such as X-ray crystallographic diffraction data. To this end, we explore an approach that more tightly couples these traditional crystallographic and recent ML-based methods by training a hybrid 3D vision transformer and convolutional network on inputs from both domains. We make use of two distinct input constructs: Patterson maps, which are directly obtainable from crystallographic data, and `partial structure' template maps derived from predicted structures deposited in the AlphaFold Protein Structure Database with subsequently omitted residues. With these, we predict electron-density maps that are then post-processed into atomic models through standard crystallographic refinement processes. Introducing an initial data set of small protein fragments taken from Protein Data Bank entries and placing them in hypothetical crystal settings, we demonstrate that our method is effective at both improving the phases of the crystallographic structure factors and completing the regions missing from partial structure templates, as well as improving the agreement of the electron-density maps with the ground-truth atomic structures. Set (abstract data type) ; Crystal structure ; Diffraction ; Data set ; Protein Data Bank ; Training set ; Protein structure Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: M. Arifuzzaman
Source: IET conference proceedings. (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Application of ML: the sustainable advantages of sulfur-based binders in pavement construction Elemental sulfur and sulfur-based binders present a cost-effective and sustainable alternative to traditional asphalt binders in pavement construction. Historically, elemental sulfur has successfully replaced 30 to 40% of the binder's weight, enhancing pavement performance and reducing dependency on petroleum-based binders. Sulfur's abundance and lower cost, being a byproduct of petroleum refining and natural gas processing, make it an attractive option. Sulfur integration improves resistance to rutting and cracking, extends pavement life, and reduces maintenance costs, especially in regions with extreme weather conditions. Environmentally, sulfur-based binders decrease the carbon footprint of pavement construction by reducing reliance on fossil fuels and utilizing industrial waste. This study, conducted at KFU’s transportation engineering lab, evaluated the influence of binder type, binder content, sulfur %, mixing method, and aging on maximum load capacity using four machine learning models—LWL, DecisionStump, RandomForest, and RandomTree. LWL outperformed others, demonstrating superior predictive accuracy and error performance. The ReliefF ranking filter identified sulfur % as the most influential attribute, emphasizing the need for optimizing sulfur content, mixing methods, binder content, and considering aging effects to maximize material strength and durability. Sulfur ; Environmental science ; Forensic engineering ; Construction engineering ; Civil engineering ; Engineering ; Materials science ; Metallurgy Structural mechanics and materials
Authors: Yongfeng Huang | Songlin Yao | Hongjian Chen | Pan Li | Kun Wang | Jun Chen | Huiqiu Deng | Wangyu Hu
Source: Journal of Nuclear Materials (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

An atomistic study of γ → γ0 → α’’ martensitic transformation in uranium-6wt % niobium Niobium ; Uranium ; Diffusionless transformation ; Materials science ; Metallurgy ; Transformation (genetics) ; Martensite ; Chemistry ; Microstructure Nuclear Materials and Properties ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: M. Brambilla | Andrea Lazzari | Simone Giovinazzo | F.M. Tangorra | Aldo Calcante | Carlo Bisaglia
Source: Journal of Agricultural Engineering (article, 2025-03-04)
Field: Chemistry — Analytical Chemistry

Operational evaluation of an optical sensor for the automatic in-line estimation of total mixed ration fibre length and particle size in a mixing wagon The optimal management of cattle nutrition promotes animal health and welfare, increases livestock farms’ productivity and competitiveness, and enhances environmental sustainability practices. Animal feeding operations play a crucial role as many factors can drive the theoretical ration formulated by nutritionists far from the one the animals ingest. Precision feeding technologies (e.g., NIR sensors on the milling cutter of the chopper-mixer wagon; computer vision systems installed in the mixing tank) may allow for accurate and real-time analysis of the chemical and physical properties of total mixed ration (TMR) ingredients, reducing errors during its preparation and distribution. This work compares the physical quality and the length of the fibre of the TMR resulting from the chopping-mixing process of a conventional mixing wagon, one machine-learning-assisted mixing wagon and an automatic feeding system under actual operating conditions. Between October 2021 and November 2022, TMR sampling occurred on four dairy farms and one fattening bulls farm in Northern Italy, specifically in the Brescia, Cremona, and Mantua districts. TMR samples underwent particle size analysis using the Penn State Particle Separator (PSPS) method and, once in the laboratory, moisture analysis and fibre length measurement. Concerning TMR particle size analysis, the PSPS method revealed that the machine learning-assisted mixing wagon provided TMR with physical features comparable to that from ordinarily run mixing wagons. At the same time, the automatic feeding system resulted in TMR with finer particle size, following the farmers’ choice not to use long-stemmed forages. Regarding fibre length, only the TMR resulting from the operator-based mixing wagon aligned with the targeted fibre length of 5 cm, while the AFS and the ML-assisted mixing resulted in higher fibre lengths. Overall, the use of computer vision (CV) systems is helpful for the consistency of the TMR and represents a valuable solution for animal farming, particularly when employing low- or inexperienced operators. Further studies are, however, needed to improve the training of the with elements that can replicate the operator experience. Mixing (physics) ; Line (geometry) ; Particle size ; Optical fiber ; Mathematics ; Statistics ; Engineering ; Veterinary medicine ; Biomedical engineering ; Physics ; Telecommunications ; Medicine Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Sensor Technology and Measurement Systems
Authors: Ali Pourian | Sina Maghsoudy | Sherif Farag | Sajjad Habibzadeh
Source: Environmental Science and Pollution Research (article, 2025-10-25)
Field: Chemistry — Inorganic Chemistry

Bridging classical and intelligent models: machine learning-guided adsorption isotherms for tailored acetone capture on porous carbon Adsorption ; Acetone ; Porosity ; Bridging (networking) ; Correlation coefficient ; Activated carbon ; BET theory ; Specific surface area Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Nisha Geng | Giacomo Joseph Scilla | Eva Zurek
Source: Materials Today Physics (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Theoretical predictions of MB<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si94.svg" display="inline" id="d1e478"><mml:msub><mml:mrow/><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msub></mml:math>N<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si94.svg" display="inline" id="d1e486"><mml:msub><mml:mrow/><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msub></mml:math>: Atom-stuffed boronitride clathrate cages derived from the high-pressure superhydrides Scalable Vector Graphics ; Materials science ; Artificial intelligence ; Computer science ; Computer graphics (images) ; World Wide Web Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials ; Advanced ceramic materials synthesis
Authors: Frederik Doktor S. Simonsen | Niels M. Mikkelsen | Signe Kyrkjebø | Prashant Srivastava | Richard Balog | Bjørk Hammer | Karina Morgenstern | Liv Hornekær
Source: The Journal of Physical Chemistry C (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

Water Nucleation via Transient Bonds to Oxygen-Functionalized Graphite We present a study on the initial stages of ice growth on pristine and oxygen-functionalized highly oriented pyrolytic graphite (O-HOPG), combining low-temperature scanning tunneling microscopy (LT-STM) and machine-learning structural searches. LT-STM images show that oxygen atoms act as nucleation sites for ice growth and that the size, structure, and porosity of the nanometer-sized ice clusters depend strongly on the growth temperature. Machine learning-assisted structural searches and first-principles energy calculations confirm that clusters of water molecules are likely to bind to chemisorbed oxygen atoms through hydrogen bonding. During the early stages of the cluster growth, clusters of water molecules are likely to be immobilized by binding to more than one chemisorbed oxygen atom through hydrogen bonding. However, the energy gain by hydrogen bond formation of a molecule, upon incorporation into smaller clusters only bound to a single oxygen atom, is large enough to induce cluster diffusion and favor the growth of larger ice clusters. Our results demonstrate that the mobility of water molecules is significantly lowered in the presence of defects on the surface. The observed lower mobility on defected carbon presented here offers an enhanced understanding of macroscopic anti-icing properties observed for functionalized HOPG under ambient conditions and provides insight into the early stages of ice growth on dust grain surfaces in interstellar space. Nucleation ; Transient (computer programming) ; Graphite ; Oxygen ; Materials science ; Chemical engineering ; Chemical physics ; Polymer chemistry ; Chemistry ; Organic chemistry ; Composite material ; Computer science ; Engineering Graphene research and applications ; Advancements in Battery Materials ; Fiber-reinforced polymer composites
Authors: Sisi Tang | Ruili Li | Yu Cheng | S HUANG | Q. Liu | X. Chen
Source: Journal of Hazardous Materials (article, 2025-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simultaneous identification and detection of five antibiotics using a machine learning-enhanced glass capillary SERS sensor Capillary action ; Detection limit ; Raman scattering ; Fingerprint (computing) ; Multiplexing ; Capillary electrophoresis ; Analytical technique ; Principal component analysis Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jothi Arunachalam Solairaju | R. Saravanan | T. Sathish | Bashar Tarawneh | Vinuja Gurumoorthy | Johnson Santhosh Antony | A. Anderson
Source: Engineering Reports (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

Development and Validation of <scp>ANN</scp> Models for Water Absorption in Sawdust and Kenaf Fiber‐Reinforced Polystyrene Composites ABSTRACT This research paper involved modeling the water absorption behavior of polystyrene (PS) composites with sawdust and kenaf fiber (KF) reinforcement using the Artificial Neural Network (ANN) method. The composites were made by manual mixing combined with the hand lay‐up process at room temperature (25°C ± 2°C) and cured in an open mold over 7 days at ambient temperature. The water absorption measurements were done according to the ASTM D1037‐99. The findings were that the water uptake was enhanced by filler content as well as immersion duration in the sawdust composite and KF. The ANN model also had good accuracy; the coefficients of determination ( R 2 ) in all the ANN models were more than 0.98 in all the training, validating, and test sets of both types of materials. Also, values of root mean square error (RMSE) were low (less than 1 wt%), indicating that this model was very accurate in forecasting the behavior of water absorption. Parity plots indicated that there was a good balance of the performance of the predictions, which captured the low and high values of absorption. Moreover, the p value was lower than 0.05, which showed ANOVA results are statistically significant. Sawdust ; Kenaf ; Absorption of water ; Composite number ; Polystyrene ; Bamboo ; Fiber Natural Fiber Reinforced Composites ; Engineering and Agricultural Innovations ; Textile materials and evaluations
Authors: Nayara Carral-Sainz | Toraya Fernández-Ruiz | Jorge Íñiguez | Javier Junquera | Pablo García‐Fernández
Source: Physical review. B./Physical review. B (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Systematic generation of electron models for second-principles density functional theory methods We present a systematic, quasiautomated methodology for generating electronic models in the framework of second-principles density functional theory (SPDFT). This approach enables the construction of accurate and computationally efficient models by deriving all necessary parameters from first-principles calculations on a carefully designed training set. A key feature of our method is the enforcement of space-group symmetries, which reduces both the number of independent parameters and the required computational effort. The formalism includes improved treatments of one-electron Hamiltonians, electron-lattice coupling, through both linear and quadratic terms, and electron-electron interactions, enabling accurate modeling of structural and electronic responses. We apply the methodology to ${\mathrm{SrTiO}}_{3}$, LiF, and metallic Li materials that present a wide variety of properties as corresponds to a transition-metal perovskite, a wide-band-gap ionic insulator, and a free-electron metal, respectively. In all cases, the resulting models reproduce DFT reference data with high fidelity across various atomic configurations and charge states. Our results validate the robustness of the approach and highlight its potential for simulating complex phenomena such as polarons and excitons. This work lays the foundation for extending SPDFT to real-time simulations of optoelectronic properties and further integration with machine-learning methods. Density functional theory ; Formalism (music) ; Robustness (evolution) ; Quadratic equation ; Polaron ; Electronic structure ; USable ; Hybrid functional Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Peng Ai | Shuwei Tang | Qingshun Quinn Li | Da Wan | Xiaodong Li | Wanrong Guo | Tuo Zheng | Xiuling Qi | Shu‐Lin Bai
Source: Journal of Rare Earths (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Strong anharmonic phonon scattering induced low lattice thermal conductivity in La2Sn2Se6 monolayer with high thermoelectric performance Anharmonicity ; Condensed matter physics ; Phonon ; Thermoelectric effect ; Monolayer ; Phonon scattering ; Thermal conductivity ; Scattering ; Materials science ; Thermoelectric materials ; Lattice (music) ; Thermal ; Physics ; Nanotechnology ; Thermodynamics ; Quantum mechanics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Anjali Gaur | Nikhil V. S. Avula | Sundaram Balasubramanian
Source: Journal of Chemical Theory and Computation (article, 2025-08-07)
Field: Chemistry — Spectroscopy

Decoding Temperature-Dependent Conformer Populations and Dipole Moments in Liquid Ethylene Glycol with Classical and Machine-Learned Potentials Delineating the conformer populations in neat liquid ethylene glycol (EG) has been challenging as experiments lack sufficient resolution, while simulations suffer from low accuracy and/or high computational demand. Ab initio MD (AIMD) simulations of liquid EG at ambient conditions have reported that about 20% of the molecules have their central (OCCO) dihedral in the <i>trans</i> conformation. Yet, adequate conformational sampling is an issue with AIMD simulations, given the limited duration of the trajectory. We describe the development of deep neural network-based potentials (DP) fitted to density functional theory data and confirm the <i>trans</i> population to be 22% at ambient conditions. The DP models provide well-converged structural properties and conformer populations over the entire temperature range of liquid EG. An increase in the population of <i>trans</i> conformers was observed with increasing temperature and is rationalized on two grounds: the increasingly similar coordination environments of <i>trans</i> and <i>gauche</i> conformers, and a decrease in the polarity of the liquid with temperature. Despite the latter, the <i>trans</i> population saturates to a value of 24% at 400 K as rising intermolecular distances favor the <i>gauche</i> conformation with intramolecular hydrogen bonding. Wannier Centroid analysis carried out using a Deep Wannier model reveals a mean molecular dipole moment in liquid EG of 3.3 D at ambient conditions, which decreases to a value of 3.0 D at 463 K. Conformational isomerism ; Dipole ; Intramolecular force ; Ab initio ; Population ; Dihedral angle ; Ab initio quantum chemistry methods ; Intermolecular force ; Computational chemistry ; Chemistry ; Chemical physics ; Hydrogen bond ; Materials science ; Molecular physics ; Molecule ; Stereochemistry ; Organic chemistry Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Jeongjae Lee | Yu Jia | Xiangzi Li | Dae Jong Kim | Sungkwon Park
Source: LWT (article, 2025-08-11)
Field: Chemistry — Analytical Chemistry

Comprehensive non-destructive assessment of beef quality: Focus on textural feature analysis and tenderness using AI and hyperspectral imaging This study developed a non-destructive method to predict beef tenderness using hyperspectral imaging (HSI), near-infrared (NIR) spectroscopy, and machine learning (ML). A total of 159 beef loin samples were analyzed to quantify moisture and collagen content using partial least squares regression ( R 2 = 0.973 and 0.953, respectively). Texture features were extracted from HSI-based chemical maps using gray-level co-occurrence matrix (GLCM) analysis. These features, combined with chemical indicators, were used to classify tenderness into three categories based on Warner-Bratzler Shear Force. The Random Forest model achieved the highest accuracy (F1-score = 1.00), followed by SVM (97 %). The proposed method offers accurate and efficient tenderness assessment suitable for large-scale industrial application. • The paper presents the Comprehensive AI (CAI) framework , designed to connect narrow AI, AGI, and human-like thinking through modular subsystems inspired by cognitive architectures. • Modular & Hierarchical : CAI is built like the human mind, with separate components for perception, reasoning, memory, learning, and self-monitoring, enabling it to handle a wide range of tasks. • Human-Inspired : It borrows ideas from neuroscience and cognitive science, incorporating abilities like attention, episodic memory, and self-reflection for more adaptable and context-aware AI. • Explainable & Ethical : CAI prioritizes transparency, trustworthy reasoning, and alignment with human values—key for safe AGI development. • Real-World Applications : This approach could improve AI safety and robustness in healthcare, education, robotics, and scientific research, boosting innovation while managing risks. Tenderness ; Hyperspectral imaging ; Feature (linguistics) ; Focus (optics) ; Quality (philosophy) ; Artificial intelligence ; Quality assessment ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Engineering ; Food science ; Chemistry ; Evaluation methods ; Optics ; Reliability engineering Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Huadong Yang | Yuxiao Zhu | Harold M. H. Chong | Ruomeng Huang
Source: Applied Thermal Engineering (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Accurate and efficient design and optimization of thermoelectric cooler via machine learning technologies • ANN predicts TEC performance 187,000 × faster than COMSOL simulations. • Neural network achieves > 99.9 % accuracy in temperature prediction. • ANN–GA framework optimizes TEC geometry and current within 77 s. • Dynamic current control maintains target T C while reducing power use. • Approach enables real-time optimization for adaptive thermal management. The increasing demand for efficient thermal management in applications such as on-chip hotspot cooling and battery systems calls for high-performance thermoelectric coolers (TECs) that can be rapidly optimized for varying thermal loads. In this work, we propose a data-driven framework that integrates a high-fidelity artificial neural network (ANN) with a genetic algorithm (GA) to enable fast and accurate prediction and optimization of TEC performance across a broad design and operating range. This approach is particularly valuable in real-world scenarios where input power fluctuates over time and maximizing TEC efficiency is critical. Unlike previous studies, which primarily relied on ANN models for performance prediction alone, the present work extends their functionality to include efficient configuration optimization within significantly shorter timescales. The ANN was trained on 20,000 data points generated from validated COMSOL Multiphysics® simulations under diverse conditions, achieving a prediction accuracy exceeding 99.9 %. Once trained, the ANN performs performance predictions over 180,000 × faster than COMSOL, enabling high-throughput simulation, sensitivity analysis, and real-time optimization. Coupling the ANN with a GA, we optimized TEC geometry and operating current to achieve minimum cold-side temperatures under varied boundary conditions. The ANN–GA framework shows excellent agreement with COMSOL-based optimization while achieving orders-of-magnitude gains in computational efficiency. Practical scenarios with dynamically varying heat inputs and temperature thresholds were also explored in which the system successfully adapts to changing loads, identifying optimal currents in under 33 s per case, demonstrating strong potential for real-time adaptive thermal management. TEC ; Thermoelectric cooling ; Artificial neural network ; Thermal ; Sensitivity (control systems) ; Performance prediction ; Optimization problem ; Computation Advanced Thermoelectric Materials and Devices ; Parallel Computing and Optimization Techniques ; Ferroelectric and Negative Capacitance Devices