jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Liang | Tzu‐Hao Chiu | Samia Kahlal | Jian‐Hong Liao | C. W. Liu | Jean‐Yves Saillard
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Hydride-containing Ag- and Au-rich 8-electron superatomic icosahedral cores: a DFT investigation Following several reports on ligand-protected atom-precise nanoclusters which encapsulate hydrides as interstitial dopants within their icosahedral core, the stability, structure and bonding of MH<sub><i>x</i></sub>@Ag<sub>12</sub> and MH<sub><i>x</i></sub>@Au<sub>12</sub> (M = Mo-Ag; W-Au) 8-electron cores is investigated through DFT calculations. The encapsulation of up to <i>x</i> = 3 hydrides appears to be possible but at the cost of substantial structural distortions. In most of the computed models, the hydrides are found nearly free to move inside their icosahedral cages. Systems with one (<i>nido</i>-type) or two (<i>arachno</i>-type) missing vertices on the icosahedron are also predicted to be viable. In general, the MH<sub><i>x</i></sub>@Au<sub>12</sub> species appear to be of lower stability than their MH<sub><i>x</i></sub>@Ag<sub>12</sub> homologs. We believe that this the work will provide some new directions for the synthesis of hydride-encapsulating superatoms. Icosahedral symmetry ; Hydride ; Electron ; Materials science ; Crystallography ; Chemical physics ; Chemistry ; Physics ; Nuclear physics ; Metallurgy ; Metal Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Chemical Physics Studies
Authors: Kristian Lytje | Jan Skov Pedersen
Source: Journal of Applied Crystallography (article, 2025-07-12)
Field: Materials Science — Biomaterials

Small-angle X-ray scattering profile calculation for high-resolution models of biomacromolecules We introduce a new approach to calculating small-angle X-ray scattering (SAXS) profiles from high-resolution atomic structures, which is realized in the open-source software <i>AUSAXS</i>. We employ an efficient implementation of the Debye equation, incorporating both accurate excluded volume models and a novel hydration shell model based on explicit dummy atoms. Two new excluded volume models are presented: (i) a simple, heterogeneous equivalent atom model, and (ii) a grid-based model. The two approaches reduce the risk of overfitting by either eliminating fitting parameters or introducing a safer volume scaling method. These models are compared with the traditional Gaussian sphere method, which is widely used in existing software. The comparisons reveal significant shortcomings in previously accepted methods, suggesting they may be more prone to overfitting than previously thought. This underscores the importance of a well tested and openly accessible baseline implementation like <i>AUSAXS</i>. <i>AUSAXS</i> is freely available at https://github.com/AUSAXS/AUSAXS. Small-angle X-ray scattering ; Scattering ; Resolution (logic) ; High resolution ; X-ray ; Optics ; Small-angle scattering ; Materials science ; Physics ; Computational physics ; Geology ; Computer science ; Remote sensing Nanoparticle-Based Drug Delivery ; Enzyme Structure and Function
Authors: Peng Cheng | Stéphanie Lau‐Truong | Sarra Gam‐Derouich | Roméo Bonnet | Alexandre Chevillot‐Biraud | Macilia Braïk | Abdelaziz Mezeghrane | Abderrahmane Belkhir | Shang Li | Manon Dewynter | Renaud Bachelot | Anne‐Laure Baudrion | Nordin Félidj
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Aluminum surface lattice resonances for enhanced near-infrared performance in asymmetric environments Aluminum (Al) is a cost-effective alternative to noble metals for plasmonics, particularly in the ultraviolet (UV) and visible regions. However, in the near-infrared (NIR) region, its performance is hindered by interband transitions (IBTs) at around 825 nm, leading to increased optical losses and broad resonances. Surface lattice resonances (SLRs) offer a promising solution by enhancing the plasmonic quality factor (<i>Q</i>-factor) through coherent coupling of localized surface plasmon (LSP) modes with Rayleigh anomalies. Although high-<i>Q</i> SLRs have been demonstrated in homogeneous environments, achieving similar enhancements in asymmetric media such as air remains a challenge. This study presents a novel approach for improving the <i>Q</i> factor of aluminum in air by utilizing SLRs in aluminum nanoparticle (NP) arrays fabricated <i>via</i> electron beam lithography (EBL) on a high refractive index indium tin oxide (ITO) substrate. The ITO substrate enhances long-range coupling between NPs, reinforcing coherent interactions. Using absorption micro-spectrometry and finite-difference time-domain (FDTD) simulations, we demonstrate <i>Q</i> factors reaching 110 in air, significantly exceeding typical values in IR in an asymmetrical surrounding medium. Our results establish aluminum as a viable low-cost material for high-performance plasmonic applications in sensing, telecommunications, and optoelectronics. Infrared ; Lattice (music) ; Materials science ; Aluminium ; Chemical physics ; Surface (topology) ; Nanotechnology ; Optoelectronics ; Composite material ; Optics ; Chemistry ; Physics ; Acoustics ; Geometry Optical Coatings and Gratings ; Photonic and Optical Devices ; Semiconductor Lasers and Optical Devices
Authors: Elías Portolés | Marta Perego | Pavel A. Volkov | Mathilde Toschini | Yana Kemna | Alexandra Mestre-Torá | Giulia Zheng | Artem O. Denisov | Folkert K. de Vries | Peter Rickhaus | Takashi Taniguchi | Kenji Watanabe | J. H. Pixley | Thomas Ihn | K. Ensslin
Source: Nature Communications (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Quasiparticle and superfluid dynamics in Magic-Angle Graphene Magic-Angle Twisted Bilayer Graphene (MATBG) shows a wide range of correlated phases which are electrostatically tunable. Despite a growing knowledge of the material, there is yet no consensus on the microscopic mechanisms driving its superconducting phase. A major obstacle to progress in this direction is that key thermodynamic properties, such as specific heat, electron-phonon coupling and superfluid stiffness, are challenging to measure due to the 2D nature of the material and its relatively low energy scales. Here, we use a gate-defined, radio frequency-biased, Josephson junction to probe the electronic dynamics of MATBG. We demonstrate evidence for two processes determining the low-frequency dynamics across the phase diagram: thermalization of electronic quasiparticles through phonon scattering and inductive response of the superconducting condensate. A phenomenological approach allows us to relate the experimentally observed dynamics to several thermodynamic properties of MATBG, including electron-phonon coupling and superfluid stiffness. Our findings support anisotropic or nodal superconductivity in MATBG and demonstrate a broadly applicable method for studying properties of 2D materials with out-of-equilibrium nanodevice dynamics. Quasiparticle ; Superfluidity ; Graphene ; Physics ; Condensed matter physics ; Dynamics (music) ; MAGIC (telescope) ; Superconductivity ; Quantum mechanics Graphene research and applications ; Mechanical and Optical Resonators ; Quantum Electrodynamics and Casimir Effect
Authors: Kimihiko Nakajima | Masami Ouchi | Yuichi Harikane | Eros Vanzella | Yoshiaki Ono | Yuki Isobe | Moka Nishigaki | Takuji Tsujimoto | Fumitaka Nakamura | Yi Xu | Hiroya Umeda | Yechi Zhang
Source: ArXiv.org (preprint, 2025-06-13)
Field: Chemistry — Spectroscopy

An Ultra-Faint, Chemically Primitive Galaxy Forming in the Reionization Era The formation of the first stars and galaxies marked the onset of chemical enrichment, yet direct observations of such primordial systems remain elusive. Here we present James Webb Space Telescope spectroscopic observations of LAP1-B, an ultra-faint galaxy at redshift z_{spec}=6.625 +/-0.001, corresponding to a cosmic age of 800 million years after the Big Bang, strongly magnified by gravitational lensing. LAP1-B exhibits a gas-phase oxygen abundance of (4.2 +/- 1.8) x 10^{-3} times the solar value, making it the most chemically primitive star-forming galaxy discovered to date. The galaxy displays an exceptionally hard ionizing radiation field, which is inconsistent with chemically enriched stellar populations or accreting black holes but matches theoretical predictions for an exceptionally metal-deficient stellar population. It also shows an elevated carbon-to-oxygen abundance ratio for its metallicity in the interstellar medium, consistent with nucleosynthetic yields from a stellar population formed in the absence of initial metals. The lack of detectable stellar continuum constrains the stellar mass to &lt;3,300 Msun, while the dynamical mass, derived from emission-line kinematics, exceeds the combined stellar and gas mass and indicates a dominant dark matter halo. Our findings establish LAP1-B as a "fossil in the making", a direct high-redshift progenitor of the ancient ultra-faint dwarf galaxies observed in the local Universe, offering a rare window into the earliest stages of galaxy formation. Galaxy ; Reionization ; Population ; Stellar population ; Metallicity ; Galaxy formation and evolution ; Stellar mass ; Stars ; COSMIC cancer database Molecular Spectroscopy and Structure ; Geophysics and Sensor Technology ; Methane Hydrates and Related Phenomena
Authors: Malte Pietsch | Konrad Henkel | René Körbitz | Ronny Hüttner | Kai Uhlig | Sascha Bruk | Matthieu Fischer | Jochen Hampe | Andreas Richter | Franz Brinkmann
Source: Scientific Reports (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

A deflectable video camera system for laparoscopic surgery based on shape memory alloy actuators Laparoscopic instruments are usually not deflectable and therefore have significant usability limitations in minimally invasive surgery. Flexible-tip devices have been introduced, but they are still controlled by manual Bowden cables or conventional motors and are heavy and expensive. An interesting alternative are shape memory alloy (SMA) actuators, because they are lightweight, cost-efficient and offer the potential for a universal actuator platform for laparoscopic and other endoscopic instruments. This paper presents a laparoscopic video camera with electronic deflection control via thumb joystick. Its actuator unit consists of four SMA wires, whose special tensioning mechanism ensures both optimal preload and length compensation. The camera is tiltable in two degrees of freedom, enabled by a laser-cut Nitinol joint. Based on three assembled units and an automated measurement system, the functionality of the camera system was characterised. Deflection angles of more than 178° per degree of freedom were achieved. At operating frequencies of up to 0.5 Hz, the units showed suitable deflection behaviour. Each remained operational for more than 1200 actuation cycles or 4.4 h of continuous operation. Furthermore, the influence of continuous temperature stimulation on characteristic device parameters was investigated. This work contributes to demonstrating the feasibility of SMA actuators for the electrical control of flexible-tip laparoscopic instruments. Temperature control in particular is a challenge for future work. Actuator ; Deflection (physics) ; Joystick ; Shape-memory alloy ; SMA* ; Computer science ; Usability ; Laparoscopic surgery ; DC motor ; Mechanical engineering ; Simulation ; Artificial intelligence ; Engineering ; Surgery ; Electrical engineering ; Physics ; Optics ; Medicine ; Laparoscopy Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Arthur Grimaud | Maša Babović | Frederik Haugaard Holck | Ole N. Jensen | Veit Schwämmle
Source: Molecular & Cellular Proteomics (article, 2025-02-13)
Field: Chemistry — Spectroscopy

How to Deal With Internal Fragment Ions? Tandem mass spectrometry of peptides and proteins generates 3mass spectra of their gas-phase fragmentation product ions, including N-terminal, C-terminal, and internal fragment ions. While N- and C-terminal ions are routinely assigned and identified using computational methods, internal fragment ions are often difficult to annotate correctly. They become particularly relevant for long peptides and full proteoforms where the peptide backbone is more likely to be fragmented multiple times. Internal fragment ions potentially offer tremendous information regarding amino acid sequences and positions of post-translational modifications of peptides and intact proteins. However, their practical application is challenged by the vast number of theoretical internal fragments that exist for long amino acid sequences, leading to a high risk of false-positive annotations. We analyze the mass spectral contributions of internal fragment ions in spectra from middle-down and top-down experiments and introduce a novel graph-based annotation approach designed to manage the complexity of internal fragments. Our graph-based representation allows us to compare multiple candidate proteoforms in a single graph, and to assess different candidate annotations in a fragment ion spectrum. We demonstrate cases from middle-down and top-down data where internal ions enhance amino acid sequence coverage of polypeptides and proteins and accurate localization of post-translational modifications. We conclude that our graph-based method provides a general approach to process complex tandem mass spectra, enhance annotation of internal fragment ions, and improve proteoform sequencing and characterization by mass spectrometry. Fragment (logic) ; Chemistry ; Computational biology ; Computer science ; Biology ; Algorithm Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Nuclear Physics and Applications
Authors: Malik Ahmed Mubeen | Fuyi Chen
Source: Journal of Applied Physics (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Deep reinforcement learning unveils ternary nanocluster configurations: A case study on Ag6Pd5Cu4 The identification of global minimum configurations in ternary nanoclusters faces challenges due to the inherent complexity of multimetallic potential energy surfaces. Traditional methods, such as genetic algorithms, suffer from computational inefficiency and premature convergence. In this study, we utilized a deep reinforcement learning framework integrating trust region policy optimization to balance exploration and exploitation in potential energy surface navigation. Applied to Ag6Pd5Cu4, the deep reinforcement learning framework predicted a global minimum configuration with the energy of 0.5324 eV/atom, validated by density functional theory. Effective medium theory potentials reduced computational costs as compared to density functional theory, enabling the rapid discovery of 15 distinct low-energy configurations. Ab initio molecular dynamics simulations confirmed thermal stability at 300 K. This work demonstrates the capability of deep reinforcement learning to autonomously resolve multimetallic complexity, offering a scalable pathway for accelerating nanocluster design in materials science. Ternary operation ; Reinforcement ; Materials science ; Reinforcement learning ; Computer science ; Nanotechnology ; Artificial intelligence ; Composite material Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Muhammad Usman | Fuyi Chen
Source: ACS Applied Nano Materials (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Deep Reinforcement Learning for Structural Optimization and Potential Energy Landscape of 13-Atom Gold Nanoclusters for Application in 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 (Au13). 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 Au13 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 Au13 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 ; Nanomaterials ; Atom (system on chip) ; Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Lauren Takahashi | Micke Kuwahara | Keisuke Takahashi
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics The integration of artificial intelligence (AI) and robotics is fundamentally transforming the landscape of chemical and materials research, enabling the rise of autonomous laboratories capable of conducting high-throughput, data-driven experimentation with minimal human intervention. The current state, challenges, and future directions of AI-driven automation in the laboratory are explored, emphasizing the technical, ethical, and cultural shifts required to support this paradigm. Efforts to democratize access to laboratory automation <i>via</i> open-source hardware, modular systems, and digitial fabrication are highlighted, showcasing methods of innovation for smaller research groups that conduct research alongside well-funded institutions. Concurrently, the need for robust safety systems, validation protocols, and interdisciplinary collaboration are stressed to ensure reliability, transparency, and inclusivity. Beyond technical capability, the emergence of AI systems capable of hypothesis generation and scientific reasoning raises critical questions about the evolving nature of creativity, intuition, and authorship in science. Rather than replacing human researchers, an argument is made for a model of collaborative intelligence- where humans and machines co-create knowledge, each contributing distinct strengths. This perspective proposes that the future of research lies not only in smarter tools but in the development of smarter, ethically grounded systems that amplify human insight and redefine the very practice of science in the 21st century. Robotics ; Automation ; Artificial intelligence ; Robot ; Computer science ; Engineering ; Systems engineering ; Mechanical engineering Machine Learning in Materials Science
Authors: Yuhang Wang | Xiaotong Liu | Haotian Chen | Tao Yang | Xingchen Liu | Xiaodong Wen
Source: Cell Reports Physical Science (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Exploring lightweight language models for materials informatics with AlchemBERT Wang et al. demonstrate that language models effectively represent and predict material structures, outperforming many traditional approaches, with the BERT-base model as a baseline. Using natural language descriptions for material properties yields a 40.3% accuracy improvement over CIFs and chemical formulas. Computer science ; Informatics ; Data science ; Natural language processing ; Engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Tongao Yao | Yang Yang | Yujie Yan | Xinyi Ou | Mingyang Li | Chenxi Wang | Wuzhe Li | Chenghao Du | Xuqiang Shao | Zhijun Gao | Weijie Yang
Source: AI Agent (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Knowledge-extractor: a self-evolving scientific framework for hydrogen energy research driven by AI agents The rapid evolution of Artificial intelligence (AI) from passive “knowledge co-pilots” to autonomous “research partners” is initiating a paradigm shift in scientific discovery, a frontier now termed Agentic Science. However, applying general-purpose AI systems to dynamic, vertically integrated domains such as hydrogen energy reveals critical limitations, including a lack of deep domain knowledge, an inability to process real-time information, and insufficient autonomous planning capabilities. To address these challenges, we introduce Knowledge-Extractor, a self-evolving scientific framework for building domain-expert AI agents, which we implement and evaluate in the hydrogen energy domain via an agent named Hydrogen-Agent. The core of our framework is a Hybrid Knowledge Integration strategy, which synergistically combines a domain-fine-tuned large language model (LLM) as its "cognitive core" with a continuously updated, non-parametric knowledge base.This architecture is augmented by an autonomous toolset comprising a PolicyRetriever (for extracting information from policy documents), a WebBrowser (for retrieving online sources), and an ArxivAnalyzer (for analyzing scientific papers from arXiv). We demonstrate that through an autonomous knowledge loop, Hydrogen-Agent overcomes the static knowledge limitations of traditional models. Our experiments validate a “specialization effect” where domain-specific fine-tuning enhances factual accuracy on our HydroBench benchmark, outperforming its base model and powerful generalist LLMs. Furthermore, three case studies illustrates the ability of the agent to autonomously conduct complex, end-to-end research tasks, from multi-source data gathering to the generation of a strategic analysis report. Hydrogen-Agent serves as a robust prototype for future scientific agents, showcasing a viable path toward creating domain-expert AI that can accelerate discovery in critical scientific fields. Domain (mathematical analysis) ; Process (computing) ; Knowledge base ; Autonomous system (mathematics) ; Architecture ; Domain knowledge ; Autonomous agent ; Multi-agent system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Multi-Agent Systems and Negotiation
Authors: Alon Grinberg Dana
Source: Chemical Science (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

The agentic age of predictive chemical kinetics Predictive chemical kinetic modeling is foundational to areas ranging from energy and environmental science to pharmaceuticals and advanced materials. While significant progress has been made in automating individual steps, the development of a complete predictive model remains a human-intensive effort to orchestrate existing software tools and revise models. This perspective outlines a practical path to improved chemical kinetic model development using agentic AI. A dual-lane architecture is introduced: a fast execution lane handles mechanism generation and parameter refinement, while a deliberative agentic lane plans, refines, and revises while executing experiments and computations. The proposed outcome is a robust pathway toward decision-grade models. Humans remain central: researchers set objectives and priors, approve high-impact actions, and adjudicate new chemical insights. Creativity, complex judgment, and strategic thinking remain in the human domain. Ultimately, this approach aims to accelerate trustworthy, transparent, decision-grade model development. Outcome (game theory) ; Perspective (graphical) ; Set (abstract data type) ; Adjudication ; Chemical process ; Mechanism (biology) ; Term (time) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods