jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valeriia Rykova | Kseniia Potopalska | Rasul Abdusalamov | Олексій Водка | Mikhail Itskov
Source: Bulletin of the National Technical University «KhPI» Series Dynamics and Strength of Machines (article, 2025-09-03)
Field: Chemistry — Spectroscopy

Development of an algorithm and software for modeling the structure and mechanical behavior of aerogels Aerogels, ultra-low-density materials with remarkable properties, hold great potential for various technological applications. However, accurately modeling their internal structure is essential for understanding and optimizing their performance. This paper presents the development of an algorithm and software for modeling the structure and mechanical behavior of aerogels. The Diffusion-Limited Cluster-Cluster Aggregation (DLCA) algorithm serves as the foundation for generating realistic computational models of aerogel microstructures. This paper details the creation of algorithms that simulate the formation of aerogel structures based on DLCA principles. A dedicated software tool executes the DLCA algorithm, generating corresponding aerogel structures. Additionally, APDL (ANSYS Parametric Design Language) scripts translate the generated structures into finite element models for analysis in ANSYS software. Test calculations are performed on these models to evaluate displacements and stresses under simple loading conditions. This approach enhances the understanding and design of these versatile materials for various applications. Computer science ; Software ; Algorithm ; Materials science ; Programming language Aerogels and thermal insulation
Authors: Friedrich Hastedt | Klaus Hellgardt | Sophia N. Yaliraki | Ehecatl Antonio del Rio‐Chanona | Dongda Zhang
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Computational Assessment of Molecular Synthetic Accessibility using Economic Indicators The rapid advancement of computational drug discovery has enabled the generation of vast virtual libraries of promising drug candidates. However, evaluating the synthetic accessibility (SA) of these compounds remains a critical bottleneck. While computer-aided synthesis planning (CASP) tools can provide synthesis routes to the candidate, their computational demands make them impractical for large-scale screening. Existing rapid SA scoring methods, struggle to generalize to out-of-distribution molecules and do not account for economic viability. To address these challenges, we present MolPrice, an accurate and reliable price prediction tool. By introducing a novel self-supervised learning approach, MolPrice achieves robust generalization to diverse molecular structures of various complexities. Our comprehensive analysis of model architectures and molecular representations reveals that substructure-based features strongly correlate with market prices, supporting the relationship between synthetic complexity and economic value. MolPrice performs well on the standard literature SA benchmark, showcasing its ability for SA estimation. MolPrice thus serves as both an accurate molecular price predictor and a rapid synthetic accessibility assessment tool, enhancing the efficiency of modern drug discovery pipelines. Computer science ; Economic indicator ; Macroeconomics ; Economics Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Process Optimization and Integration
Authors: Alena Vishina | Rebecca Clulow | Daniel Hedlund | Vitalii Shtender | Peter Svedlindh | Martin Sahlberg | Olle Eriksson | Heike C. Herper
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MFe6X4 system (M = Mg, Sc, Zr; X = Al, Si, P, Ga, Ge, In, Sn, Sb) as possible ’gap’ magnets LiFe6Ge4, with a theoretically predicted saturation magnetization of 1 T, a magnetocrystalline anisotropy energy of 1.78 MJ/m3 and a Curie temperature of 620 K was suggested to be a promising permanent magnet as an outcome of a data-mining search. Magnetic measurements of the synthesized sample are reported here. Unfortunately, experiments revealed a weak ferromagnetic behaviour with magnetization values much below that predicted by theory. This discrepancy is analyzed in detail, and is attributed to the trigonal crystal symmetry that was missed in the previous characterisation of the material. The correct crystal structure is R-3mH (space group 166) and it is found here to have an antiferromagnetic ground state, as opposed to a theoretically predicted ferromagnetic state of the previously reported monoclinic crystal structure. Theoretical calculations show that element substitution can stabilize a ferromagnetic state of the trigonal crystal structure, with high values of saturation magnetization and magnetocrystalline anisotropy. The best results are seen for the Al or Ga substitution for Ge of the LiFe6 X4 compound. Magnet ; Electromagnet ; Work (physics) ; Control system ; Process (computing) Magnetic Properties of Alloys ; Inorganic Chemistry and Materials ; Rare-earth and actinide compounds
Authors: Roozbeh Anvari | Deji Akinwande | Wennie Wang
Source: ChemRxiv (preprint, 2025-04-03)
Field: Materials Science — Materials Chemistry

Effect of electric field on the hysteresis and switching behavior of the MoS2/Au(111) heterojunction It can be said that the interface is the device. A holistic understanding of interfacial interactions and electronic structure of 2D materials with electrodes is far from complete, but is necessary for tailored electronic devices, including in memristors for neuromorphic computing. Here, we develop a computational protocol to study the hysteretic behavior and charge carrier transport mechanisms of the MoS2 /Au(111) junction under applied electric fields using first- principles calculations. We show that even in the absence of defects, formation of conducting bridges or significant atomic reconstruction, the pristine MoS2 /Au(111) junction shows hysteresis in the induced polarization, the electron barrier heights, and the carrier transport. A primary finding from our calculations is the modulation of the Schottky barrier and electron tunneling barrier as a mechanism behind the resistive switching mechanism of the pristine MoS2 /Au(111) junction. We also show that changes of interfacial dipole moments with exter- nal electric field contributes to the memory effect by lowering the electron barriers. Our results indicate that in the absence of defects, electric-field induced relaxation of the interlayer spacing and clustering of Mo-atoms provides the means for spontaneous polarization. Based on this understanding of the MoS2 /Au(111) junction, we propose that a back-to-back double Schottky barrier ferroelectric tunnel junction (FTJ) diode can be used to model the characteristics of MoS2 /Au(111) heterojunction. Our study has implications for revealing the physical origins of the onset of hysteretic properties of heterostructures based on low-dimensional materials, providing a more complete understanding of the mechanisms in resistive switching. Hysteresis ; Electric field ; Heterojunction ; Condensed matter physics ; Materials science ; Field (mathematics) ; Optoelectronics ; Physics ; Mathematics ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Mortadha Shehab Ahmed | Hind M. Saleh | Mahmoud Gharbavi
Source: Jundishapur Journal of Natural Pharmaceutical Products (article, 2025-12-14)
Field: Materials Science — Biomaterials

Fabrication of Novel Electrospun Polyacrylic Acid/Chitosan Nanofibers for Organic Dye Adsorption from Aqueous Solution Background: In this study, electrospun nanofibrous composites made of polyacrylic acid (PAA) and chitosan (Chi), specifically at a 70:30 weight ratio, are successfully fabricated, characterized, and evaluated for performance. These composites are designed to adsorb methylene blue (MB) dye from aqueous solutions with high efficiency. Methods: The high surface area required for better adsorption kinetics was provided by uniform, bead-free nanofibers with an average diameter of 276.15 ± 108.20 nm, which were produced using optimized electrospinning conditions. The material’s applicability was validated by structural and surface characterizations: Zeta potential examination indicated the fibers were negatively charged (-21.5 ± 4.78 mV), allowing for robust electrostatic contact with the cationic MB dye, while Fourier-transform infrared (FTIR) analysis confirmed successful polymer interaction by typical peak shifts. By exhibiting pseudoplastic (shear-thinning) behavior, which is essential for steady fiber creation, rheological evaluations provided additional support for the procedure. Results: The composite performed well under ideal batch adsorption conditions (pH 6, 25ºC), attaining a removal efficiency of over 90% and a maximum adsorption capacity (Qm) of 78.125 mg/g. While isotherm modeling demonstrated a better fit with the Langmuir model [(coefficient of determination (R2 = 0.9789)], confirming monolayer adsorption on homogeneous sites, kinetic analysis significantly supported the pseudo-second-order model (R2 = 0.9859), suggesting that chemisorption is the rate-limiting phase. Performance was dose-dependent, and at a starting concentration of 200 mg/L, capacity increased to surpass 65 mg/g. Also, 50 - 60% of the total capacity was caught by the quick initial adsorption in the first two hours, and equilibrium was reached in around eight hours. Conclusions: All of these quantitative findings point to the polyacrylic acid-chitosan (PAA-Chi) composite nanofibers as a practical, affordable, and extremely effective adsorbent that provides a long-term way to treat wastewater that contains cationic organic dyes. Adsorption ; Nanofiber ; Polyacrylic acid ; Aqueous solution ; Zeta potential ; Electrospinning ; Cationic polymerization ; Langmuir adsorption model Electrospun Nanofibers in Biomedical Applications ; Adsorption and biosorption for pollutant removal ; Cancer Research and Treatment
Authors: Gopal Krishna Murthy H.R. | Revanasiddappa H.D. | S Shivaprakash | K.N. Vennila | Mohammed Saleh Al Ansari | Suhail Ahmad
Source: Research Square (preprint, 2025-06-17)
Field: Chemistry — Organic Chemistry

Design, Synthesis, Structural, and Computational Studies of (Z)-2,2′,4,4′,5,5′-Hexahydroxy Stilbene as a Potential Antifungal Agent Antifungal ; Combinatorial chemistry ; Chemistry ; Stereochemistry ; Computer science ; Biochemical engineering ; Biology ; Engineering ; Microbiology Synthesis and biological activity ; Synthesis of Indole Derivatives ; Sirtuins and Resveratrol in Medicine
Authors: Neha Dindhoria | Heena Inani
Source: International Journal of Novel Research and Development (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Analysis and Investigations on Synthesis and Biological Evaluation of Certain Nitrogen Containing Heterocyclic Compounds Medicinal chemistry is centered about nitrogen containing heterocyclic compounds because they possess very diverse structural framework and possess remarkable pharmacology potential. Herein, We provide an overview of recent progress in the synthesis and biological evaluations of imidazole, pyrazoles, triazoles, quinolines and pyridines. These heterocycles are significant because a wide range of heterocycles are utilized in drug discovery and therapeutic development. Heterocycles have activities like antimicrobial, anticancer, anti-inflammatory, antiviral, antitubercular, and antifungal activities. A number of synthetic strategies are described involving from a classical multistep synthesis to modern approaches of green chemistry such as microwave and solvent free techniques which emphasize the efficiency, selectivity, and environmental impact. The SAR studies are critically reviewed to establish how molecular features correlate with bioactivity profiles so as to suggest ways for pharophore design and optimization. Biological screening methods used to screen efficacy and mechanism of action of these heterocyclic compounds are also discussed. The review also deals with molecular docking and in silico modeling techniques to define target specific interaction and binding affinities. Recently, recent trends in the hybrid molecule synthesis where two or more pharmacophores are combined into a single scaffold, are discussed for their synergistic therapeutic potential. Overall, the review presents the best of a great deal of research work in a format which can be used to understand how to design novel nitrogen based heterocyclic entities. The medicinal chemistry, pharmacology, synthetic organic chemistry community may well benefit from its use as a valuable reference in developing next generation bioactive compounds. The discussion is designed to be comprehensive and can be used as a base for future perspectives that may help in development of new heterocyclic approaches in drug discovery and widen the application of the nitrogen heterocycles as a tool to combat global health challenges. Chemistry ; Combinatorial chemistry ; Nitrogen ; Biochemical engineering ; Organic chemistry ; Engineering Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Mohamed Hasaan Hussain | Muhammad Huzaimi Haron | Norazah Abd Rahman | Huey Ling Tan | Lay Kek Teh | Norbert Radacsi | Greg M. Harris | Noor Fitrah Abu Bakar
Source: ASEAN Journal of Chemical Engineering (article, 2025-12-31)
Field: Materials Science — Biomaterials

Cellulose-Based Materials for Neural Tissue Engineering: From Scaffolds to Interfaces - A Review Cellulose and its derivatives have emerged as versatile natural polymer platforms for neural tissue engineering due to their intrinsic biocompatibility, low cytotoxicity, and tunable biodegradability. Recent advances demonstrate that cellulose-based materials can be engineered to reproduce key structural and physicochemical features of the neural extracellular matrix, including polysaccharide-rich architectures and fibrillar topographies that support neuronal adhesion, alignment, and network formation. Bacterial cellulose, nanocrystalline cellulose, methylcellulose, ethyl cellulose, and their composite systems exhibit hydrogel-forming capability and abundant chemically active functional groups, enabling chemical modification, injectability, and controlled delivery of bioactive agents. Importantly, the incorporation of conductive fillers or chemical modification strategies has enabled electrically active cellulose-based scaffolds with conductivities spanning approximately 7.8 × 10⁻⁷ to 0.49 S cm⁻¹, while largely preserving cytocompatibility, positioning these materials as promising candidates for electrically responsive scaffolds and long-term neuronal interfaces. Despite these advances, challenges remain in achieving precise control over mechanical properties, degradation kinetics, and long-term electrical stability under physiological conditions. This review critically examines the progress in cellulose-derived materials for neural tissue engineering and neuronal interface fabrication, with an emphasis on material composition, fabrication methodologies, structure-property-function relationships, and current limitations, and outlines future directions for the rational design of multifunctional cellulose-based neural biomaterials. Tissue engineering ; Electrically conductive ; Neural engineering ; Neural tissue engineering ; Rational design ; Neural activity ; Neural Prosthesis ; Fabrication ; Self-healing hydrogels Advanced Cellulose Research Studies ; Plant and Biological Electrophysiology Studies ; Neuroscience and Neural Engineering
Authors: ShengZheng Liu
Source: International Journal of Computer Science and Information Technology (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Automated InChI Sequence Generation from Chemical Images with Configurable Encoder-Decoder Architecture In the chemical and pharmaceutical domains, researchers frequently rely on structural diagrams found in patents, scientific literature, and archival documents. Converting these diagrams into standardized text representations, such as the International Chemical Identifier (InChI), is traditionally a labor-intensive manual process. To address this challenge, we introduce an optical chemical structure recognition (OCSR) system based on an encoder–decoder framework that translates molecular images into InChI strings efficiently and accurately. The workflow begins with image preprocessing, where each structural diagram is resized and transformed into normalized tensors. The encoder then extracts salient visual features through backbone networks, including ResNet, EfficientNet, and Vision Transformers, generating high-dimensional feature maps that capture both spatial and semantic information. These are passed to the decoder, implemented with GRU, LSTM, or Transformer architectures, which sequentially outputs InChI tokens. To enhance performance, the framework integrates a soft attention mechanism that directs focus toward relevant image regions during decoding, and employs a gradually reduced teacher forcing strategy to improve robustness during training. We evaluate multiple architectural pairings on a large-scale dataset, benchmarking model outputs against ground-truth InChI strings using exact match accuracy, Levenshtein distance, BLEU, and METEOR metrics. Results reveal clear performance variations across encoder–decoder configurations, with EfficientNet-B0 combined with an LSTM decoder achieving accuracy up to 84.0% on a 20K dataset, striking an effective balance between computational efficiency and predictive precision. These findings provide critical insights into designing OCSR systems tailored for varying levels of chemical complexity and resource availability, thereby advancing automated molecular translation for diverse applications in research and industry. Workflow ; Robustness (evolution) ; Benchmarking ; Encoder ; Salient ; Feature (linguistics) ; Levenshtein distance ; Sequence diagram Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Arkaprava Roy | Sampreeti Bhattacharya
Source: ChemRxiv (preprint, 2025-01-16)
Field: Materials Science — Materials Chemistry

A Look Into Solubility Variations Via a Novel Nonparametric Additive Characterization Learning aqueous solubility remains a key challenge in drug development for improving oral bioavailability. Traditional data-driven solubility estimations using standard supervised models, however, can often suppress the information embedded in a molecule’s chemical properties and the intricate connectivity of its atoms. In addition to the predictive analyses for solubility, it is also crucial to investigate the underlying scientific factors that influence the aqueous solubility of potential drug candidates. This paper attempts to learn such properties and further proposes a novel nonparametric model with an additive-type characterization that provides insights into the differential contribution of the chemical and graphical descriptors, that are important in learning a molecule’s solubility in an aqueous solvent. The dataset used in this work includes over 9000 exper- imental structural, chemical, and electronic properties. Using graph-theoretic attributes derived from molecular graphs, we characterize the structural traits of the molecules. Our analysis reveals several intriguing properties of solubility and its physicochemical relationship with both structural and chemical features of the molecules, specifically based on their weights and shapes. Nonparametric statistics ; Characterization (materials science) ; Solubility ; Econometrics ; Mathematics ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry Crystallization and Solubility Studies
Authors: Andraž Gnidovec | Simon Čopar
Source: Communications Physics (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Controlling curvature of self-assembling surfaces via patchy particle design Abstract Curved surfaces in soft matter and biological systems commonly emerge as a result of self-assembly processes where building blocks aggregate in a controlled manner, giving rise to specific system structure and properties. However, achieving precise control of curvature in assembled structures remains challenging and usually requires tuning of complex interactions between particles. We present an inverse design approach to target self-assembly into surfaces with a specified spherical curvature in a one-component system of model patchy particles. Our optimization framework leverages automatic differentiation and addresses the challenge of exploding gradients when differentiating over chaotic molecular dynamics trajectories, yielding stable gradients suitable for general optimization schemes. We outline the model requirements for successful optimization and determine the significant hyperparameter choices influencing algorithm performance. Finally, we demonstrate that the proposed approach allows us to design interactions for self-assembly into structures with a tunable target curvature, paving the way for new functional materials with precisely controlled geometries. Curvature ; Chaotic ; Inverse ; Surface (topology) ; Particle system ; Particle (ecology) ; Aggregate (composite) ; Trajectory ; Shape optimization ; Complex system Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Varadi, Kristof | Márk Marosi | Péter Antal
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Circuits, Features, and Heuristics in Molecular Transformers Transformers generate valid and diverse chemical structures, but little is known about the mechanisms that enable these models to capture the rules of molecular representation. We present a mechanistic analysis of autoregressive transformers trained on drug-like small molecules to reveal the computational structure underlying their capabilities across multiple levels of abstraction. We identify computational patterns consistent with low-level syntactic parsing and more abstract chemical validity constraints. Using sparse autoencoders (SAEs), we extract feature dictionaries associated with chemically relevant activation patterns. We validate our findings on downstream tasks and find that mechanistic insights can translate to predictive performance in various practical settings. Heuristics ; Transformer ; Parsing ; Computational model ; Autoregressive model ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sonal C. Bhangale | Laxmikant S. Dhamande | Bhagyashree Ashok Tingare | Tarun Dhar Diwan | R. A. Kapgate | P. William | Prasad M. Patare
Source: Journal of Nano- and Electronic Physics (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Optimization of Defect Detection in Atomic Materials Using Graphene Layer Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Layer (electronics) ; Atomic layer deposition ; Graphene ; State (computer science) ; Population ; Solid-state Electron and X-Ray Spectroscopy Techniques