jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lars Dammann | Richard Kohns | Patrick Huber | Robert H. Meißner
Source: Journal of Chemical Theory and Computation (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Maximum Entropy-Mediated Liquid-to-Solid Nucleation and Transition Molecular dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD simulations but can be difficult to prepare due to insufficient atomistic information. At the same time, wide-angle X-ray scattering (WAXS) measurements can determine the radial distribution function (RDF) of atomic structures. However, the interpretation of RDFs is often challenging. Here, we present an algorithm that can bias MD simulations with RDFs by combining the information on the MD atomic interaction potential and the RDF under the principle of maximum relative entropy. We show that this algorithm can be used to adjust the RDF of one liquid model, e.g., the TIP3P water model, to reproduce the RDF and improve the angular distribution function (ADF) of another model, such as the TIP4P/2005 water model. In addition, we demonstrate that the algorithm can initiate crystallization in liquid systems, leading to both stable and metastable crystalline states defined by the RDF, e.g., crystallization of water to ice and liquid TiO<sub>2</sub> to rutile or anatase. Finally, we discuss how this method can be useful for improving interaction models, studying crystallization processes, interpreting measured RDFs, or training machine-learned potentials. Metastability ; Crystallization ; RDF Schema ; Nucleation ; Chemical physics ; Radial distribution function ; Statistical physics ; Molecular dynamics ; RDF ; Water model ; Materials science ; Computer science ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry ; Artificial intelligence ; Quantum mechanics Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Phase Equilibria and Thermodynamics
Authors: I. D. Shonichev | Константин Валерьевич Михайловский | В С Тынченко | Вячеслав Владимирович Богачёв
Source: Industrial processes and technologies (article, 2025-06-27)
Field: Materials Science — General Materials Science

Нейросетевое прогнозирование параметров пропитки заготовок деталей из полимерных композиционных материалов Производство крупногабаритных конструкций из полимерных композиционных материалов (ПКМ) по технологии вакуумной инфузии обладает высокой экономической рентабельностью в сравнении с другими из-за отсутствия необходимости использования дорогостоящего промышленного оборудования наподобие автоклавов, вакуумных термошкафов и др. Для повышения качества крупногабаритных конструкций из ПКМ крайне важно осуществлять контроль вязкости связующего, температуры оснастки и связующего, мест его подачи и откачки. Из-за сложности происходящих процессов при выборе технологических параметров велика доля интуитивно-эмпирических приёмов, которые могут приводить к возникновению технологического брака конструкций из полимерных композиционных материалов. В связи с чем необходимо применение методов математического моделирования и создание достоверных методик контроля качества пропитки в режиме реального времени. Цель работы — разработка методики прогнозирования параметров процесса пропитки полимерными связующими заготовок ПКМ на основе параметрического моделирования и машинного обучения для повышения качества деталей. Приведены результаты параметрического моделирования пропитки заготовок деталей из ПКМ, изложены приёмы автоматизации и результаты моделирования для применения методов машинного обучения. Выдвинуты ключевые метрики точности для разрабатываемой прогнозирующей модели. Предложена масштабируемая прогнозирующая модель на основе искусственных полносвязных нейронных сетей прямого распространения. Приведены результаты оптимизации параметров сенсорной системы и гиперпараметров нейронной сети. Исследовано влияние размера массива данных на метрики точности прогнозирующей модели. Сделаны выводы о практической реализуемости предлагаемой прогнозирующей модели. Political science Material Properties and Applications ; Fiber-reinforced polymer composites ; Smart Materials for Construction
Authors: Stephen Quirk | Gwendell M. Thomas | Lindsay M. Wallace | Mary A. Woody | Mary Elizabeth Peek | Raquel L. Lieberman
Source: Protein Science (article, 2025-07-21)
Field: Materials Science — Biomaterials

A multidomain hydrolase from the thermophile <i>Thermanaeromonas toyohensis</i> degrades high molecular mass forms of polyhydroxybutyrate The use of polyhydroxybutyrate (PHB) bioplastics has the potential to create a circular and sustainable recycling plastic system. Here, we focus on PHB degradation and characterize the multidomain PHB hydrolyzing enzyme from the extremophile Thermanaeromonas tohohensis. The catalytic domain fold is conserved across a diverse taxonomic range, but the C-terminal domains are unique amongst PHBases and instead share similarities with carbohydrate degrading enzymes from human gut microbiota. The enzyme has a preference for higher molecular weight PHB. Hydrolysis of PHB films correlates with the ability of the enzyme to bind those films, most likely driven by its C-terminal substrate binding domain. The enzyme denatures in a highly cooperative manner over a short temperature range, and the kinetics of folding/unfolding are well described by Eyring theory. Finally, the study of this enzyme has been incorporated into an undergraduate biochemistry teaching laboratory that focuses on "real life" research problems with relevance to industry. Polyhydroxybutyrate ; Thermophile ; Hydrolase ; Chemistry ; Molecular mass ; Biochemistry ; Computational biology ; Biophysics ; Biology ; Enzyme ; Genetics ; Bacteria biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Production and Characterization
Authors: Lakshmanji Verma | Ken A. Dill
Source: ArXiv.org (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

Statistical mechanical theory of liquid water Water is an unusual liquid. Its thermophysical properties are non-monotonic with temperature T and pressure p. It's not been known how water's behaviors are encoded in its molecules. We give a statistical physics model, Cage Water, which assumes three bonding states: van der Waals, pairwise hydrogen bonding, and multi-body cooperative caging hydrogen bonds. The model is analytical, so very fast to compute, yet it gives excellent agreement with extensive pT experiments. Through readily interpretable substates, Cage Water explains water's liquid anomalies -- including its controversial liquid-liquid supercooling transition -- as simple switchovers among the three bonding types. Supercooling ; Liquid water ; Hydrogen bond ; Simple (philosophy) ; Pairwise comparison ; Water model ; Specific heat Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Quantum, superfluid, helium dynamics
Authors: Ankitkumar J. Patel | Vishant C. Patel | Darshan S. Patel | Amitkumar B. Dholakia | Mohit Agrawal | Magdi E. A. Zaki
Source: Journal of Biochemical and Molecular Toxicology (article, 2025-11-01)
Field: Chemistry — Organic Chemistry

Synthesis, and Computational Insights Into Quinoline Hydrazide Derivatives as Potential Antimicrobial Agents A series of novel quinoline hydrazide derivatives (4a-4o) were synthesized and characterized by mass spectrometry, ¹H NMR, and ¹³C NMR. The compounds exhibited notable antibacterial and antifungal activity. Among gram-positive strains, compounds 4f and 4j showed potent inhibition against Bacillus subtilis (MIC = 0.033 and 0.015 mM), while compound 4o was highly active against Streptococcus pneumoniae (MIC = 0.139 mM). Against gram-negative bacteria, compounds 4d and 4n were most effective against E. coli (MIC = 0.150 and 0.112 mM). Molecular docking and 100 ns molecular dynamics simulations revealed stable binding of compounds 4d and 4n with E. coli DNA gyrase B, primarily through hydrogen bonding and π-cation interactions. DFT studies supported their electronic reactivity, and in silico ADMET profiling predicted high intestinal absorption and favorable pharmacokinetic properties. Overall, the results highlight quinoline hydrazide derivatives as promising candidates for antimicrobial drug development. Quinoline ; Hydrazide ; In silico ; Bacillus subtilis ; DNA gyrase ; Antimicrobial ; Docking (animal) ; AutoDock ; Hydrogen bond Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Quinazolinone synthesis and applications
Authors: Juntae Kim | Tae-Gyun Rho | Eun-Sung Park | Oh-Tae Kwon | Sang-Joon Lee | Mohammad Akbar Faqeerzada | Rahul Joshi | Hanim Zuhrotul Amanah | Byoung–Kwan Cho
Source: Research Square (preprint, 2025-10-12)
Field: Chemistry — Analytical Chemistry

Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system Hyperspectral imaging ; Moisture ; Water content ; Mixing (physics) ; Homogenization (climate) ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Julia Chrząstowska | Tomasz Sokalski | Ekaterina Korotkova | Johan Bobacka | Anna Sundberg | Chunlin Xu
Source: Holzforschung (article, 2025-06-09)
Field: Chemistry — Analytical Chemistry

Machine learning-assisted spectrometric method for pulp extractive analysis based on model pulps Abstract Extractives in wood, despite being vital in plant survival in adverse environmental conditions, can cause issues in pulping and papermaking, leading to significant financial losses within the forest industry. Conventional analysis is time consuming due to the challenging sample preparation, data interpretation and complex nature of extractives. There is a clear need for the development of a quick, non-destructive method for on-line pulp extractives monitoring. In this work, a machine learning-supported procedure for classification and prediction of extractives based on near infrared (NIR) and Raman spectroscopies was proposed. To avoid the influence of many variables, the method was developed and validated using a model compound approach, where cellulose was spiked with model extractives compounds. The accuracy of sample classification depending on the extractive added was 92.4 % based on NIR. The accuracy of classification of six samples containing different concentrations of the model compound using NIR data was 89.5 %. Partial least squares calibration model applied to pretreated NIR spectra yielded R 2 and root standard error of 0.78 and 0.35, respectively This means that the method could be used for non-selective quick estimation of extractive content with potential application to forest industry in process and fiber quality control in pulp and paper. Pulp (tooth) ; Pulp and paper industry ; Industrial chemistry ; Chromatography ; Chemistry ; Process engineering ; Biochemical engineering ; Materials science ; Engineering ; Dentistry Spectroscopy and Chemometric Analyses ; Lignin and Wood Chemistry ; Water Quality Monitoring and Analysis
Authors: Samira Nascimento Antunes | Madalena de Oliveira Barbosa Divino | Luana dos Santos Cordeiro | Fernanda Aguiar | Marcelo Tsuguio Okano
Source: IAES International Journal of Artificial Intelligence (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Enhanced classification of aromatic herbs using EfficientNet and transfer learning &lt;span lang="EN-US"&gt;Herbs have long been used for culinary and medicinal purposes, as well as in religious rituals, due to their essential oils and aromatic properties. However, distinguishing between aromatic and medicinal herbs based on visual characteristics alone can be challenging. With recent advances in computer vision, plant identification from images has seen significant growth, offering promising applications in several domains. This article aims to evaluate the classification of aromatic herbs using the EfficientNet convolutional neural network (CNN) technique with transfer learning. The methodology used is experimental research, systematically manipulating variables to observe their effects on the object of study. The researcher plays an active role in this process, rather than being a passive observer. Based on the results and the literature review, it is evident that the objective of this research was achieved, as despite the opportunities for improvement in training to achieve accuracy above 0.8, it was possible to evaluate the classification of aromatic herbs using EfficientNet CNN through the transfer learning technique.&lt;/span&gt; Convolutional neural network ; Transfer of learning ; Aromatic plants ; Medicinal herbs ; Identification (biology) ; Object (grammar) ; Chinese herbs Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Traditional Chinese Medicine Studies
Authors: Harshit Gouri | Geetika Bhalla | Charu Kumar | Meenu Sarbhai Srivastava | Vinita Narula
Source: Asian Journal of Chemistry (article, 2025-12-31)
Field: Materials Science — Biomaterials

Sustainable Polymer–Nanoparticle Nanocomposites: A Comparative Review of PMMA, PLA, PVA and Chitosan Systems The rising demand for eco-friendly, high-performance materials has accelerated interest in polymer–nanoparticle nanocomposites (PNNCs) as sustainable alternatives to conventional plastics. This review presents an integrated comparison of PNNCs based on polymethyl methacrylate (PMMA), polylactic acid (PLA), polyvinyl alcohol (PVA) and chitosan matrices reinforced with inorganic nanoparticles such as TiO2, ZnO, Ag, hydroxyapatite (HA) and Fe3O4. Nanofiller incorporation typically enhances the tensile strength by 20-60%, thermal stability by 10-25ºC and antimicrobial efficiency up to &gt;99% reduction in bacterial load, depending on matrix–nanoparticle compatibility. For example, PLA–HA nanocomposites show up to 40% improvement in modulus and accelerated bioactivity for orthopaedic applications, whereas PMMA–TiO2 composites maintain optical clarity while providing enhanced UV-shielding and scratch resistance. A sustainability framework is applied to compare biodegradability and recyclability, highlighting PLA and chitosan as circular-economy-aligned matrices, while PMMA and PVA require responsible end-of-life strategies. Emphasis is placed on green synthesis routes, nanoparticle leaching behaviour and life-cycle considerations including environmental exposure risks. Challenges such as achieving stable nanoparticles dispersion, tuning interfacial chemistry and minimizing ecotoxicity are critically evaluated. The novelty of this work lies in bridging functional performance with life-cycle sustainability, directly aligning PNNC design principles with relevant UN Sustainable Development Goals. Chitosan ; Nanocomposite ; Polylactic acid ; Ultimate tensile strength ; Thermal stability ; Nanoparticle ; Polyvinyl alcohol biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer Nanocomposites and Properties
Authors: Polina Avdiunina | Shamieraah Jamal | Filipp Gusev | Olexandr Isayev
Source: Journal of Chemical Information and Modeling (article, 2025-09-03)
Field: Chemistry — Spectroscopy

All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models Proteochemometric models (PCMs) are used in computational drug discovery to employ both protein and ligand representations jointly for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often serving as a basis for scoring functions, rigorous evaluation standards have not always been consistently applied. In this study, using kinase-ligand bioactivity prediction as a model system, we highlight the critical roles of data set curation, permutation testing, class imbalances, and various data splitting strategies for mitigating plausible data leakage and embedding quality in determining model performance. Our findings indicate that data splitting and class imbalances are the most critical factors affecting PCM performance, emphasizing the challenges in the generalizing ability of ML/DL-PCMs. We evaluated various protein-ligand descriptors and embeddings, including those augmented with multiple sequence alignment information. However, permutation testing consistently demonstrated that protein embeddings contributed minimally to PCM efficacy. This study advocates for the adoption of stringent evaluation standards to enhance the generalizability of models to out-of-distribution data and improve benchmarking practices. Gold standard (test) ; Data science ; Computer science ; Mathematics ; Statistics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: V. A. Ulitko | D. N. Yasinskaya | S. A. Bezzubin | A. A. Koshelev | Yu. D. Panov
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

Predicting parameters of a model cuprate superconductor using machine learning The computational complexity of calculating phase diagrams for multi-parameter models significantly limits the ability to select parameters that correspond to experimental data. This work presents a machine learning method for solving the inverse problem - forecasting the parameters of a model Hamiltonian for a cuprate superconductor based on its phase diagram. A comparative study of three deep learning architectures was conducted: VGG, ResNet, and U-Net. The latter was adapted for regression tasks and demonstrated the best performance. Training the U-Net model was performed on an extensive dataset of phase diagrams calculated within the mean-field approximation, followed by validation on data obtained using a semi-classical heat bath algorithm for Monte Carlo simulations. It is shown that the model accurately predicts all considered Hamiltonian parameters, and areas of low prediction accuracy correspond to regions of parametric insensitivity in the phase diagrams. This allows for the extraction of physically interpretable patterns and validation of the significance of parameters for the system. The results confirm the promising potential of applying machine learning to analyze complex physical models in condensed matter physics. Monte Carlo method ; Inverse ; Hamiltonian (control theory) ; Parametric statistics ; Inverse problem ; Phase diagram ; Phase (matter) ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Physics of Superconductivity and Magnetism
Authors: Jiaruo Yan | Ioannis Katsantonis | Savvas Papamakarios | Panagiotis Konstantakis | Michail Loulakis | Thomas Koschny | Maria Farsari | Stelios Tzortzakis | Maria Kafesaki
Source: Nanophotonics (article, 2025-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D‐printed mirror‐less helicity preserving metasurface “mirror” for THz applications Stimulated by seminal works on generalized reflection and refraction laws, metasurfaces have evolved to a highly promising research direction, as they allow a multitude of different functionalities by optically thin wave-control elements/structures. Among them, structures functioning in the low THz regime are of extensive research interest, due to their high potential in communication and sensing applications. In this paper we propose a simple THz metasurface design that exhibits ideally perfect cross-polarized reflection for linear polarization and perfect helicity-preserving reflection with a geometric (Pancharatnam-Berry) phase for circular polarization, without the presence of any back-reflector. Numerical calculations demonstrating the structure response are justified by analytical models, which provide physical insights on this response. The designed metasurfaces are fabricated using the direct laser writing 3D-printing technology, metallized with electroless silver plating, and are characterized by THz time domain spectroscopy, with the experimental results validating the corresponding theoretical ones. Applications including beam steering and focusing, exploring the Pancharatnam-Berry phase, are also demonstrated numerically. Besides those applications, the helicity preserving mirror response of our metasurfaces can be valuable, among others, also in molecular chirality sensing applications, an issue that is also highlighted here. Helicity ; Terahertz radiation ; Optoelectronics ; Optics ; Metamaterial ; Materials science ; Nanomaterials ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ilham Pratama Aldiansah | Muhamad Akrom
Source: Journal of Applied Informatics and Computing (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Effect of Virtual Sample Generation in Predicting Corrosion Inhibition Efficiency on Pyridazine The purpose of this research is to study how the application of virtual sample generation using the linear interpolation and gaussian noise augmentation method impacts the improvement of prediction model performance in the case of corrosion inhibition efficiency using pyridazine. Random Forest Regressor, Gradient Boosting Regressor, and Bagging Regressor are the models used. The coefficient of determination (R2) values for each model are -0.06, 0.05, and 0.12 on the initial data; the RMSE values are 34.80, 32.90, and 31.65, respectively. After the use of virtual sample development, the R2 values significantly increased to 0.99, 0.96, and 0.99, while the RMSE values significantly decreased to 1.59, 2.88, and 1.25. The research results show that the linear interpolation method can enrich the dataset without altering the data distribution pattern, this method significantly improves the model's accuracy. This performance improvement demonstrates the ability of virtual sample generation to overcome the limitations of the original data; ultimately, this results in a more accurate and reliable predictive model. In the field of material efficiency prediction especially for material technology applications and corrosion control this research helps develop data augmentation methods for similar cases. Pyridazine ; Corrosion ; Sample (material) ; Materials science ; Chemistry ; Metallurgy ; Chromatography ; Medicinal chemistry Corrosion Behavior and Inhibition