jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kostiantyn Shabelnyk | Lyudmyla Antypenko | Natalia M. Bohdan | Victor Ryzhenko | Igor Belenichev | Petakh Pavlo | Oleksandr Mykhailovych Kamyshnyi | Serhii Kovalenko
Source: Journal of Neurochemistry (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Discovery and Characterization of Novel Spirotriazoloquinazolines as Potential Neuroprotectors: Synthesis, Computational Screening, and Preliminary In Vivo Evaluation This study describes the rational design, synthesis, and evaluation of forty 2'-R-6'H-spiro(cycloalkyl/heterocyclyl)[1,2,4]triazolo[1,5-c]quinazolines as potential neuroprotective agents targeting multiple receptor systems implicated in cognitive dysfunction. The molecular design integrated structural features from established nootropic and anxiolytic pharmacophores to create compounds with putative multi-target activity. In silico ADMET analyses assessed drug-likeness parameters, while molecular docking studies evaluated binding interactions with nine neuroreceptor targets: glutamate GluA3, GABA(A)R, dopamine D2, serotonin 5-HT1A and 5-HT7, cannabinoid CB2, muscarinic M2 acetylcholine, corticotropin-releasing factor receptor 1 (CRF1R), and metabotropic glutamate receptor 5 (mGluR5). Based on computational predictions, selected compounds underwent preliminary in vivo screening using a ketamine-induced cognitive impairment model in rats. Behavioral assessments examined anxiety-related responses and cognitive performance relative to piracetam and fabomotizole controls. Biochemical analyses measured inflammatory markers (IL-1β, caspase-1), cell survival indicators (Bcl-2), and hypoxic adaptation responses (HIF-1 mRNA). Docking studies indicated favorable binding profiles across tested receptor targets compared to reference ligands, with calculated affinities suggesting potential modulatory interactions. The experiments showed that compounds 25, 26, and 32 attenuated ketamine-induced behavioral alterations, demonstrating effects in anxiety reduction and cognitive performance that appeared numerically greater than piracetam and fabomotizole, though the magnitude and statistical robustness of these differences require further characterization. Compound 31 reduced IL-1β expression by 72% and caspase-1 by 80% relative to ketamine-treated controls. Compound 26 increased Bcl-2 expression by 96% and HIF-1 mRNA levels by 3.5-fold compared to control conditions. These findings suggest that spirotriazoloquinazolines may function as positive modulators at cognitive-enhancing receptors, potentially exerting neuroprotective effects through anti-inflammatory and anti-apoptotic mechanisms. Further investigation is necessary to validate the observed effects, establish dose-response relationships, and elucidate the molecular mechanisms underlying the apparent neuroprotective properties of these compounds. Piracetam ; Neuroprotection ; Nootropic ; Pharmacophore ; Anxiolytic ; In silico ; Metabotropic glutamate receptor 5 ; In vivo Quinazolinone synthesis and applications ; Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles
Authors: Evan R. Antoniuk | Peggy Li | Nathan Keilbart | Stephen Weitzner | Bhavya Kailkhura | Anna M. Hiszpanski
Source: npj Computational Materials (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Active learning enables generation of molecules that advance the known Pareto front Abstract Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model. Iterative and incremental development ; Stability (learning theory) ; Workflow ; Generalization ; Property (philosophy) ; Process (computing) ; Pipeline (software) ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Haoran Ma | Yuchen Zheng | L. Zhang | Xiaofei Chen | Dan Wang
Source: ArXiv.org (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Transformer-based prediction of two-dimensional material electronic properties under elastic strain engineering Strain engineering provides a powerful route for tuning the electronic properties of two-dimensional (2D) materials, but exploring the full multidimensional strain space with density functional theory (DFT) is computationally prohibitive due to the nonlinear coupling between normal and shear components. In this work, we introduce a Transformer-based, multi-target surrogate model framework that achieves DFT-level bandgap prediction accuracy, reaching a mean absolute error of 0.0103 eV while retaining full interpretability through attention-weight analysis. The learned self-attention map consistently identifies shear strain as the interaction center that influences both bandgap and phonon stability, an insight not readily captured by classical feature-importance metrics. This work establishes attention-based architectures as physically interpretable surrogate models for multi-property prediction, offering a generalizable strategy for accelerating deep elastic strain engineering in materials informatics. Interpretability ; Nonlinear system ; Coupling (piping) ; Work (physics) ; Strain (injury) ; Strain engineering ; Material properties ; Band gap 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Jiejing Li | Chuanhao He | Yaoxin Lin
Source: Chinese Science Bulletin (Chinese Version) (article, 2026-01-01)
Field: Materials Science — Biomaterials

The design of self-assembling peptides and application in diagnosis and treatment of diseases 自组装多肽作为一种高度可编程的生物材料，近年来在疾病诊疗领域取得显著进展。通过对自组装多肽驱动机制与序列-结构-功能关系的深入理解，能够精准设计出具有特定纳米形貌与智能响应特性的组装体，使其功能从结构支撑向复杂生物信号模拟与主动诊疗方向拓展。在生物医药领域，自组装多肽因其优异的生物相容性、可降解性和仿生特性，已成为临床前研究和临床应用的关键材料平台，展现出巨大的转化潜力。本文综述了自组装多肽的分子驱动机制与自组装多肽的设计策略，总结了其在疾病诊断与治疗中的应用，并对其未来发展趋势进行展望。 Disease ; Identification (biology) ; MEDLINE ; Clinical trial Supramolecular Self-Assembly in Materials ; Advanced Biosensing Techniques and Applications ; Chemical Synthesis and Analysis
Authors: Kadir Mercan
Source: Uludağ University Journal of The Faculty of Engineering (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

SIZE EFFECTIVE MODAL BEHAVIOR OF STRETCHABLE E-SKIN FOR HUMANOID ROBOTS AND PROSTHETIC HAND As sensor technology advances, the dimensions of components continue to shrink to the nanoscale, making the consideration of size effects imperative for accurate design and analysis. This study investigates the modal behavior of stretchable electronic skin (e-skin) for humanoid robots and prosthetic hands, explicitly accounting for the micro-nanoscale effects of the sensor layer. While molecular dynamics simulations with tools like LAMMPS provide realistic results by incorporating atomic interactions, they are computationally prohibitive for systems of micrometer dimensions, which can contain over 3.8×10¹¹- 5×10¹¹ atoms. To bridge this scale gap, molecular dynamics simulations were employed to calibrate the nonlocal parameter (e₀a) of the Eringen nonlocal elasticity theory for graphene, graphene oxide (GO), and reduced graphene oxide (rGO) nanosensors on a SiO₂ substrate. These calibrated parameters were then integrated into ANSYS and COMSOL Multiphysics, finite element software that typically does not incorporate nanoscale effects. A comparative modal analysis was performed using classical plate theory, standard finite element methods, and the proposed size effect considered finite element approach. Realistic boundary conditions modeling the sensor's connection to an electronic circuit were applied. The results demonstrate that employing realistic boundary conditions, as opposed to idealized clamped supports, leads to a significant reduction in natural frequencies by an average of 43%, underscoring the critical importance of accurate modeling for predictive design. Finite element method ; Modal ; Humanoid robot ; Robot ; Modal analysis ; Boundary value problem ; Natural frequency ; Boundary (topology) ; Software Nonlocal and gradient elasticity in micro/nano structures ; Advanced Sensor and Energy Harvesting Materials ; Graphene research and applications
Authors: Nicole Venachi | Alejandra I. Guerrero | Pedro Albán Bolaños | Lily Marcela Palacios | Germán Antonio Arboleda Muñoz | Diego Fernando Roa Acosta | Héctor Samuel Villada Castillo
Source: Journal of Polymers and the Environment (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Review on Aging of Biodegradable Polymers: Experimental Approaches and Emerging Role of Machine and Deep Learning Abstract Biodegradable polymers are currently the focus of intense research due to their biodegradability and potential to replace conventional plastics. However, they still face several limitations: high cost, limited large-scale production, and faster decline of its properties over time, which compromises its performance and durability. Their complex molecular structures, combined with the strong influence of physical, chemical, and environmental factors, make their performance difficult to predict with traditional methods. These complexities highlight the need for advanced modeling approaches capable of capturing nonlinear and high-dimensional behaviors. Machine learning (ML) and deep learning (DL) offer promising tools in this context. Their application to biodegradable polymers can improve predictions of aging behavior and lifetime under different environmental conditions, guide the design of biodegradable polymers, identify optimal synthesis and processing parameters for extended service life and detect complex patterns in large datasets. This review discusses experimental techniques commonly used to evaluate the aging of biodegradable polymers and examines recent applications of ML and DL in this field. It also links experimental data with ML/DL models and outlines the key challenges and future perspectives for integrating these approaches into biodegradable polymer research. Deep learning ; Key (lock) ; Biodegradable polymer ; Focus (optics) ; Face (sociological concept) Machine Learning in Materials Science ; Polymer composites and self-healing ; Microplastics and Plastic Pollution
Authors: Mohammad Hossain | ROSS A. FILBRUN | Shayne Roth | Hiroshi Sakagami | Shigeru Amano | HARUNA NAGASE | Keitaro Satoh | Jonathan R. Dimmock
Source: Anticancer Research (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Cytostatic Benzylidene-isophthaloylhydazides With Diminished Neurotoxicity Benzylidene-isophthaloylhydrazides - particularly compound <b>1h</b> - are promising antineoplastic agents with noteworthy tumor selectivity and low neurotoxicity accompanied by favorable ADME properties. Their robust activity, favorable ADME properties, and clear structural trends support further optimization and preclinical development. ADME ; Neurotoxicity ; Pharmacokinetics ; Toxicity ; Chemotherapy ; Cytotoxicity Synthesis and biological activity ; Synthesis of Tetrazole Derivatives ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Faiqa Noreen | Muhammad Iftikhar Shoukat | Imran Imran | Farhan Siddique | Sumaira Nadeem | Mostafa A. Ismail | Xianliang Zhao | Magdi E. A. Zaki | Sobhi M. Gomha | Zahid Shafiq
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Design, synthesis and anticonvulsant evaluation of novel 2,4,5-trichlorobenzenesulfonate-based dihydrothiazoles supported by <i>in vivo</i> and <i>in silico</i> studies This study describes the synthesis of a new series of 2,4,5 trichlorobenzenesulfonate structural class dihydrothiazoles and assesses the potential of the compounds as antiepileptic drugs using <i>in vivo</i> methods, network pharmacology, molecular docking, and DFT computational methods. Ten compounds 3(a-j) were synthesized and characterized, and their anti-seizure effects were evaluated on the standard seizure models. <i>In vivo</i> anticonvulsant activity was explored in the 6 HZ corneal stimulation model. Compound 3c showed complete protection, with return to normal behavior in less than 10 seconds post stimulation. The rest of the compounds exhibited 25% (3a), 50% (3d), 75% (3f), and 75% (3j) protection, respectively. In the pentylenetetrazole (PTZ) chemoconvulsant model, compound 3c at 150 mg kg<sup>-1</sup> showed complete protection from death and hind limb extension. None of the animals showed seizure activity for 30 minutes post-administration. In the <i>in silico</i> molecular docking, compound 3c showed the greatest anticonvulsant activity among the synthesized compounds. Compound 3c had a binding affinity of -9.7 kcal mol<sup>-1</sup>, while the co-crystallized reference ligand had -6.7 kcal mol<sup>-1</sup> which indicates a direct correlation between binding score and experimental anticonvulsant activity. Based on both <i>in vivo</i> and <i>in silico</i> findings, compound 3c emerged as the most important candidate, demonstrating superior anti convulsant activity across multiple seizure models. These results underscore the capability of the newly synthesized 2, 4, 5-trichlorobenzenesulfonate based dihydrothiazoles as promising scaffolds for the development of new antiepileptic drugs. Anticonvulsant ; Anticonvulsant drugs ; Drug ; Epilepsy Synthesis and biological activity ; Synthesis of Tetrazole Derivatives ; Enzyme function and inhibition
Authors: Shouwei Gao | Xu Zheng | Dongsheng Luo | Sheng Di | Wenqian Dong
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation The rapid growth of scientific machine learning (SciML) has accelerated discovery across diverse domains, yet designing effective SciML models remains a challenging task. In practice, building such models often requires substantial prior knowledge and manual expertise, particularly in determining which input features to use and how large the model should be. We introduce LUMOS, an end-to-end framework based on L0-regularized learning that unifies feature selection and model pruning to democratize SciML model design. By employing semi-stochastic gating and reparameterization techniques, LUMOS dynamically selects informative features and prunes redundant parameters during training, reducing the reliance on manual tuning while maintaining predictive accuracy. We evaluate LUMOS across 13 diverse SciML workloads, including cosmology and molecular sciences, and demonstrate its effectiveness and generalizability. Experiments on 13 SciML models show that LUMOS achieves 71.45% parameter reduction and a 6.4x inference speedup on average. Furthermore, Distributed Data Parallel (DDP) training on up to eight GPUs confirms the scalability of Scalability ; Workflow ; Pruning ; Feature (linguistics) ; Inference ; Adaptation (eye) ; Speedup ; Feature selection Machine Learning in Materials Science ; Machine Learning and Data Classification ; Domain Adaptation and Few-Shot Learning
Authors: Aman Sharma | Paras Chopra
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages Large language models achieve near-ceiling performance on code generation benchmarks, yet these results increasingly reflect memorization rather than genuine reasoning. We introduce EsoLang-Bench, a benchmark using five esoteric programming languages (Brainfuck, Befunge-98, Whitespace, Unlambda, and Shakespeare) that lack benchmark gaming incentives due to their economic irrationality for pre-training. These languages require the same computational primitives as mainstream programming but have 1,000-100,000x fewer public repositories than Python (based on GitHub search counts). We evaluate five frontier models across five prompting strategies and find a dramatic capability gap: models achieving 85-95% on standard benchmarks score only 0-11% on equivalent esoteric tasks, with 0% accuracy beyond the Easy tier. Few-shot learning and self-reflection fail to improve performance, suggesting these techniques exploit training priors rather than enabling genuine learning. EsoLang-Bench provides the first benchmark designed to mimic human learning by acquiring new languages through documentation, interpreter feedback, and iterative experimentation, measuring transferable reasoning skills resistant to data contamination. Exploit ; Benchmark (surveying) ; Python (programming language) ; Second-generation programming language ; Language model ; Programming paradigm ; Sketch ; Lexicographical order Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Nico Unglert | Michael Ketter | Georg K. H. Madsen
Source: npj Computational Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10-15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions. Sampling (signal processing) ; Computation ; Process (computing) ; Phase (matter) ; Phase diagram ; Fraction (chemistry) ; Reliability (semiconductor) ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Vidhya Prasanth
Source: Journal on Materials and its Characterization (article, 2026-02-23)
Field: Materials Science — Polymers and Plastics

Biomedical Applications of Polymer-Composite Materials using Fuzzy ARAS Method Polymer composites typically two primary components Contains: A polymer matrix and a Reinforcement phase. Polymer matrix, serving as the continuous phase, provides cohesion, shape, and protection to the composite. Various thermoset and thermoplastic polymers, such as epoxies, polyesters, polyamides, and polypropylene, are chosen as matrix materials based on their processing characteristics and desired properties. Meanwhile, the reinforcement phase enhances the matrix, boosting Mechanical of mixing, Thermal and Electrical properties. Reinforcements come in forms like fibers, particles, or additives, each offering unique advantages. Common fiber reinforcements include glass, carbon, aramid, and natural fibers like hemp and flax, known for their high strength-to-weight ratios and tailored mechanical properties. In materials science and engineering, polymer composites are recognized as versatile and innovative solutions with applications across various industries. These materials, characterized by a combination of distinct constituents with complementary properties, have seen significant advancements over time. The history of polymer composites dates back centuries, with early instances of rudimentary composite materials found in ancient civilizations. For instance, ancient Egyptians used straw-reinforced clay for constructing bricks, showing early experimentation with composites. However, substantial progress in polymer composites occurred in the 20th century, driven by advancements in polymer science, manufacturing technologies, and the pursuit of materials with superior properties. The mid-20th century marked a pivotal moment with the introduction of fiberglass-reinforced polymers (FRPs) pioneered by Owens Corning. This innovation revolutionized industries like aerospace, automotive, and construction. The development of synthetic polymers such as epoxy, polyester, and vinyl ester further propelled the growth of polymer composites, allowing for tailored formulations to meet specific performance requirements. Bio-based Polymer Composites, Recycled Polymer Composites and Nanostructured Polymer Composites. Mechanical Properties, Thermal Properties, Electrical Properties and Chemical Resistance. The FUZZY ARAS ranking of Polymer Composite Materials indicates the highest value for Recycled Polymer Composites and the lowest value for Bio-based Polymer Composites. Thermosetting polymer ; Polymer ; Composite number ; Thermoplastic polymer ; Synthetic fiber ; Thermoplastic ; Biomimetics ; Reinforcement Natural Fiber Reinforced Composites ; Scientific and Engineering Research Topics ; Internet of Things and AI