jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amric Bonil | Ghader Darbandy | Jan Frede | Moritz Flemming | Christian D. Matthus | Lautaro N. Petrauskas | Juan Wang | Kyung‐Geun Lim | Hans Kleemann
Source: Advanced Functional Materials (article, 2025-01-16)
Field: Materials Science — Polymers and Plastics

Organic Permeable Base Transistors—Reliable Large‐Scale Anodization for High Frequency Devices Abstract Organic permeable base transistors (OPBTs) have demonstrated impressive performance and potential in various applications, such as display driving circuits, light‐emitting transistors, and logic circuits requiring high‐frequency operation. However, large‐scale implementation is hindered by fabrication reliability and repeatability issues, a problem also common with other types of organic transistors, leading to reliance on exceptional “hero” devices. To address this challenge, an electrochemical anodization process is scaled up and optimized for OPBTs to produce consistent performance across an entire 15 cm × 15 cm wafer. By controlling the Al base oxidation, an 87% yield of functional devices and a median transconductance of d −3 is achieved S, proving that the anodization process does not degrade the device performance. Additionally, anodization reduces leakage current to below d −9A, increasing the current gain to a median of 10 6 , and decreases the oxide capacitance ( C ox ) without affecting the transconductance ( g m ), resulting in a driving‐voltage normalized unity‐gain cutoff frequency ( f T / V ) of up to 2.6 MHzV −1 . The validity of the experimental results is confirmed through properly calibrated technology computer‐aided design (TCAD) simulations, which rely on DC and small‐signal AC analysis of OPBTs, based on the underlying physical equations. Materials science ; Transistor ; Anodizing ; Base (topology) ; Scale (ratio) ; Optoelectronics ; Nanotechnology ; Electrical engineering ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Cristina Chiva | Roger Olivella | An Staes | Teresa Mendes Maia | Christian Panse | Karel Stejskal | Thibaut Douché | Bérangère Lombard | Andrea Schuhmann | Damarys Loew | Karl Mechtler | Mariette Matondo | Mandy Rettel | Dominic Helm | Francis Impens | Simon Devos | Anna Shevchenko | Paolo Nanni | Eduard Sabidó
Source: Journal of Proteome Research (article, 2025-01-01)
Field: Chemistry — Spectroscopy

A Multiyear Longitudinal Harmonization Study of Quality Controls in Mass Spectrometry Proteomics Core Facilities Quality control procedures play a pivotal role in ensuring the reliability and consistency of data generated in mass spectrometry-based proteomics laboratories. However, the lack of standardized quality control practices across laboratories poses challenges for data comparability and reproducibility. In response, we conducted a harmonization study within proteomics laboratories of the Core for Life alliance with the aim of establishing a common quality control framework, which facilitates comprehensive quality assessment and identification of potential sources of performance drift. Through collaborative efforts, we developed a consensus quality control standard for longitudinal assessment and adopted common processing software. We generated a 4-year longitudinal data set from multiple instruments and laboratories, which enabled us to assess intra- and interlaboratory variability, to identify causes of performance drift, and to establish community reference values for several quality control parameters. Our study enhances data comparability and reliability and fosters a culture of collaboration and continuous improvement within the proteomics community to ensure the integrity of proteomics data. Comparability ; Harmonization ; Standardization ; Quality (philosophy) ; Data quality ; Reliability (semiconductor) ; Consistency (knowledge bases) ; Computer science ; Data science ; Engineering ; Operations management ; Metric (unit) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Muhammad Umar Farooq | Fuyi Chen
Source: Physica Scripta (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Deep Reinforcement learning generation and optimization of AgPd nanoclusters Abstract The determination and prediction of atomic cluster structures are crucial for nanocluster and materials research, as the molecular structures directly influence the properties of nanoclusters. Traditional quantum chemical calculations are often time-consuming, and structural prediction approaches tend to be computationally expensive. Determining the most stable configurations of nanoclusters is particularly challenging due to their complex energy landscapes and the existence of numerous metastable states. In this study, we employed a novel Deep Reinforcement Learning (DRL) method to investigate the potential energy surfaces (PES) and identify the global minima (GM) structures of Ag-Pd nanoclusters. The DRL agent, through an iterative process of trial and error, and reward feedback, dynamically adapts to the increasing complexity of the energy landscapes. By exploring a wide range of possible configurations, the DRL agent successfully identifies global minima even in the face of high-dimensional and complex PES landscapes. The model’s ability to balance the exploration of new configurations and the exploitation of known low-energy states allows it to efficiently navigate the intricate energy landscapes of nanoclusters. The DRL agent identifies stable configurations faster than traditional optimization algorithms by refining its strategy through continuous learning. To validate the results, density functional theory (DFT) calculations were performed on all nanocluster configurations identified as global minima. The results indicate that all Ag-Pd nanoclusters have negative binding energies, suggesting their stability. Furthermore, the inclusion of Pd has a significant impact on the electronic characteristics of the nanoclusters, as demonstrated by the analysis of the electronic density of states (DOS). Charge analysis, specifically through Bader charge calculations, shows a clear redistribution of charge from Ag to Pd, confirming charge transfer and polarization effects, especially as the Pd content increases. Additionally, optical absorption spectra reveal that Ag-Pd nanoalloys with varying Pd contents exhibit tunable absorption properties across the UV–visible spectrum. This study highlights the critical role of metal composition in determining the structural, electronic, and optical properties of nanoclusters, providing a robust DRL framework for designing stable and versatile nanomaterials. The integration of DRL and DFT provides an efficient methodology for optimizing nanocluster properties, and advancing the development of functional metallic nanostructures. Nanoclusters ; Reinforcement learning ; Computer science ; Biochemical engineering ; Materials science ; Artificial intelligence ; Nanotechnology ; Engineering Nanocluster Synthesis and Applications
Authors: Yi Bi | Yuhao Wang | Yufei Song | Qing Chen | Francesco Ciucci
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Achieving high-performance OER catalysis with dual-site modulated Fe-based perovskites Improving OER activity of Fe-based double perovskite through dual-site modulation. Dual (grammatical number) ; Materials science ; Perovskite (structure) ; Catalysis ; Optoelectronics ; Nanotechnology ; Chemistry ; Crystallography Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Onofre Rojas | G. V. Mirzoyan | Zhirayr Adamyan | V. V. Papoyan | G. Amatuni | N.S. Ananikian
Source: Scientific Reports (article, 2025-04-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic properties and entanglement in antiferromagnetic interactions in copper(II) dinuclear and trinuclear complexes In this work, the synthesis, structural characterization, and magnetic properties of a series of dinuclear and trinuclear antiferromagnetic copper (II) complexes with different bridging ligands are studied. The research employs antiferromagnetic theoretical models and experimental data to analyze the magnetic characteristics, including magnetization plateaus, magnetic susceptibility, and entanglement entropy. The results demonstrate significant changes in the magnetization behavior of the complexes, revealing distinct 1/3 magnetization plateaus and saturation points corresponding to different exchange interactions. The entanglement entropy analyses further correlate these magnetic transitions, offering insights into the quantum behavior of these metal-containing spin-1/2 compounds at low temperatures. These results highlight the importance of ligand design in tuning the magnetic properties of copper (II) complexes, providing a foundation for future studies of similar antiferromagnetic systems. Antiferromagnetism ; Copper ; Quantum entanglement ; Chemistry ; Crystallography ; Condensed matter physics ; Physics ; Quantum ; Quantum mechanics ; Organic chemistry Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Quantum many-body systems
Authors: Shiya Cao | Huaqiang Fu | Pengfei Chen | Hao Feng | Ziran Zhang | Ziqi Lin | Zhe Wang | Wei Qian | Chao Tian | Lun Li | Daping He
Source: Cell Reports Physical Science (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Weldable graphene foams for wide-range thermal switches <h2>Summary</h2> Dynamic thermal regulation has become the pivotal issue for future electronics thermal management. Porous elastic materials have been widely developed as smart thermal switches, exhibiting compression-induced tunable heat transfer behavior. However, such tunability is severely restricted by the limited out-of-plane macroscale. Herein, we report a multilayer welding strategy for fabricating graphene foam (GF) thermal switches with wide tuning ranges (TRs) and high switch ratios (SRs). Infinitely expanding the out-of-plane size is realized by constructing a reliable welding interface, leveraging the capillary force and hydrogen bonding within graphene oxide foam. After graphitization, the obtained multilayer GFs (M-GFs) demonstrate excellent elasticity and wide thermal resistance TRs. Specifically, the 7-layer GFs demonstrate an ultra-wide TR of thermal resistance (15.49–604.85 cm<sup>2</sup> K W<sup>−1</sup>) and a much-improved SR (39.0) within the elastic range. When applied to lithium-ion battery thermal management, M-GFs ensure safe temperature and maintain discharge capacity, showing great potential in dynamic thermal management. Graphene ; Materials science ; Thermal ; Range (aeronautics) ; Thermal management of electronic devices and systems ; Composite material ; Nanotechnology ; Mechanical engineering ; Engineering ; Meteorology ; Physics Thermal properties of materials ; Graphene research and applications ; Thermal Radiation and Cooling Technologies
Authors: Akshat Chaudhari | Janghoon Ock | Amir Barati Farimani
Source: ChemRxiv (preprint, 2025-05-02)
Field: Materials Science — Materials Chemistry

Modular Large Language Model Agents for Multi-Task Computational Materials Science The integration of large language models (LLMs) with domain-specific computational tools offers a promising pathway to streamline and enhance materials science workflows. This paper presents MatSciAgent, a multi-agent framework capable of performing key tasks such as materials data retrieval, continuum simulation, crystal structure generation, and molecular dynamics simulation. At the core of the framework is the master agent, which interprets the user’s natural language query, identifies the task type, and delegates it to a corresponding task-specific agent equipped with appropriate computational tools. Leveraging databases such as the Materials Project and MatWeb, the framework retrieves and summarizes materials data with grounded, factual responses—addressing a key limitation of vanilla LLMs. In cases where the target material is not found in existing databases, a generative task-specific agent can propose plausible crystal structures. For simulation tasks, dedicated agents extract relevant parameters from the user query to conduct continuum simulations (e.g., Cellular Automata and Monte Carlo Annealing) and atomistic simulations (e.g., Molecular Dynamics) using both established software and custom code. The modular design of these agents and their associated tools enables seamless extensibility, allowing the framework to evolve as new capabilities are integrated. Modular design ; Task (project management) ; Computer science ; Programming language ; Artificial intelligence ; Natural language processing ; Systems engineering ; Engineering Machine Learning in Materials Science
Authors: Liang Zhang | Weihang Geng | Xinwen Ou | Shan He | Xiaohang He | Wenlang Li | Yuchen Jiao | Ziyi Cheng | Ziliang Jin | Jun Qian | Ben Zhong Tang
Source: Nature Communications (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Boosting the brightness of aggregation-caused quenching chromophore-based covalent organic frameworks via energy level matching strategy Most chromophores exhibit an aggregation-caused quenching (ACQ) effect, resulting in minimal or weak emission when incorporated into covalent organic frameworks (COFs). Consequently, the development of luminescent COFs, particularly those utilizing ACQ chromophores, presents both a compelling and formidable challenge. Herein, we report the construction of highly emissive ACQ chromophore-based COFs by an energy level matching strategy. This approach effectively modulates the electronic structures within the COFs, thereby suppressing intralayer charge transfer to ACQ chromophores, such as the imine linkage and dibenzo[g,p]chrysene unit, while simultaneously inhibiting interlayer charge transfer and non-radiative decay pathways. As a result, the undesired ACQ effect is mitigated, enabling the realization of highly emissive COFs. Notably, COF-BT-PhDBC demonstrates high brightness with a solid-state photoluminescence quantum yield of 14.7%, standing among the best ACQ chromophore-based COFs. Furthermore, COF-BT-PhDBC has been successfully utilized for in vivo three-photon fluorescence imaging of murine brain vasculature, achieving an impressive imaging depth of approximately 800 µm. Chromophore ; Brightness ; Photoluminescence ; Quenching (fluorescence) ; Quantum yield ; Covalent bond ; Luminescence ; Fluorescence Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: Hanchen Xu | Hongwei Shou | Ziwei Yan | Kefu Zhu | Chuanqiang Wu | Wei Jiang | Zhanfeng Liu | Shiqiang Wei | Jialin Shi | Hassan Akhtar | Changda Wang | Li Song
Source: Advanced Materials (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Halogen Ion‐Mediated Hydrothermal Synthesis of Diverse MXenes with Tailored Heterostructures Two-dimensional transition metal carbides and nitrides (MXenes) have attracted significant attention due to their exceptional physicochemical properties. Despite extensive studies, efficient methods for the production of MXenes with precise structural control still remain a challenge, thus hindering their potential in many specific applications. Herein, a halogen ion-mediated hydrothermal approach is proposed for the controllable preparation of diverse MXenes and their heterostructures with well-defined interfacial architectures, demonstrating its potential as a high-throughput synthesis strategy. As proof of concept, Mo<sub>2</sub>C can be synthesized on a gram scale by employing NH₄F in the hydrothermal etching process of Mo<sub>2</sub>Ga<sub>2</sub>C. Subsequently, this approach is applied to various MXenes, including Ti<sub>3</sub>C<sub>2</sub>, V<sub>2</sub>C, and Nb<sub>4</sub>C<sub>3</sub>. Moreover, NH<sub>4</sub>X (X = Cl, Br, I) etchants combined with small-molecule intercalants enabled the targeted synthesis of MXene-based heterostructures, such as Mo<sub>2</sub>CT<sub>x</sub>@MoS<sub>2</sub> featuring ≈15 nm amorphous MoS<sub>2</sub> surface layers. Notable, the Mo<sub>2</sub>CT<sub>x</sub>(Br) heterostructure exhibited outstanding electrochemical stability, delivering a capacity of 465.5 mAh g⁻¹ after 300 cycles at 1 A g⁻¹, and achieving high coulombic efficiency of 99.8% during lithium-ion battery cycling. This work establishes a versatile and scalable platform for the synthesis of MXene-based materials, thus paving the way for accelerating their potential in various fields. MXenes ; Materials science ; Heterojunction ; Nanotechnology ; Hydrothermal synthesis ; Amorphous solid ; Hydrothermal circulation ; Nitride ; Electrochemistry ; Chemical engineering ; Optoelectronics ; Electrode ; Layer (electronics) ; Chemistry ; Physical chemistry ; Crystallography MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Yonghao Bu | Ye Tao | Ruowen Wang | Jing Zhou | Jie Deng | Yixiao Zhen | Mengdie Shi | Wenji Jing | Rui Xin | Yujie Zhang | Junwei Huang | Tianxin Li | Wei Lü | Xiaoshuang Chen
Source: Nature Communications (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

High discrimination infrared polarimetry via intrinsic anisotropic photogating in black phosphorus meta-polarization detectors Filterless miniaturized polarization photodetectors are essential for next-generation ultracompact optical and optoelectronic systems. However, achieving high polarization discrimination and high sensitivity over a broad wavelength range based on a simple device structure remains difficult. Here, we address this challenge by leveraging a band modulation effect induced by intrinsic anisotropic photogating. We realize a meta-polarization-detector consisting of a black phosphorus (BP) flake partially supported by SiO<sub>2</sub> with trap states and partially supported by hexagonal boron nitride (hBN) with almost no trap states. The band distribution and hence the self-powered photocurrent are significantly influenced by light illumination in a polarization dependent manner. The polarization extinction ratio (PER) tending to ∞/-∞ is achieved over the near- to mid-infrared range with high specific detectivity. Further, our device achieves PER = ∞/-∞ under blackbody radiation. The specific polarization angle detectivity reaches 3 × 10<sup>7 </sup>cm Hz<sup>0.5</sup> W<sup>-1</sup> degree<sup>-1</sup>. Despite utilizing the photogating effect, the photoresponse times are of the order of microseconds. Our work provides a promising approach for high performance ultracompact polarization photodetectors. Polarization (electrochemistry) ; Photodetector ; Photocurrent ; Polarimetry ; Anisotropy ; Black-body radiation ; Wavelength ; Infrared 2D Materials and Applications ; Advanced Fiber Laser Technologies ; Photorefractive and Nonlinear Optics
Authors: Minh–Hao Van | Prateek Verma | Chen Zhao | Xintao Wu
Source: ArXiv.org (preprint, 2025-06-25)
Field: Materials Science — Materials Chemistry

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are typically narrow in scope and require task-specific engineering, FMs offer cross-domain generalization and exhibit emergent capabilities. Their versatility is especially well-suited to materials science, where research challenges span diverse data types and scales. This survey provides a comprehensive overview of foundation models, agentic systems, datasets, and computational tools supporting this growing field. We introduce a task-driven taxonomy encompassing six broad application areas: data extraction, interpretation and Q\&amp;A; atomistic simulation; property prediction; materials structure, design and discovery; process planning, discovery, and optimization; and multiscale modeling. We discuss recent advances in both unimodal and multimodal FMs, as well as emerging large language model (LLM) agents. Furthermore, we review standardized datasets, open-source tools, and autonomous experimental platforms that collectively fuel the development and integration of FMs into research workflows. We assess the early successes of foundation models and identify persistent limitations, including challenges in generalizability, interpretability, data imbalance, safety concerns, and limited multimodal fusion. Finally, we articulate future research directions centered on scalable pretraining, continual learning, data governance, and trustworthiness. Transformative learning ; Scope (computer science) ; Foundation (evidence) ; Process (computing) ; Bridging (networking) ; Data-driven Machine Learning in Materials Science
Authors: Etinosa Osaro | Nell Karpinski | Sonia Alornyo | Joshua O. Ighalo
Source: Materials Today Chemistry (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence in materials science and chemistry: Past, present and future trajectories Leverage (statistics) ; Heuristic ; Big data ; Artificial life ; Empirical research ; Scientific discovery Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Ziqi Wang | Haiming Huang | Hancheng Zhao | Changwen Xu | Shang Zhu | Jan Janßen | Venkatasubramanian Viswanathan
Source: ArXiv.org (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation Materials discovery relies on high-throughput, high-fidelity simulation techniques such as Density Functional Theory (DFT), which require years of training, extensive parameter fine-tuning and systematic error handling. To address these challenges, we introduce the DFT-based Research Engine for Agentic Materials Screening (DREAMS), a hierarchical, multi-agent framework for DFT simulation that combines a central Large Language Model (LLM) planner agent with domain-specific LLM agents for atomistic structure generation, systematic DFT convergence testing, High-Performance Computing (HPC) scheduling, and error handling. In addition, a shared canvas helps the LLM agents to structure their discussions, preserve context and prevent hallucination. We validate DREAMS capabilities on the Sol27LC lattice-constant benchmark, achieving average errors below 1\% compared to the results of human DFT experts. Furthermore, we apply DREAMS to the long-standing CO/Pt(111) adsorption puzzle, demonstrating its long-term and complex problem-solving capabilities. The framework again reproduces expert-level literature adsorption-energy differences. Finally, DREAMS is employed to quantify functional-driven uncertainties with Bayesian ensemble sampling, confirming the Face Centered Cubic (FCC)-site preference at the Generalized Gradient Approximation (GGA) DFT level. In conclusion, DREAMS approaches L3-level automation - autonomous exploration of a defined design space - and significantly reduces the reliance on human expertise and intervention, offering a scalable path toward democratized, high-throughput, high-fidelity computational materials discovery. Scalability ; Context (archaeology) ; Density functional theory ; Convergence (economics) ; Automation ; Face (sociological concept) ; Bayesian probability Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides