jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-23)
Field: Materials Science — Polymers and Plastics

Hyper-Adaptive Microencapsulation for Dynamic Moisture Management in Smart Athletic Textiles **Abstract:** This paper details a novel approach to dynamic moisture management in athletic textiles utilizing hyper-adaptive microencapsulation (HAM). Existing moisture-wicking fabrics offer limited dynamic response to varying humidity levels. HAM significantly improves upon this by incorporating microcapsules containing a stimuli-responsive polymer network exhibiting a switchable hydrophilic/hydrophobic behavior in response to environmental humidity. This technology, coupled with a layered fabric architecture and a reinforcement learning-based control system, allows for proactive moisture regulation, maximizing comfort and performance. We demonstrate a 30-45% improvement in moisture evaporation rate compared to leading moisture-wicking fabrics, along with significantly enhanced thermal comfort scores based on PMV/PPD modeling. The system readily leverages existing microencapsulation and polymer chemistry, offering a rapid pathway towards commercialization. Moisture ; Humidity ; Evaporation ; Thermal comfort ; Water content Textile materials and evaluations ; Microencapsulation and Drying Processes ; Hydrogels: synthesis, properties, applications
Authors: Xingang Li (319129) | Jiefeng Liu (9194412) | Wen Li (143214) | Heng Zhang (320479) | Chuying Liu (13803777) | Xin Li (51274) | Yongqing Lin (10106231)
Source: Figshare (article, 2025-08-26)
Field: Materials Science — Polymers and Plastics

Research onThermal Conductivity Prediction of NaturalEster Insulating Oil Based on Transfer Learning This paper proposes a thermal conductivity prediction model for natural ester insulating oil based on transfer learning, aimed at predicting the thermal conductivity of triglycerides. First, molecular dynamics simulations were used to calculate the thermal conductivity of FR3 vegetable oil and mineral oil at different temperatures, laying a foundation for expanding the data set of triglycerides. Next, molecular descriptors of 10 fatty acid molecules were calculated, and key features related to thermal conductivity were identified. Based on this, two regression models, extreme gradient boosting (XGBoost) and random forest, were used to construct a thermal conductivity prediction model for fatty acids. Knowledge from this model was then transferred to the triglyceride prediction task through transfer learning. The final model achieved an <i>R</i><sup>2</sup> score of 0.97 on the test set, significantly enhancing stability and generalization capability. This study applies transfer learning, enabling high-accuracy predictions even with small sample sizes. The developed prediction model can be integrated with molecular design, providing a scientific basis for designing insulating oils with high thermal conductivity. This not only promotes the development and application of new materials but also facilitates the optimization and sustainable development of energy systems. Thermal conductivity ; Heat transfer ; Generalization ; Thermal ; Transfer of learning ; Thermal conductivity measurement ; Thermal stability ; Construct (python library) Polymer composites and self-healing ; Thermal properties of materials ; Power Transformer Diagnostics and Insulation
Authors: Xu Dong
Source: ArXiv.org (preprint, 2025-06-15)
Field: Materials Science — Biomaterials

Analytical coarse grained potential parameterization by Reinforcement Learning for anisotropic cellulose Cellulose nanocrystals (CNCs) are a type of cellulose with excellent mechanical performance and other merit attributes. According to previous reports, hydrogen bonds play a pivotal role in the anisotropic structure of the CNC. Understanding the structure and mechanical behavior of CNC on a mesoscopic scale is critical for the development and manufacture of cellulose materials. However, experimental observations and atomistic simulations are not appropriate on the mesoscopic scale. In this study, we introduce an analytical coarse-grained (CG) potential following an extended bottom-up approach that is directly parameterized using Reinforcement Learning (RL). RL is a powerful tool for industrial and academic applications in various fields. Nevertheless, the potential of RL has not yet been fully exploited in the field of molecular dynamics. The RL and Boltzmann inversion methods were employed to develop a novel CG model of cellulose to represent its anisotropy and polymer stiffness. The resultant CG model is not limited to the target properties for training, and can reproduce the dynamics mechanical properties under other circumstances without additional training. This model confirms that RL can construct a CG potential that is both physically explainable and powerful. Anisotropy ; Mesoscopic physics ; Molecular dynamics ; Cellulose ; Process (computing) ; Reinforcement learning ; Parameterized complexity ; Property (philosophy) Advanced Cellulose Research Studies ; Digital Image Processing Techniques ; Advanced Materials and Mechanics
Authors: Suneet Dhanasekaran | Karthika Shanmugam | R. Saravanathamizhan
Source: Chemical Product and Process Modeling (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of temperature profile using reinforcement learning in batch crystallization process Abstract The temperature profile is a critical parameter in industrial batch cooling crystallization processes which directly impacts the crystal size distribution (CSD) and subsequent down streaming operations. Conventional cooling approaches, including natural and linear cooling, often fail to achieve optimal results. Linear cooling may trigger excessive primary nucleation by crossing metastable limits, while natural cooling leads to inconsistent nucleation and growth, resulting in smaller and non-uniform crystals. Hence, optimized cooling profiles are used to achieve the desired target CSD. This study employs reinforcement learning (RL) to optimize the temperature profile for batch cooling crystallization for two systems: paracetamol in water and potassium dihydrogen phosphate (KDP) in aqueous solution. RL dynamically adjusts the cooling trajectory by iteratively interacting with a process model, aiming to maximize mean crystal size while minimizing the coefficient of variation. A comprehensive mathematical model incorporating population balance equations, mass balance, and energy balance is developed to simulate the crystallization process. For the paracetamol system, the RL-optimization strategy resulted in a 107.5 % increase in mean crystal size compared to the natural cooling profile. In the case of the KDP system, a 12 % increase in mean crystal length is achieved relative to a linear cooling profile, along with a significant reduction in the coefficient of variation, indicating improved crystal size uniformity. The optimization results obtained using RL are also compared with that from a genetic algorithm for both cases, and RL demonstrated superior performance. This work underscores the potential of RL in advancing broader applications in chemical process optimization. Crystallization ; Process (computing) ; Reinforcement learning ; Process engineering ; Materials science ; Reinforcement ; Process optimization ; Computer science ; Artificial intelligence ; Chemical engineering ; Engineering ; Composite material Crystallization and Solubility Studies ; Process Optimization and Integration ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Da‐Jiang Liu | James W. Evans
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nanoscale Structure of Ordered Mesoporous Carbon Formed by Nanocasting within a Hard Template: Md Simulation with Ml Potentials Mesoporous material ; Materials science ; Nanoscopic scale ; Nanotechnology ; Carbon fibers ; Molecular dynamics ; Chemistry ; Computational chemistry ; Organic chemistry ; Catalysis ; Composite material Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis ; Dendrimers and Hyperbranched Polymers
Authors: Silvan Käser (9261566) | Jeremy O. Richardson (1345353) | Markus Meuwly (1433455)
Source: Figshare (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Transfer Learningfor Predictive Molecular Simulations:Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy Accurate potential energy surfaces (PESs) are critical for predictive molecular simulations. However, obtaining a PES at the highest levels of quantum chemical accuracy, such as CCSD(T), becomes computationally infeasible as molecular size increases. This work presents CCSD(T)-quality PESs using data-efficient techniques based on transfer learning to obtain state-of-the-art accuracy at a fraction of the computational cost for systems that would otherwise be intractable. Most importantly, the framework for accurate molecular simulations pursued here extends beyond specific observables and follows a rational strategy to obtain highest-accuracy PESs, which can be used for applications to spectroscopy and other experiments. As rigorous tests of the PESs, semiclassical tunnelling splittings for tropolone and the (propiolic acid)–(formic acid) dimer (PFD) as well as anharmonic frequencies for tropolone were determined. For tropolone, all observables are in excellent agreement with the experiment using the high-level PES, whereas for PFD, the agreement is less good but still orders of magnitude better than previous calculations. Tropolone ; Observable ; Semiclassical physics ; Anharmonicity ; Quantum tunnelling ; Work (physics) ; Potential energy ; Energy (signal processing) ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Muhammad Usman (1504405) | Fuyi Chen (561870)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Deep Reinforcement Learning for Structural Optimizationand Potential Energy Landscape of 13-Atom Gold Nanoclusters for Applicationin Nanomaterial Discovery The properties of atomic clusters strongly depend on their structure; therefore, identifying stable structures is critical for materials discovery. In this study, we implemented a deep reinforcement learning (DRL) framework to generate and optimize the gold nanoclusters (Au<sub>13</sub>). The DRL framework is a policy-based actor-critic model with trust region policy optimization (TRPO). The DRL agent effectively navigates the potential energy surface (PES), starting from randomized configurations and progressively learning to identify low-energy and thermodynamically favorable states. The deep reinforcement learning model successfully predicts the global minimum cluster of Au<sub>13</sub> with icosahedral (Ih) symmetry and discovers 25 unique low-energy minima configurations, demonstrating its ability to balance exploration and exploitation effectively. Furthermore, the density functional theory (DFT) confirms the stability of the Au<sub>13</sub> nanocluster obtained from DRL. This work presents a significant advancement in the autonomous structural optimization of nanoclusters, offering insights into their stability and energy landscapes. Moreover, the proposed DRL framework demonstrates strong potential for accelerating nanomaterial discovery by identifying stable nanocluster structures through the efficient exploration of potential energy surfaces (PES). Nanoclusters ; Maxima and minima ; Reinforcement learning ; Icosahedral symmetry ; Stability (learning theory) ; Energy landscape ; Potential energy surface ; Cluster (spacecraft) Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Huijuan Ma | Xiaoying Wei | Peiliao Wang | Zhiang Gong | Zhili Hu | Lin Hua
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — General Materials Science

Mechanical Properties Prediction and Process Parameters Deduction of High-Strength Aluminum Alloy Components Formed Under the Phf Process Process (computing) ; Alloy ; Aluminium ; Materials science ; Computer science ; Metallurgy ; Process engineering ; Engineering Material Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Rıza Özçelik | Sarah de Ruiter | Francesca Grisoni
Source: ArXiv.org (preprint, 2025-07-23)
Field: Chemistry — Physical and Theoretical Chemistry

Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic The scarcity of molecules with desirable properties (i.e., `positive' molecules) is an inherent bottleneck for generative molecule design. To sidestep such obstacle, here we propose molecular task arithmetic: training a model on diverse and abundant negative examples to learn 'property directions' - without accessing any positively labeled data - and moving models in the opposite property directions to generate positive molecules. When analyzed on 33 design experiments with distinct molecular entities (small molecules, proteins), model architectures, and scales, molecular task arithmetic generated more diverse and successful designs than models trained on positive molecules in general. Moreover, we employed molecular task arithmetic in dual-objective and few-shot design tasks. We find that molecular task arithmetic can consistently increase the diversity of designs while maintaining desirable complex design properties, such as good docking scores to a protein. With its simplicity, data efficiency, and performance, molecular task arithmetic bears the potential to become the de facto transfer learning strategy for de novo molecule design. Task (project management) ; Bottleneck ; Generative model ; Property (philosophy) ; De facto Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Jian Chen | Yao Kang | Xudong Wang | Hao Huang | Man Yao
Source: Journal of Zhejiang University. Science A (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Electrostatic potential distribution image-transfer learning method for highly accurate prediction of lithium diffusion barriers on transition metal dichalcogenide surfaces Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB-TMDs was identified. Subsequently, transfer learning techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search calculation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS2 compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials. Saddle point ; Diffusion ; Transition metal ; Lithium (medication) ; Transition point ; Binding energy ; Metric (unit) ; Saddle 2D Materials and Applications ; Advanced Memory and Neural Computing ; Electrocatalysts for Energy Conversion
Authors: Zexin Lin | Zhenzhong Wang | Yunpeng Gong | Shengming Gu | Xuan Wei | Min Jiang
Source:  (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

SDSR: A Multi-Decoder Symbolic Regression Framework for Solving Coupled PDE Systems Accurate and interpretable modeling of multivariate systems is critical in a wide range of scientific and engineering domains, including fluid dynamics, material modeling, and biological processes. However, deriving compact symbolic representations directly from data remains a significant challenge due to the combinatorial growth of expression spaces and the complex dependencies among multiple coupled target functions. In this paper, we propose SDSR, a Synergistic Deep Symbolic Regression framework for learning human-readable solutions to multivariate regression problems. SDSR employs multiple independent Long Short-Term Memory (LSTM) decoders to simultaneously generate symbolic expressions for all target functions, while incorporating a hidden state fusion mechanism that enables decoders to share contextual information during sampling. To improve search efficiency and progressively build expression complexity, the framework adopts a curriculum learning strategy that gradually increases the allowable expression length. The approach is trained end-to-end via policy gradient reinforcement learning. Experimental results on representative benchmarks demonstrate that SDSR can discover accurate and interpretable symbolic expressions. This work provides a generalizable approach for interpretable symbolic modeling in real-world applications. Symbolic regression ; Expression (computer science) ; Symbolic data analysis ; Regression ; Multivariate statistics ; Range (aeronautics) ; State (computer science) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Evolutionary Algorithms and Applications
Authors: Jone Ugarte Valdivielso | Jose I. Aizpurua | Manex Barrenetxea | Brian Stewart
Source: ArXiv.org (preprint, 2025-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers Transformers are essential components for the reliable operation of power grids. The transformer core is constituted by a ferromagnetic material, and accordingly, depending on the magnetization state, the energization of the transformer can lead to high magnetizing inrush currents. Such high amplitudes shorten the life expectancy of a transformer and cause power quality issues in power grids. Various techniques have been proposed to minimize the inrush current; however, the application of Reinforcement Learning (RL) for this challenge has not been investigated. RL incorporates the ability to learn inrush minimization strategies adjusted to the dynamic transformer operation environment. This study proposes an inrush current minimization framework by combining controlled switching with RL. Depending on the opening angle of the circuit breaker and the remanent fluxes at disconnection, the proposed method learns the optimal closing instant of the circuit breaker. Two RL algorithms have been trained and tested through an equivalent duality-based model of a real 7.4 MVA power transformer. The evaluation of the RL algorithms is carried out with real measurement data and compared with real laboratory inrush currents. The results show that the inrush current is effectively minimized with the proposed framework. Inrush current ; Circuit breaker ; Transformer ; Minification ; Control theory (sociology) ; Current transformer Magnetic Properties and Applications ; Power Systems Fault Detection ; Power System Optimization and Stability
Authors: Steven Lanier
Source: Journal of Quantitative Spectroscopy and Radiative Transfer (article, 2025-11-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning to precondition: Reinforcement learning enhanced three-level circulant preconditioning for the Discrete Dipole Approximation Preconditioner ; Circulant matrix ; Solver ; Reinforcement learning ; Stability (learning theory) ; Convergence (economics) ; Iterative method ; Range (aeronautics) Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Electromagnetic Simulation and Numerical Methods
Authors: Kai Tan | Fan Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

D-PAC: A Data-Efficient, Physics-Guided, Agent-Based, and Cost-Aware Framework for Multi-Layer Shielding Design in Compact Nuclear Reactors Using Deep Reinforcement Learning Electromagnetic shielding ; Modular design ; Reinforcement learning ; Initialization ; Optimal design ; Adaptation (eye) ; Energy (signal processing) ; Radiation shielding Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Sparavigna, Amelia Carolina | Gemini (Modello Linguistico di Google)
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

L'Orizzonte dell'AI nella Spettroscopia Raman: Verso la Creazione Autonoma di Nuovi Materiali e la Diagnostica Quantistica in Tempo Reale Questo documento delinea il futuro di frontiera della ricerca che integra l'Intelligenza Artificiale (AI) con la spettroscopia Raman, spostando l'obiettivo dall'ottimizzazione dei parametri sperimentali alla scoperta scientifica autonoma e alla creazione concettuale di nuovi materiali. Partendo dal nostro approccio metodologico degli pseudo-spettri per la valutazione della qualità, si esplorano tre direzioni che promettono di trasformare il laboratorio: 1) Sintesi Spettroscopica Ipotetica (SS-I), che utilizza Modelli Generativi (come Transformer e Flow-based Models) per predire lo spettro Raman di materiali non ancora sintetizzati, trasformando l'AI in un co-progettista di composti teorici; 2) Reinforcement Learning per la Scoperta Autonoma di Nuove Fasi (RL-Fase), in cui un Agente è addestrato con una Ricompensa per la "Novità Chimica" per cercare attivamente e caratterizzare le fasi sconosciute in un campione complesso; e 3) Spettroscopia Quantistica-AI in Tempo Reale, che crea modelli surrogate per correlare istantaneamente lo spettro misurato con le proprietà quantistiche fondamentali (come la carica atomica o le energie di legame), bypassando i lunghi calcoli di Chimica Quantistica (DFT). Queste direzioni puntano a stabilire una scienza sperimentale completamente nuova, guidata dall'AI. Transformer ; Computer aided learning Machine Learning in Materials Science ; Cold Fusion and Nuclear Reactions ; Economic and Technological Innovation
Authors: Hao Yu | Yongliang Li
Source: Research Square (preprint, 2025-10-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and application of high stability yellow CDs fluorescence conversion materials for marine lighting Diode ; Quantum dot ; Characterization (materials science) ; Carbon fibers ; Fluorescence ; Laser ; Carbon quantum dots Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Carbon and Quantum Dots Applications