jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuya Yamazaki | Wei Nong | Ruiming Zhu | Kostya S. Novoselov | A. Ustyuzhanin | Kedar Hippalgaonkar
Source: ArXiv.org (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces. However, this approach faces two primary challenges: data scarcity for functional properties and the multi-objective optimization required to balance competing tasks. Here, we present a multi-property-directed generative framework designed to overcome these limitations and enhance site symmetry-compliant crystal generation beyond P1 (translational) symmetry. By incorporating Wyckoff-position-based data augmentation and transfer learning, our framework effectively handles sparse and small functional datasets, enabling the generation of new stable materials simultaneously conditioned on targeted space group, band gap, and formation energy. Using this approach, we identified previously unknown thermodynamically and lattice-dynamically stable semiconductors in tetragonal, trigonal, and cubic systems, with bandgaps ranging from 0.13 to 2.20 eV, as validated by density functional theory (DFT) calculations. Additionally, we assessed their thermoelectric descriptors using DFT, indicating their potential suitability for thermoelectric applications. We believe our integrated framework represents a significant step forward in generative design of inorganic materials. Generative grammar ; Transformative learning ; Transfer of learning ; Generative Design ; Ranging ; Space (punctuation) ; Curse of dimensionality Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Fernando Arrais Romero Dias Lima | Ruan de Rezende Faria | Marcellus G.F. de Moraes | Argimiro R. Secchi | Idelfonso B. R. Nogueira | Maurício B. de Souza
Source: IFAC-PapersOnLine (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Controlling Paracetamol Batch Crystallization in Ethanol by Reinforcement Learning In this work, we proposed a controller based on reinforcement learning (RL) for the unseeded batch crystallization of paracetamol in ethanol. The controller aims to achieve the crystal mean volume size and the crystal mass at fve different targets by manipulating the temperature. We used the deep deterministic policy gradient (DDPG) algorithm to train the control agent. The performance of the RL controller was compared to an NMPC using a population balance model (PBM) as its internal model and tested for the fve different scenarios. The controllers were also tested taking into account a 5% disturbance in the concentration measurement. Both controllers were able to reach values of the controlled variables close to the targets even accounting for disturbances. However, the RL controller was able to calculate the control action much faster than the NMPC and imposed less temperature changes, which presents as better alternative for real control applications. Therefore, the RL controller presented as a more suitable approach than an NMPC using a PBM as its internal model for controlling the paracetamol batch crystallization process. Crystallization ; Ethanol ; Reinforcement learning ; Reinforcement ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Composite material ; Organic chemistry Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Yucheng Zhang | Junyu Luo | Hao Li | Wen-Rui Li | Wendong Li | Ming Zhang | Guanjun Zhang
Source: Macromolecules (article, 2025-12-08)
Field: Materials Science — Polymers and Plastics

Development of Disulfide Epoxy Vitrimers via Transfer Learning: Bridging the Gap between Excellent Comprehensive Properties and Dynamic Cross-Linking Networks Vitrimers are some cross-linked polymers with sustainability introduced by dynamic covalent bonds. However, vitrimers may exhibit more compromised thermal, mechanical, and electrical properties than traditional thermosets. Due to the structural diversity and complexity, developing vitrimers with both excellent comprehensive properties and eco-friendly characteristics remains challenging without quantitative structure–property relationships (QSPRs). Herein, we construct 2 data sets for disulfide vitrimers: one data set containing +100,000 entries of organic molecular properties and another data set with +1700 experimental entries for macroscopic properties of epoxy resins. A QSPR model (AdaQSPR) based on a 3D molecular representation learning framework and transfer learning is developed, which can efficiently and accurately predict the glass-transition temperature (Tg), initial thermal decomposition temperature (Td5%), tensile strength (TS), relative permittivity (ε), dielectric loss (tanδ), and electrical breakdown strength (Eb) of epoxy resins. This model screens 216 vitrimers based on common disulfides and selects 5 promising candidates. Experimental tests verify their good comprehensive properties: e.g., for all selected vitrimers, Tg > 146 °C, Eb > 34.8 kV/mm; four selected vitrimers have TS ≥ 60 MPa. These vitrimers also possess repairability and degradability. This QSPR model can also be utilized for other novel epoxy resins to accelerate research and reduce R&D costs. Epoxy ; Bridging (networking) ; Polymer ; Ultimate tensile strength ; Thermal decomposition ; Dielectric ; Quantitative structure–activity relationship ; Permittivity Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Flame retardant materials and properties
Authors: Yilin Chen | Tianyu Lu | Chenglong Zhao | Hannah K. Wayment-Steele | Po‐Ssu Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-06)
Field: Materials Science — Materials Chemistry

SLAE: Strictly Local All-atom Environment for Protein Representation Building physically grounded protein representations is central to computational biology, yet most existing approaches rely on sequence-pretrained language models or backbone-only graphs that overlook side-chain geometry and chemical detail. We present SLAE, a unified all-atom framework for learning protein representations from each residue's local atomic neighborhood using only atom types and interatomic geometries. To encourage expressive feature extraction, we introduce a novel multi-task autoencoder objective that combines coordinate reconstruction, sequence recovery, and energy regression. SLAE reconstructs all-atom structures with high fidelity from latent residue environments and achieves state-of-the-art performance across diverse downstream tasks via transfer learning. SLAE's latent space is chemically informative and environmentally sensitive, enabling quantitative assessment of structural qualities and smooth interpolation between conformations at all-atom resolution. Autoencoder ; Representation (politics) ; Interpolation (computer graphics) ; Feature (linguistics) ; Sequence (biology) ; Feature learning ; Fidelity Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Annemie Bogaerts | Gabriele Centi | Jason C. Hicks
Source: EES Catalysis (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Introduction to understanding and new approaches to create synergy between catalysis and plasma themed collection Annemie Bogaerts, Gabriele Centi and Jason Hicks introduce the EES Catalysis themed collection on understanding and new approaches to create synergy between catalysis and plasma. Nanotechnology ; Computer science ; Materials science Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Nanomaterials for catalytic reactions
Authors: Ferenc Kubicsek | Ferenc Hegedűs | Péter Csizmadia
Source: Archive of Mechanical Engineering (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Optimization of sonochemical ammonia synthesis in non-Newtonian fluids This study investigates the theoretical energy intensity of ammonia production via sonochemical reactions in non-Newtonian test fluids. A single, freely oscillating microbubble containing nitrogen and hydrogen is modelled using the Keller–Miksis equation, the first law of thermodynamics, and a detailed reaction mechanism. The goal is to assess whether modifying fluid properties can improve the energy intensity compared to conventional methods. Key parameters—including bubble size, ambient pressure, gas composition, and rheological properties—are varied systematically. The total energy input includes bubble expansion work, hydrogen production via electrolysis, and gas compression energy. The lowest energy intensity achieved is 682.6 GJ/t, which is 17.5 times higher than the Haber–Bosch process using renewable hydrogen. Compared to previous studies in water, a 5.3% improvement is observed, attributed primarily to the increased sound speed. The non-Newtonian rheology had only minor influence, suggesting that future efforts should focus on optimizing acoustic parameters and fluid compressibility rather than rheological properties. Ammonia production ; Non-Newtonian fluid ; Ammonia ; Materials science ; Newtonian fluid ; Chemical engineering ; Chemistry ; Thermodynamics ; Engineering ; Organic chemistry ; Physics Ultrasound and Cavitation Phenomena
Authors: Roya Darabi | Ana Reis | J De
Source: Advanced Engineering Materials (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

Exploring the Dynamic Transition from Aging to Additive Manufacturing: Investigating Microstructural Evolution of Metastable γ″ Precipitates in Inconel 625 through Coupled Phase‐Field Simulation This study investigates the microstructural evolution during the coprecipitation of metastable γ″ (D022‐Ni3Nb) particles within a supersaturated γ matrix in commercial IN625 Ni‐based superalloy. The research aims to comprehensively understand the morphological evolution and fraction distribution of precipitate phases to enhance material strength and guide the design of additive manufacturing processes. A sophisticated coupled computational phase‐field model is developed. The novel phase‐field model within the open‐source multiphysics object oriented simulation environment finite element framework enables the efficient construction of coupled phase‐field mechanics models for multiphase systems. The quantitative precipitation model incorporates essential inputs, such as ab initio calculations, experimental data, precipitate–matrix orientation relationships, interfacial energy, interdiffusivities, and a tailored thermodynamic database. Simulation results reveal the intricate interplay of alloy composition, lattice misfit, external stress, temperature, and time, influencing the dynamic microstructure evolution during additive manufacturing. The findings highlight the significant influence of thermal cycles and process parameters on γ″ precipitate formation, directly impacting material properties and volumetric fraction. Leveraging experimental‐driven formulations for coherent strength optimization aims to maximize γ″ precipitates while minimizing postprocessing costs. Materials science ; Metastability ; Inconel ; Phase transition ; Field (mathematics) ; Inconel 625 ; Metallurgy ; Phase field models ; Phase (matter) ; Microstructure ; Thermodynamics ; Alloy Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Mariana Rusu | Irina Gabriela CARA | Manuela Filip | Denis Țopa | G. Jităreanu
Source: Journal of Applied Life Sciences and Environment (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

COMPARATIVE ANALYSIS OF DIGESTION METHODS FOR QUANTIFYING HEAVY METALS IN PLUM ORCHARDS Increasing interest in healthy food among the population raises concerns about heavy metals in fruit and their impact on public health. To assess this issue, this study presents a comparative analysis of digestion methods for quantifying heavy metals in plum orchards managed under conventional and ecological practices in the “Adamachi” Farm district of Iasi University of Life Sciences (IULS). We evaluated and optimised two wet digestion methods - in an open system and microwave-assisted - to determine the concentrations of heavy metals, such as copper (Cu), zinc (Zn), nickel (Ni), lead (Pb), and cadmium (Cd), which pose environmental and health risks. The metal concentrations were measured using an atomic absorption spectrophotometer, according to standard methods. Microwave-assisted digestion was more efficient and faster than the conventional method (in an open system). The ecologically managed orchards showed a lower heavy metal content overall, with the exception of Cu levels, due to the Cu-based treatments. By determining the estimated daily intake (EDI), target hazard quotient (THQ), and hazard index (HI) for both children and adults, the potential health risks from heavy metals were determined. There were no related associated risks to human health (THQ and HI &lt; 1), and the accumulated metals in plum fruit samples showed that the EDI values followed the descending order of Cu &gt; Zn &gt; Ni &gt; Pb &gt; Cd. The analysis revealed non-significant differences for most data obtained after processing using the two methods. These results highlight the importance of selecting the optimal digestion methods for heavy metal analysis in plums and sustainable agricultural practices to safeguard the environment and consumer health from heavy metal contamination. Heavy metals ; Digestion (alchemy) ; Environmental science ; Chemistry ; Environmental chemistry ; Chromatography Heavy Metals in Plants
Authors: Qiangguo Zhou
Source: Journal of Applied Physics (article, 2025-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-band tunable terahertz linear dichroism metasurface based on photosensitive silicon Metasurfaces, with their precise manipulation of electromagnetic waves, provide an ideal platform for realizing efficient terahertz linear dichroism (LD) devices. However, the existing LD metasurfaces primarily rely on fixed structures, making it difficult to achieve multi-band response and dynamic control, thereby limiting their application in complex scenarios. This article first proposes a dual-band terahertz LD metasurface composed of asymmetric double-opening square ring and double-opening circular ring. The results show that the device exhibits a LD value of 0.99 at 0.850 THz and 0.98 at 1.094 THz, demonstrating a significant LD effect. By systematically analyzing the influence of structural parameters on device performance, this study provides guidance for designing high-performance LD metasurfaces. More importantly, by embedding photosensitive silicon at specific positions in the unit structure, dynamic regulation of the LD effect is achieved through external optical excitation. The results demonstrate that under all-optical modulation, the LD amplitudes at the two frequency points achieve modulation depths of 98.1% and 95.7%, respectively. As the optical pumping power increases, a new resonant peak appears near 1.049 THz, with an LD value of 0.966. This phenomenon indicates that the device has the capability to switch between different frequencies, demonstrating excellent LD tuning performance. The proposed terahertz LD metasurface possesses dual-band response and dynamic tuning capabilities, holding promise for key applications in the fields such as biosensing, molecular orientation analysis, and terahertz communication. Terahertz radiation ; Silicon ; Modulation (music) ; Dichroism ; Circular dichroism ; Terahertz spectroscopy and technology ; Linear dichroism ; Square (algebra) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Sinem Uzun | Yasin Genç | Hanan Rifai | Ouadie Kabach | Bedrettin Uzun | Zouhair Sadoune | El Mahjoub Chakir | Hamid Amsil | Fadi El Banni
Source: Nuclear Energy and Technology (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Thermal-hydraulic investigation of novel thorium-uranium fuel mixtures in advanced SMR assembly configurations Nuclear energy sustainability and deployment flexibility can be significantly enhanced through Small Modular Reactors (SMRs) technology. Critical to their operational success is the thorough assessment of thermal-hydraulic characteristics, especially when incorporating advanced fuel design concepts. This research conducts an extensive thermal-hydraulic analysis examining various thorium-based fuel formulations, including (Th- 235 U)O 2 , (Th- 233 U)O 2 , and an innovative (Th- 233 U- 235 U)O 2 composition, benchmarked against standard UO 2 fuel. The investigation encompasses both solid fuel arrangements and dual-cooled annular assembly designs, focusing on safety optimization and operational efficiency enhancement. The analysis focuses on key safety parameters, including pressure drop, coolant enthalpy, fuel centerline temperature, and Departure from Nucleate Boiling Ratio (DNBR). Results for solid fuel configurations reveal that thorium-based fuels exhibit reduced pressure drop, more efficient enthalpy distribution, lower peak fuel temperatures, and higher DNBR values compared to conventional UO 2 , highlighting improved thermal stability and safety margins. The (Th- 233 U- 235 U)O 2 mixture demonstrates a balanced performance by mitigating the limitations of other thorium compositions. In annular configurations, all fuel types benefit from enhanced heat removal due to the dual cooling surfaces, resulting in further reductions in pressure drop and peak temperatures, as well as a significant increase in DNBR values. The highest DNBR, reaching up to 3.051, confirms the annular geometry’s superior safety performance against boiling crises. Coolant ; Inherent safety ; Pressure drop ; Modular design ; Boiling ; Fuel element failure ; Thorium fuel cycle ; Flexibility (engineering) ; Nuclear fuel ; Solid fuel Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Brown, Nathan
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Molecular Design Platforms Integrating Chemists, Biologists, and Machines The rise of new drug modalities demands discovery platforms that unite diverse scientific domains. In this talk, I will share lessons from leading digital chemistry and AI-driven drug discovery initiatives based on my experience working recently in the TechBio sector, focusing on how to design platforms that empower multidisciplinary teams. I will explore key technical and organisational principles: flexible data structures, user-centric interfaces, AI integration at point-of-need, and real-time collaboration across chemistry, biology, and informatics specialists and data. Special attention will be given to bridging scientific mindsets - from the hypothesis-driven chemist to the data-driven AI scientist - and building a common working language and flow. Drawing on real-world examples, I will show how thoughtfully designed systems can accelerate innovation across molecular types and therapeutic areas. Finally, I will sketch a vision for the next generation of discovery platforms: modality-agnostic, continuously learning, and capturing the full creativity of human and machine discovery. Sketch ; Bridging (networking) ; Multidisciplinary approach ; Modalities ; Creativity ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yanzhen Wang | Yiyang Jiang | Diana Golovanova | Kamal Das | Hyeonhu Bae | Yufei Zhao | Huu-Thong Le | Abhinava Chatterjee | Yunzhe Liu | Chao-Xing Liu | Felipe H. da Jornada | Binghai Yan | Xiao-Liang Qi
Source: arXiv (Cornell University) (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

QMBench: A Research Level Benchmark for Quantum Materials Research We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model's ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community. Benchmark (surveying) ; Quantum ; Quantum computer ; Symmetry (geometry) ; Quantum information science ; Quantum information Machine Learning in Materials Science ; Quantum many-body systems ; Electronic and Structural Properties of Oxides
Authors: Yanzhen Wang | Yiyang Jiang | Diana Golovanova | Kamal Das | Hyeonhu Bae | Yufei Zhao | Huu-Thong Le | Abhinava Chatterjee | Yunzhe Liu | Chao-Xing Liu | Felipe H. da Jornada | Binghai Yan | Xiao-Liang Qi
Source: ArXiv.org (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

QMBench: A Research Level Benchmark for Quantum Materials Research We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model's ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community. Benchmark (surveying) ; Quantum ; Quantum computer ; Symmetry (geometry) ; Quantum information science ; Quantum information Machine Learning in Materials Science ; Quantum many-body systems ; Electronic and Structural Properties of Oxides