jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thi Anh
Nguyet Nguyen (11192009) | Fan-Ching Chien (844928) | Thuy Doan Khanh Huynh (21009752) | Huy Kim Nhat (21009755) | Yu-Chi Chiu (344336) | Hao-Tsung Yang (21009758) | Chen-Yi Yu (20634901) | Chih-Ming Wang (1603054) | Jian-Zong Lai (16950683) | Duy Thanh Cu (21009761) | Chien Cheng Kuo (21009764) | Kun-Yu Lai (1423363)
Source: Figshare (article, 2025-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid-Free DNATest by Band Engineering of Nitride Semiconductor and Machine Learning Deoxyribonucleic acid (DNA) testing is a key step in personalized medical treatments. The technique often involves hybridization between complementary single DNA strands to identify the target gene. However, the formation of a hybrid DNA is slow. To capture the target quickly, we present a label-free hybrid-free DNA detection by surface-enhanced Raman spectroscopy (SERS), whose performances are boosted by InGaN quantum wells (QWs) and machine learning. This is realized by tuning the energy states of QWs, within which the confined electrons resonate with those vibrating on the oligonucleotide and the roughened aluminum (Al) surface. The QW-Al-DNA resonance promotes many minor SERS signals to the detectable level, allowing the machine to identify four distinct circulating tumor DNAs responsible for pancreatic, thyroid, lung, and breast cancers in 1 h. DNA ; Oligonucleotide ; Energy (signal processing) ; Electron ; Semiconductor ; Resonance (particle physics) ; Quantum dot ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Vishikh Athavale (12623809) | Nikita Fedik (5869697) | William Colglazier (22313294) | Anders M. N. Niklasson (2851028) | Maksim Kulichenko (6650072) | Sergei Tretiak (565803)
Source: Figshare (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

PYSEQM 2.0: Accelerated Semiempirical Excited-StateCalculations on Graphical Processing Units We report the development and implementation of electronic excited-state capabilities for semiempirical quantum chemical methods at both the Configuration Interaction Singles and Time-Dependent Hartree–Fock levels of theory, integrated within the PYSEQM 2.0 software package (https://github.com/lanl/PYSEQM). PYSEQM is a Python-based package designed for efficient and scalable quantum chemical simulations. Leveraging the PyTorch framework enables PYSEQM to benefit from automatic differentiation and GPU acceleration, leading to substantial performance gains in molecular property evaluations. In particular, our implementation enables efficient calculation of excited-state properties for large molecular systems. Benchmarking on systems with up to a thousand atoms demonstrates that excited-state computations can be completed in under a minute on modern GPUs, making this approach particularly suitable for high-throughput screening, real-time feedback in interactive simulations, and large-scale dynamical studies. Additionally, PYSEQM includes a machine learning interface that supports Hamiltonian parameter reoptimization and neural network training. These capabilities open new avenues for data-driven excited-state dynamics simulations, offering a path toward combining quantum chemical rigor with machine learning efficiency. Overall, this work facilitates access to excited-state quantum chemistry for large systems, while laying the foundation for future hybrid quantum-machine-learning approaches in photochemistry, photophysics, and materials discovery. Scalability ; Quantum chemical ; Interface (matter) ; Software ; Quantum ; Benchmarking ; Computation ; Artificial neural network ; Molecular dynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Yifan Zhang | Hongyang Liu | Meiheng Lv | Huaxin Liu | Yinhua Ma | Fangjian Shang | Wenze Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revealing the Thermal Stability Regulation Mechanism of Nitroaromatic Cl-20 Co-Crystal Materials: A Neural Network Potential Simulation Artificial neural network ; Stability (learning theory) ; Mechanism (biology) ; Thermal stability ; Thermal Machine Learning in Materials Science ; Metallurgical and Alloy Processes ; Model Reduction and Neural Networks
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Jinzhe Zeng | John Z. H. Zhang | Tong Zhu
Source: ChemRxiv (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

DeePaTB: A deep learning powerd semi-empirical quantum mechanical method Deep learning approaches achieve high accuracy with low computational cost by leveraging feature representations from modern low-level quantum methods. We present Deep Atomic Density-Based Tight-Binding (DeePaTB), a novel machine learning-based semi-empirical quantum mechanical (ML-SQM) framework, developed upon our recently proposed atomic density-based tight-binding (aTB) method, which can generate the descriptor by Amesp, "eigenvalue of the local density matrix." This neural network-enhanced semi-empirical quantum mechanical model demonstrates remarkable computational efficiency and transferability across diverse chemical systems. The framework successfully models closed-shell configurations, open-shell configurations with unpaired electrons, and environments with external electric fields. Through reaction data training, DeePaTB achieves density functional theory (DFT)-level accuracy while maintaining the computational efficiency characteristic of semi-empirical quantum mechanical methods. This work demonstrates a versatile framework that bridges the accuracy-efficiency trade-off in quantum chemical calculations, offering a promising tool for large-scale computations and reaction mechanism studies. Quantum ; Deep learning ; Artificial intelligence ; Computer science ; Psychology ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Hoda Ghavaminai
Source:  (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Optimization of Mechanical and Thermal Properties of Polymer Composites Reinforced with Carbon Nanotubes Using Deep Neural Networks and Molecular Dynamics در این پژوهش، یک چارچوب ترکیبی مبتنی بر شبکه عصبی عمیق و دینامیک مولکولی برای مطالعه خواص مکانیکی و حرارتی کامپوزیت‌های پلیمری - نانولوله کربنی توسعه داده شد. مدل شبکه عصبی عمیق با استفاده از داده‌های کوانتومی آموزش داده شد و به‌عنوان میدان نیروی دقیق در شبیه‌سازی‌های دینامیک مولکولی مورد استفاده قرار گرفت. نتایج نشان داد که مدل پیشنهادی خطای جذر میانگین مربعات را در مقایسه با میدان‌های نیروی متعارف به‌طور قابل توجهی کاهش می‌دهد. افزودن ۵ درصد وزنی نانولوله کربنی به ماتریس پلیمری، مدول یانگ را تا ۲۲۴ درصد و هدایت حرارتی را تا ۷۴۳ درصد بهبود بخشید. همچنین زمان محاسبات برای سیستم‌های بزرگ‌ مقیاس تا ۹۴ درصد کاهش یافت. مقایسه نتایج شبیه‌سازی با داده‌های تجربی، خطای نسبی کمتر از ۵ درصد را برای خواص مکانیکی و حرارتی تأیید کرد. این پژوهش نشان می‌دهد که مدل ترکیبی توسعه‌یافته می‌تواند به‌عنوان یک ابزار کارآمد و دقیق برای طراحی و بهینه‌سازی نانوکامپوزیت‌های پلیمری در صنایع مختلف از جمله هوافضا، خودروسازی و الکترونیک مورد استفاده قرار گیرد. Carbon nanotube ; Thermal ; Polymer ; Molecular dynamics ; Thermal conductivity ; Composite number Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Physical and Chemical Molecular Interactions
Authors: Deeya Viradia
Source: Journal of undergraduate research in physics and astronomy. (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

ML/AI Discovery of Quantum Materials Quantum materials exhibit unique properties essential for advancements in technology, yet their discovery is hindered by challenges in navigating a vast chemical space. This research explores the integration of machine learning (ML) and genetic algorithms (GAs) to efficiently identify quantum materials with specific properties, such as desired energy levels, transition rates, and crystal structures. The approach leverages graphical molecular representations, tailored GA operations, and ML models to optimize chemical space exploration and reduce computational costs. Quantum chemical ; Quantum ; Space (punctuation) ; Quantum computer ; Energy (signal processing) ; Chemical space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Volodymyr HUTSAYLYUK | Vladyslav Demchyk | Oleh YASNIY | Nadiia Lutsyk | Andrii FIIALKA
Source: Applied Computer Science (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

The modelling of NiTi shape memory alloy functional properties by machine learning methods Shape memory alloys (SMAs) exhibit several unique properties, including superelasticity and the shape memory effect. They can return to their original shape after deformation when heated. SMAs are widely used in various fields of science and technology. Shape memory alloys are functional materials that are used under loading, which in many cases is cyclic in nature. In the present study, the functional properties of NiTi shape memory alloys were modeled using supervised learning methods. The analysis was performed using Orange data mining software, which allows the creation of visual flowcharts and the generation of results in tables and graphs. The modeling was performed on four specimens. For each specimen, several functional properties, such as residual strain range Der and dissipated energy range DWdis. Each data set was divided into two unequal parts - the training and test sets. The training sets comprised 66% of the total data set. The remaining 34% was used for the test set. Among the methods studied, kNN, AdaBoost, Gradient Boosting and Random Forest showed the best results in terms of prediction errors. Therefore, ML learning methods are a powerful and promising tool for solving tasks related to the prediction of functional properties of SMAs. Shape-memory alloy ; Pseudoelasticity ; Test data ; Nickel titanium ; Boosting (machine learning) ; Gradient boosting ; Residual ; Range (aeronautics) ; Test set Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Material Selection and Properties
Authors: Shridhar N Mathad | Raghavendra P. Bakale | Sushant Kakati | Laxmi B. Ulavi
Source: Juniper Online Journal Material Science (article, 2025-12-16)
Field: Chemistry — Inorganic Chemistry

Harnessing Metal-Organic Frameworks (MOFs) and Their Derivatives for Next Generation Energy Devices The advancement of the next generation energy storage and conversion technologies has increased the demand for new materials with tunable structures, high stability, and multifunctional properties.Metal-organic frameworks (MOFs) are a class of porous crystalline materials composed of metal ions and organic linkers that have emerged as promising candidates due to their structural versatility and chemical tailor ability.This review provides an overview of recent progress in harnessing MOFs and their derivatives for energy related applications, including batteries, supercapacitors, and fuel cells.Emphasis is placed on material design strategies, structure property relationships, and performance optimization.Furthermore, challenges associated with scalability, conductivity, and long-term stability are discussed, along with emerging opportunities such as machine learning driven MOF design and green synthesis pathways for sustainable implementation in future energy systems. Energy (signal processing) ; Field (mathematics) ; Energy consumption ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Energetic Materials and Combustion ; Thermal Expansion and Ionic Conductivity
Authors: HASAN ARSLANOĞLU | Ercan Aydoğmuş | Muhammet Aydın
Source: Çanakkale Onsekiz Mart University AVESIS (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System for Biocomposite Material Synthesis and Property Prediction Artificial neural network ; Property (philosophy) ; Biocomposite ; Inference system ; Inference Machine Learning in Materials Science ; Material Selection and Properties ; Fuzzy Logic and Control Systems
Authors: Yan-Yu Xie (22436977) | Xiao-Dong Li (1675453) | Cheng-Xiang Liu (22436980) | Jia-Xin Li (1489726) | Xiu-Ying Liu (22406518) | Jing-Xin Yu (22436983)
Source: Figshare (article, 2025-10-15)
Field: Chemistry — Inorganic Chemistry

Multivariate Metal Organic Frameworks for High-EfficiencyH<sub>2</sub>S/CO<sub>2</sub>/CH<sub>4</sub> Adsorption and Separation:A Combined Molecular Simulation and Machine Learning Study Natural gas with high sulfur content frequently contains harmful impurities, such as CO<sub>2</sub> and H<sub>2</sub>S, which poses a critical demand for the development of highly efficient and selective adsorbent materials to achieve effective purification. In this study, we have systematically investigated and evaluated the adsorption and separation capabilities of metal–organic frameworks (MTV-MOFs) for H<sub>2</sub>S/CO<sub>2</sub>/CH<sub>4</sub> ternary mixture by combining molecular simulation with machine learning techniques. Grand Canonical Monte Carlo (GCMC) simulations reveals that the framework functionalized with the NH<sub>2</sub>–F–F group not only exhibits excellent H<sub>2</sub>S adsorption capacity, but also has excellent separation ability for ternary mixed gas. The cuf_6586 containing functional group NH<sub>2</sub>–F–F can achieve an adsorption capacity of 18.12 mmol/g under the conditions of 298 K and 1 bar. Based on Henry’s constant and adsorption heat, the interaction mechanism between the gas and the framework was deeply analyzed. For separation, cuf_10289, which also contains functional group NH<sub>2</sub>–F–F, is identified as the most effective separation adsorbent. Within the pressure range of 0.1–5 bar, its selectivity for H<sub>2</sub>S and CO<sub>2</sub> is higher than 45. Furthermore, machine learning models were constructed using structural, chemical, and thermodynamic descriptors to predict adsorption capacity and selectivity. Among them, the XGBoost model achieved excellent performance, with an R<sup>2</sup> of 0.93 for regression tasks and a precision of 0.90 for high-performance material classification. SHAP analysis further confirmed the dominant role of thermodynamic descriptors in driving model predictions. This study can provide some theoretical references and an efficient strategy for the high-throughput screening and rational design of advanced adsorbents targeting the purification of high-sulfur natural gas. Ternary operation ; Adsorption ; Separation (statistics) ; Metal-organic framework ; Monte Carlo method ; Multivariate statistics ; Grand canonical ensemble Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Carbon Dioxide Capture Technologies
Authors: Samir Darouich | Vinh Tong | Tanja Bien | Johannes Kästner | Mathias Niepert
Source: ArXiv.org (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

Adaptive Transition State Refinement with Learned Equilibrium Flows Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical processes. However, accurately and efficiently identifying these states remains one of the most challenging problems in computational chemistry. In this work, we introduce a new generative AI approach that improves the quality of initial guesses for TS structures. Our method can be combined with a variety of existing techniques, including both machine learning models and fast, approximate quantum methods, to refine their predictions and bring them closer to chemically accurate results. Applied to TS guesses from a state-of-the-art machine learning model, our approach reduces the median structural error to just 0.088 $\unicode{x212B}$ and lowers the median absolute error in reaction barrier heights to 0.79 kcal mol$^{-1}$. When starting from a widely used tight-binding approximation, it increases the success rate of locating valid TSs by 41\% and speeds up high-level quantum optimization by a factor of three. By making TS searches more accurate, robust, and efficient, this method could accelerate reaction mechanism discovery and support the development of new materials, catalysts, and pharmaceuticals. Quantum chemical ; Variety (cybernetics) ; Quality (philosophy) ; State (computer science) ; Quantum ; Mechanism (biology) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Prashant Rajesh Iyer
Source: Journal of Purdue Undergraduate Research (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Using Neural Networks to Predict Microstructure Evolution in Granular Materials Granular material ; Artificial neural network ; Microstructure ; Feature (linguistics) Machine Learning in Materials Science ; Powder Metallurgy Techniques and Materials ; Geotechnical Engineering and Soil Mechanics
Authors: Anastasia Danshina (21443492) | Ivan Zlobin (21443495) | Svetlana Solov’eva (21443498) | Nikolai Rekut (21443501) | Yulia V. Nelyubina (563717)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Ammonium Organosulfonatesas Machine Learning-Driven“Crystallization Cocktails” for the Structure Determinationof Liquid Molecules A concept of “crystallization cocktails” for crystal structure determination of small (liquid) molecules is introduced, offering a simple one-step procedure of one-pot mixing commercial sulfonic acids (such as <i>p</i>-toluenesulfonic and 1,5-naphtalenedisulfonic) and geometrically diverse amines (such as <i>p</i>-phenylenediamine, 4,4′-oxidianiline, and 4,4′-diamino-3,3′-dichlorodiphenylmethane) in different combinations to cocrystallize with a chosen liquid compound by simple solvent evaporation. These “crystallization cocktails” may adapt to different guests owing to “labile” intermolecular interactions between sulfonate and ammonium ions of diverse geometries and flexibility, as demonstrated by a successful cocrystallization of three model phenol derivatives (2,4-dimethyl-, 2-isopropyl-, and 2-ethylphenol); one of them being characterized by X-ray diffraction for the first time. The guest-to-host ratio in the obtained cocrystals, three of which featured the same ammonium organosulfonate but different phenol guests, was found to depend on the size of the individual components of the “crystallization cocktails”. To get insight into the preferable formation of a cocrystal by some of these “cocktails”as common (descriptive) crystallographic tools failed to provide a definitive answera machine learning (ML) algorithm was developed, which allowed limiting the list of their components to a few most suitable ones for a successful cocrystallization with liquid phenols. “Crystallization cocktails” that can be “mixed to order”and boosted by the MLpave the way toward routine structure determination of liquids or other poorly crystallizing substances by X-ray diffraction, streamlining the search for new chemical compounds and identification of “old” ones in natural products, waste and natural waters, and living organisms. Cocrystal ; Intermolecular force ; Molecule ; Ammonium ; Phenol ; Solvent ; Limiting ; Mixing (physics) ; Substructure Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Molecular spectroscopy and chirality
Authors: Pengxiang Gao | Meiting Lu | Weizhen Liu | Li Zhou
Source: Sensors and Actuators B Chemical (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Machine learning-assisted sensor array based on emission-tunable sulfur quantum dots for discriminating structurally similar dye molecules Fluorescence ; Molecule ; Quantum dot ; Sensor array ; Sulfur ; Binary number ; Discriminative model Carbon and Quantum Dots Applications ; Sulfur Compounds in Biology ; Quantum Dots Synthesis And Properties
Authors: Huiran Zhang (406286) | Yudian Lin (22089452) | Shengzhou Li (17853460) | Mengmeng Dai (839863) | Yu Zhang (12946) | Lei Huang (35191) | Jiangcan Pang (22089455) | Pin Wu (402064) | Junjie Peng (623573) | Zheng Tang (1297200) | Peng Ding (119927) | Xiao Wei (44054) | Na Song (309775) | Dongbo Dai (406285)
Source: Figshare (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Substructure-EnhancedMPNN for Polymer Discovery andKnowledge: A Study in Predicting Glass Transition Temperature Understanding the relationship between polymer structures and the glass transition temperature (<i>T</i><sub>g</sub>) is crucial for the design of high-performance polymers. Machine learning models have shown great potential in accelerating the discovery and development of such materials by uncovering structure–property relationships. However, traditional machine learning models often overlook key structural features, such as functional groups, limiting their ability to effectively capture and represent complex structures of polymers. To address this, we developed the Substructure-Enhanced Message Passing Neural Network (SE-MPNN), which incorporates substructure information across multiple scales, from single atoms to functional groups, to evaluate their contributions to <i>T</i><sub>g</sub>. Results demonstrate that, by decomposing polymer structures into substructures, SE-MPNN provides more intuitive interpretations from the substructure level. These insights are consistent with established chemical understanding and offer valuable guidance for the rational design and optimization of polymer materials. Limiting ; Substructure ; Key (lock) ; Polymer ; Glass transition ; Artificial neural network Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Dynamics and Properties
Authors: Hemanth Surya Sai Sunkara | Shravani Bhat A S | R Namitha | Sushmitha Acharya | Selva Kumar Shekar | Krishnamurthy Sainath | Shabnam Siddiqui
Source: International Journal of Hydrogen Energy (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Hydrogen uptake prediction in porous carbon materials explained by decision tree machine learning Algorithms: From experimental data to interpretable predictions Decision tree ; Interpretability ; Gradient boosting ; Mean squared error ; Boosting (machine learning) ; Stacking ; Residual Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies