jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. S. Hari Narayana Moorthy | J. S. Mishra | Divesh Kumar Nagesh | Anand Kumar | Arvind Kumar Patel | M. Ravi | Nikhil | Rima Patel | Rajesh Kumar | Madan Pal Singh | Chandrabose Karthikeyan
Source: In Silico Research in Biomedicine (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ligand based analysis of the solubility of drugs using in silico techniques Solubility is one of the important physicochemical properties required for the pharmacological actions of drugs. The structural analysis of the existing drugs on the basis of their functional groups, atoms, bonds, physicochemical properties, etc. can provide important information on the factors responsible for the increased or decreased solubility of the drugs either in water or lipid. In the present investigation, machine learning based classification and quantitative structure property relationship (QSPR) analyses were performed with 118 drugs exhibiting experimental solubility data (in water and various concentrations of polyethylene glycol (PEG)-water mixture). Physicochemical descriptors and Molecular Access Systems (MACCS) Fingerprints were calculated using PaDEL software, and the machine learning-based classification studies and QSPR studies were conducted using the Waikato Environment for Knowledge Analysis (WEKA) software and BuildQSAR software respectively. Support vector machine (SVM), decision tree (DT), random forest (RF), random tree (RT), and Naïve Bayes (NB) algorithms were used in the machine learning study and multiple linear regression analysis was applied for QSPR study. The results revealed that polar and polarizable functional groups and the related physicochemical descriptors significantly contributed for the solubility prediction. Furthermore, drugs with more than one aromatic ring (MACCSFP125), side chains with nitrogen attached to aromatic atoms (MACCSFP135), triple bonds (MACCSFP19), the number of fluorine atoms (MACCSFP42), sulfur side chains attached to aromatic atoms (MACCSFP59), sulfur atoms connected between the ring and non-ring atoms (MACCSFP64), and the number of sulfur atoms were involved in the classification and prediction of the solubility. In all cases, the descriptors in the models explained that the presence of heteroatoms, polar, and polarizable groups highly influenced the aqueous solubility (with and without PEG) of the drugs. These studies concluded that the substitution of polar/polarizable functional groups in the drugs along with the position of groups and the distance between these polar/polarizable groups could improve the aqueous solubility of the drugs by forming hydrogen bonds with water molecules and the PEG. In silico ; Ligand (biochemistry) ; Solubility ; Chemistry ; Computational biology ; Combinatorial chemistry ; Biology ; Biochemistry ; Organic chemistry ; Receptor Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Alexander Adel | Ralf Wanzenböck | Georg K. H. Madsen
Source: Physical Chemistry Chemical Physics (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Completing the hierarchy of rotational defects in monolayer MoS <sub>2</sub> through symmetry-aware evolutionary search Monolayer molybdenum disulfide (MoS<sub>2</sub>) shows a plethora of defect configurations, which constitutes the basis for tailoring material properties through defect engineering. Detailed characterization of these defects remains challenging due to the complexity of the potential energy surface. We efficiently explore the three-fold rotational defect potential energy surface in monolayer MoS<sub>2</sub> by combining an evolutionary algorithm with a machine-learning force field. To improve the performance of the structure searches, the algorithm hierarchically restricts the exploration process to a lower-dimensional subspace, utilizing the symmetry operators associated with the investigated defects. We demonstrate that these constrained trajectories exhibit lower global uncertainty measures during the evolution, produce final structures with lower energy distributions and converge faster. Our approach results in the discovery of several novel structures with reasonable computational effort, thereby completing the hierarchy of rotational defects in MoS<sub>2</sub>. Monolayer ; Hierarchy ; Molybdenum disulfide ; Process (computing) ; Energy (signal processing) ; Potential energy surface ; Characterization (materials science) ; Evolutionary algorithm 2D Materials and Applications ; Machine Learning in Materials Science ; Neural Networks and Reservoir Computing
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-28)
Field: Chemistry — Spectroscopy

Beyond Retention Time: Ion Mobility-Driven Structural Elucidation in Multi-Modal Chromatography-Mass Spectrometry Traditional chromatography-mass spectrometry (LC-MS or GC-MS) workflows rely primarily on retention time and mass-to-charge ratio (m/z) for compound identification and structural elucidation. However, the inherent complexity of biological and environmental samples, often containing numerous isobaric and isomeric species, frequently leads to co-elution, complicating accurate characterization. Ion mobility-mass spectrometry (IM-MS) offers a powerful orthogonal separation dimension, differentiating ions based on their gas-phase size, shape, and charge, as quantified by their collision cross section (CCS). This paper explores the transformative potential of integrating ion mobility into multi-modal chromatography-mass spectrometry platforms for enhanced structural elucidation. By adding a rapid, gas-phase separation dimension, IM-MS significantly increases peak capacity, resolves co-eluting isomers, and provides a robust, instrument-independent physicochemical descriptor (CCS) complementary to retention time and m/z. We discuss the fundamental principles of IM-MS, its various instrumentation platforms, and advanced data analysis strategies, including computational CCS prediction and machine learning approaches. Applications across proteomics, metabolomics, and lipidomics highlight how IM-MS provides deeper structural insights, improves confidence in compound identification, and enables the characterization of structural heterogeneity previously unattainable with conventional methods. This review emphasizes that IM-MS is moving beyond a niche technique to become an indispensable tool in advanced analytical chemistry, promising new paradigms for understanding complex molecular systems. Ion-mobility spectrometry ; Mass spectrometry ; Isobaric process ; Characterization (materials science) ; Identification (biology) ; Instrumentation (computer programming) ; Ion ; Lipidomics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: S. D. Madhu Kumar | Dorina F. Sava Gallis | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal-organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanide-based MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals. Stability (learning theory) ; Entropy (arrow of time) ; Convex hull ; Density functional theory ; Interatomic potential ; Mixing (physics) ; Regular polygon Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yuechun Li (8889641) | Chenjie Nie (22414212) | Chenxin Ji (21719759) | Zhaowen Cui (4246939) | Yanwei Ji (14493601) | Min Ma (462420) | Wentao Zhang (660107) | Leina Dou (6759488) | Qianjin Liu (4258018) | Jianlong Wang (438075)
Source: Figshare (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

ThermodynamicMicroenvironment Engineering in MesoporousNanoreactors to Enhance Biocatalysis for AI-Empowered UltrasensitivePathogen Detection Harmonizing enzyme-support microenvironments to govern thermodynamic interaction landscapes presents a critical yet underexplored frontier in nanobiocatalysis for pathogen detection. Herein, we architecturally engineer mesoporous resorcinol formaldehyde nanospheres (mRFNSs, 9.95 nm pores) with tailored surface chemistry to elucidate how microenvironment modulation dictates enzyme immobilization energetics. Thermodynamic dissection demonstrates that betaine-tailored mRFNSs with optimal immobilization efficiency and activity dramatically reshape binding energetics, achieving record affinity through optimized electrostatic complementarity, hydrogen-bond networks, and hydrophobic effect. This microenvironment engineering strategy delivers an unprecedented 4.01-fold enhancement in binding constant (<i>K</i><sub>a</sub> = 1.12 × 10<sup>8</sup> vs 2.79 × 10<sup>7</sup> M<sup>–1</sup>) and superior thermodynamic spontaneity (Δ<i>G</i> = −46.0 vs −42.5 kJ mol<sup>–1</sup>). Leveraging this, we develop a paradigm-shifting ratiometric fluorescence immunoassay where ALP triggers in situ silicon quantum dot (SiQDs) synthesis (530 nm) against tetraphenylbenzidine reference (620 nm), achieving ultrasensitive Salmonella typhimurium (S. typhimurium) detection (100 CFU mL<sup>–1</sup>), which is 50-fold lower than that of conventional ELISA. A convolutional neural network (CNN) decodes smartphone-captured fluorescence hues, enabling portable classification (93.75% accuracy) of pathogen levels. Validated in food matrices (81.44–116.93% recovery), this work establishes thermodynamic-microenvironment correlations as a blueprint for next-generation nanobiocatalysts, bridging biointerface science with artificial intelligence (AI)-enhanced diagnostics. Biocatalysis ; Biosensor ; Nanomaterials ; Biointerface ; Nanoreactor ; Fluorescence ; Mesoporous material ; Aptamer Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Advanced Nanomaterials in Catalysis
Authors: Ning An | Weitao He | Xiaoyi Lv | Xiaolin Huang | Yuankui Leng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Ratiometric Fluorescent Metal-Organic Framework Sensors for Tetracycline Antibiotics Tetracycline ; Fluorescence ; Antibiotics ; Tetracycline antibiotics ; Metal ; Environmental chemistry ; Chemistry ; Environmental science ; Organic chemistry ; Physics ; Optics ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Xing Liu | Wenjun Zheng
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-18)
Field: Chemistry — Spectroscopy

Molecular dynamics simulation of the truncated NMDA receptor in the open state Abstract The GluN1/GluN2A N-methyl-D-aspartate receptor (NMDAR) is a critical ligand-gated ion channel in the central nervous system, playing essential roles in synaptic plasticity, learning, and memory. Understanding its dynamics in the open/active state is paramount for deciphering its physiological functions and for developing targeted therapeutics. Despite many past efforts, the active/open state has not yet been fully resolved at atomic resolutions. To elucidate the molecular mechanism of the NMDAR activation, computer modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this study, we started from a previously built open-state model of a truncated NMDAR, and then explored its energetics and dynamics with extensive molecular dynamics (MD) simulation (total simulation time is 5.6 µs). Based on the MD simulation, we employed an array of analysis tools to study the fluctuations/motions at the levels of individual residues, the channel pore, and the global structure, and identified a dynamic network of polar/nonpolar interactions between residues. Furthermore, we used machine learning to identify key interactions and residues relevant to channel opening/closing and validated them with evolutionary conservation grades and annotations of disease mutations in NMDAR. Taken together, our modeling and simulation provided rich structural and dynamic information which will guide functional studies of the activation of this key receptor. Molecular dynamics ; Energetics ; Mechanism (biology) ; Key (lock) ; Dynamics (music) ; Ion channel ; State (computer science) Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Molecular spectroscopy and chirality
Authors: C. Grant | Neil Williams | Spiridon Koutsonas | Liberata Guadagno | H. Hadavinia
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction and Multi-Objective Optimisation of CNC/Epoxy Composite Mechanical Properties Using Interpretable Machine Learning Gradient boosting ; Support vector machine ; Boosting (machine learning) ; Decision tree ; Random forest ; Ultimate tensile strength ; Composite number ; Modulus Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Polymer Nanocomposites and Properties
Authors: Zhikai Gao | Xianteng Zhou | Meng Xu | Yuanji Xu | Fuyang Tian
Source: Acta Materialia (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

High-throughput screening anisotropic negative thermal expansion framework materials Materials science ; Negative thermal expansion ; Thermal expansion ; Throughput ; Anisotropy ; Composite material ; Computer science ; Optics Thermal Expansion and Ionic Conductivity ; Ferroelectric and Piezoelectric Materials ; Magnesium Oxide Properties and Applications
Authors: Caio César Ferreira Florindo | K. S. Araújo | Emylly Carla Arantes Conrado | Davide Costa
Source: Cuadernos de Educación y Desarrollo (article, 2025-12-17)
Field: Chemistry — Organic Chemistry

Development of an amazonian oils database for statistical analysis and machine learning modeling This article aims to develop an Amazonian oils database to support statistical analysis and machine learning modeling. Data on fatty acid composition and physicochemical properties of 65 vegetable oils and fats, including 17 Amazonian species, were compiled. From individual fatty acid profiles, diversity indices, correlation matrices between composition and physicochemical properties, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were obtained. These approaches enabled the description of the lipid space covered by the evaluated oils and the positioning of Amazonian oils relative to conventional and exotic vegetable matrices. Correlations between SFA, MUFA and PUFA levels and parameters such as melting point, iodine value and oxidative stability were consistent with established principles of lipid chemistry. The analysis restricted to the 17 Amazonian oils revealed distinct chemotypes, including a lauric–myristic group (murumuru, ucuúba, babassu), an oleic–linoleic group (açaí, bacaba, andiroba, buriti, patauá, tucumã, pracaxi, bacuri and cupuaçu) and the singular profile of copaiba. Additionally, a supervised XGBoost (XGBClassifier) model was used to discriminate Amazonian and non‑Amazonian oils based solely on individual fatty acids. SHAP‑based interpretation indicated that gadoleic, caprylic, linoleic, oleic, lauric and myristic acids play a prominent role in characterizing the Amazonian set. Overall, the database, combined with statistical and machine learning tools in Python/Jupyter, represents a promising approach to map the compositional space of vegetable oils and to identify Amazonian lipid signatures with technological potential. Amazonian ; Linear discriminant analysis ; Principal component analysis ; Statistical analysis ; Chemometrics ; Amazon rainforest ; Cluster analysis ; Stability (learning theory) Edible Oils Quality and Analysis ; Coconut Research and Applications ; Food Chemistry and Fat Analysis
Authors: Yan Li | Furui Zhang | Jie Zhao
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A study on the prediction of MoS <sub>2</sub> transistor electrode contact characteristics based on transfer learning methods Data scarcity is one of the key bottlenecks in the application of machine learning in the field of materials discovery. In this challenge, transfer learning can leverage existing consistent large-scale data to assist in property prediction on small datasets, thereby opening up more possibilities for materials development. With the discovery of many-dimensional materials and the challenges posed by the miniaturization of transistors, the range of electrode materials available for transistors is extremely broad, but it is difficult to explore them through traditional experimental methods. Therefore, in the face of scarce data on electrode contact characteristics, this study proposes a cross-scale hybrid transfer learning framework that integrates first-principles calculations with a 2D materials database. By utilizing large-scale potential height data obtained through PBE functional calculations, the framework achieves high-precision predictions of DFT-1/2 method and HSE06 functional calculation results, with an MSE controlled within 0.04 eV. The research results indicate that this learning framework accelerates the process of screening electrode materials for MoS<sub>2</sub>, providing important theoretical guidance and technical support for the design and optimization of new electronic devices. Leverage (statistics) ; Miniaturization ; Transistor ; Transfer of learning ; Process (computing) ; Key (lock) ; Electrode ; Field-effect transistor Machine Learning in Materials Science ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Pablo R. Arantes | Souvik Sinha | Giulia Palermo
Source: Journal of Chemical Information and Modeling (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

ParametrizANI: Fast and Accessible Dihedral Parametrization for Small Molecules In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing detailed protocols for dihedral parametrization using GAFF and OpenFF force fields. The robust PyTorch-based program, TorchANI, forms the backbone of ParametrizANI, functioning as a benchmark to uphold precision in parametrization tasks. TorchANI plays a pivotal role in enabling ANI (ANAKIN-ME) deep learning models, which are trained to predict potential energy surfaces and related molecular properties with high accuracy. Our work on ParametrizANI goes beyond just creating a tool; it is about building a research-friendly environment, free from the constraints of limited resources. We are committed to democratizing research, enabling teams of all sizes to perform dihedral parametrization with DFT-level accuracy. This tool opens up new possibilities in molecular dynamics and related fields. It marks a significant step forward in improving the scientific community's ability to parametrize small molecules. For a detailed look at its features, we invite you to check out ParametrizANI on GitHub (https://github.com/palermolab/ParametrizANI). Parametrization (atmospheric modeling) ; Benchmark (surveying) ; Dihedral angle ; Molecular dynamics ; Small molecule Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Bekir Yitik
Source: Industria Textila (article, 2025-12-22)
Field: Materials Science — Polymers and Plastics

Optimisation of combed yarn properties based on yarn number and machinejaw range using artificial neural networks The need for natural clothing is increasing day by day. To meet this demand, the apparel industry is developing new systems to enhance production and raw material usage. Using healthy products is essential for a healthy life, which increases the need for natural raw materials. Cotton is the ideal natural raw material for a renewable and sustainable production line. Despite the growing production, it cannot fully meet the demand. Therefore, new systems are being developed to improve the quality of cotton production. The foundation of the textile industry is yarn, and yarn production lines consist of systematically operated machines. These production systems include carded, combed, and open-end methods. In combed production, high-quality and long fibres are used to produce yarns with counts such as Ne 30 or Ne 50. In combed yarn production, fibre length and ratio can be adjusted through machine settings. Lap feeding cylinder gaps in combed yarn machines are critical for this adjustment. In this study, experimental results were obtained using 4 different yarn counts produced from the same blend and 5 different combed feeding jaw settings. These results were optimised using artificial neural networks. In the analysis, yarn count and combing cylinder gap were used as input data, while the physical properties of the yarn were used as output data Yarn ; Cylinder ; Raw material ; Textile ; Textile industry ; Artificial neural network Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Natural Fiber Reinforced Composites
Authors: Xiaohang Tuo | B.Y. Huang | Min Yin | Yumei Gong | Ying Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Intelligent Recognition and Quantitative Analysis of Fiber Morphology for Electrospinning Membrane Performance Electrospinning ; Morphology (biology) ; Fiber ; Membrane ; Hollow fiber membrane ; Computer science ; Materials science ; Composite material ; Chemistry ; Polymer ; Geology Electrospun Nanofibers in Biomedical Applications