jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yasemin Demirhan
Source: Optical and Quantum Electronics (article, 2026-01-12)
Field: Materials Science — Surfaces, Coatings and Films

Anti-reflective and optical transparent coatings fabricated by a computer-controlled system Abstract In this study, an antireflection (AR) coating based on SiO 2 was controllably deposited using a computer-programmed CNC system and evaluated for its performance. A unique, automated high-precision technique was employed for AR layer deposition. Transmission and reflectance measurements were conducted using a UV–Vis spectrophotometer, while the photocatalytic performance of the AR layers was assessed using a methylene blue dye solution under UV illumination. The AR-coated glass was mounted on a monocrystalline silicon solar cell, and the I-V and P-V characteristic curves were obtained. The best-performing AR-coated solar cell exhibited an open-circuit voltage (V oc ) of 0.61 V and a short-circuit current density (I sc ) of 41.6 mA/cm 2 , resulting in a 2% increase in efficiency compared to the uncoated device. The CNC-controlled deposition process enabled precise tuning of parameters such as immersion speed, withdrawal speed, dwell time, and alignment, ensuring uniform coatings and enhanced optical performance. Field tests confirmed the low reflectance and improved efficiency of AR-coated solar panels. These findings highlight the potential of SiO 2 -based AR coatings for enhancing solar cell performance while maintaining mechanical stability. Anti-reflective coating ; Monocrystalline silicon ; Coating ; Solar cell ; Optical coating ; Dwell time ; Layer (electronics) ; Silicon Optical Coatings and Gratings ; Silicon and Solar Cell Technologies ; Thin-Film Transistor Technologies
Authors: Yunpeng Tao | Quanlong Gao | Xiaowen Fan | Honghong Wu | Sergey Shabala
Source: Plant and Soil (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

From uptake to resilience: How metal-based nanoparticles can enhance plant drought tolerance Abstract Background Drought stress (DS) is a major abiotic constraint threatening agricultural productivity and global food security. Traditional agronomic approaches have achieved limited success in sustainably enhancing plant drought tolerance. Nano-enabled agriculture, particularly using metal-based nanoparticles (MNPs), has emerged as a promising strategy to improve plant resilience under stress. In addition to serving as micronutrient supplements, MNPs interact with plant signalling systems to regulate physiological, biochemical, and molecular responses to DS. Scope This review synthesises current knowledge on uptake, translocation, accumulation, and physiological interactions of MNPs in plants, focusing on their drought-alleviating potential. We analyse how MNPs modulate water retention, activate antioxidant defence systems, enhance photosynthetic efficiency, and improve root nutrient acquisition under DS. A special emphasis is placed on the role of MNPs in regulating drought-responsive gene expression and stress-signalling pathways. We identify major research gaps requiring future attention, including: (1) optimising nanoparticle formulations for targeted delivery; (2) elucidating interactions between MNPs and soil microbial communities; (3) understanding long-term ecological effects and fate of MNPs in soil–plant systems; and (4) developing reliable field application strategies maximising efficacy while ensuring environmental safety. Conclusion Nano-enabled agriculture using MNPs offers a promising route to enhance plant drought resilience by improving water relations, redox balance, photosynthesis and nutrient uptake. To move from concept to practice, future work must link the mechanistic understanding of above processes with long-term fate assessments and socio-economic analyses so that nano-inputs are safe-by-design, well-regulated and genuinely affordable for farmers. Sustainability ; Drought tolerance ; Photosynthesis ; Agriculture ; Abiotic stress ; Abiotic component ; Nutrient ; Plant physiology ; Biomass (ecology) Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance ; Plant-Microbe Interactions and Immunity
Authors: Felipe M. Yamamoto | Maria Tereza Fabbro | Braulio H. K. Lopes | Giovanna S. Cembranelli | Mauricio R. Baldan | Sérgio Luiz Mineiro | Luís P. S. Santos
Source: Journal of Materials Science Materials in Electronics (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of nanoparticles of Fe, Co, and Ni oxides derived of metal–organic framework (MOF), and their combination with carbonyl iron for application in the K-band of frequencies as a microwave-absorbing material Abstract This work introduces an innovative synthesis of α-Fe 2 O 3 (Fe), Co 3 O 4 (Co), and NiO (Ni) nanoparticles based in the following metal–organic frameworks (MOFs): Fe-MOF, Co-MOF, and Ni-MOF, respectively. These nanoparticles were combined with carbonyl iron (CI) via solid-state synthesis for use as microwave-absorbing materials (MAMs) in the K-band particularly within the 24–26 GHz frequency range. The synthesized materials were characterized structurally by X-ray diffraction (XRD), morphologically by scan electronic microscopy (SEM), and elemental qualitatively the composition by electron dispersive spectroscopy (EDS). The electromagnetic (EM) characterization was performed using a vector network analyzer (VNA) with Keysight ® software to extract the complex relative electric permittivity, and complex magnetic permeability via the Nicholson Ross Weir (NRW) method based on measured S-parameters. Additionally, reflectivity-based EM simulations were conducted to assess impedance matching and determine optimal absorb thicknesses. The results indicate that each synthetized material exhibits distinct electromagnetic behavior, and the proper combination of composition and thickness can yield promising MAM composite. Notably, the combination between nano Fe/ CI achieved a reflection loss lower than −15 dB at a thickness of 1.20 mm, while Co/CI and Ni/CI exhibited superior performance, with minimum reflection loss of −23,45 dB and −26,21 dB respectively. This work provides a novel approach to developing advanced composites for K-band microwave absorption, addressing the growing demand for effective EM pollution. Reflection loss ; Nanoparticle ; Carbonyl iron ; Microwave ; Elemental analysis ; Non-blocking I/O ; Diffraction ; Reflection (computer programming) ; Scanning electron microscope Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Microwave and Dielectric Measurement Techniques
Authors: T L Chen | Alireza Ghasemi | Junyi Zhang | Liyu Shi | Zhenisbek Tagay | Youzhe Chen | Lei Chen | E. S. Choi | M. Jaime | Minseong Lee | Yiqing Hao | Huibo Cao | Barry L. Winn | Andrey A. Podlesnyak | Daniel M. Pajerowski | Ruidan Zhong | Xianghan Xu | N. P. Armitage | Robert Cava | C. Broholm
Source: Nature Communications (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase diagram and spectroscopic signatures of a supersolid in the quantum ising magnet K2Co(SeO3)2 Supersolid phases are quantum-entangled states of matter exhibiting the dual characteristics of superfluidity and solidity. Theory predicts that hard-core bosons on a triangular lattice can form such phases at half filling and near complete filling. Leveraging an exact mapping between bosons and spin- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:mfrac> </mml:math> degrees of freedom, here we show that these phases are realized in the triangular-lattice antiferromagnet K<sub>2</sub>Co(SeO<sub>3</sub>)<sub>2</sub>. At zero field, neutron diffraction reveals the development of quasi-two-dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt><mml:mrow><mml:mn>3</mml:mn></mml:mrow> </mml:msqrt> <mml:mo>×</mml:mo> <mml:msqrt><mml:mrow><mml:mn>3</mml:mn></mml:mrow> </mml:msqrt> </mml:math> magnetic order with Z<sub>3</sub> translational symmetry breaking (solidity), though with reduced amplitude indicating strong quantum fluctuations. These fluctuations manifest as equidistant bands of continuum neutron scattering, where the lowest-energy mode is gapless at K <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow> <mml:mrow><mml:mn>3</mml:mn></mml:mrow> </mml:mfrac> <mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow> <mml:mrow><mml:mn>3</mml:mn></mml:mrow> </mml:mfrac> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:math> , consistent with broken U(1) spin rotational symmetry (superfluidity). For c-axis-oriented magnetic fields near saturation, we find a second phase consistent with a high-field supersolid. These two supersolids are separated by a pronounced 1/3 magnetization plateau phase that supports coherent spin waves, from which we determine the underlying spin Hamiltonian. Supersolid ; Translational symmetry ; Boson ; Superfluidity ; Phase diagram ; Symmetry breaking ; Hexagonal lattice ; Ising model ; Spin (aerodynamics) Iron-based superconductors research ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Ujjal Roy | Sourav Manna | Souvik Chakraborty | Kenji Watanabe | Takashi Taniguchi | Ankur Das | Moshe Goldstein | Yuval Gefen | Anindya Das
Source: Nature Communications (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

Half-integer thermal conductance in integer quantum Hall states Considering a range of candidate quantum phases of matter, half-integer thermal conductance is believed to be an unambiguous evidence of non-Abelian states. It has been long known that such half-integer values arise due to the presence of Majorana edge modes, representing a significant step towards topological quantum computing. Here, we challenge this prevailing notion by presenting a comprehensive theoretical and experimental study where half-integer two-terminal thermal conductance plateau is realized employing integer quantum Hall states. Our proposed setup features a confined geometry of bilayer graphene, interfacing distinct particle and hole-like integer quantum Hall edges. Each segment of the device exhibits full charge and thermal equilibration. Our approach is amenable to generalization to other quantum Hall platforms, and may give rise to other values of fractional quantized transport. Our study demonstrates that the observation of robust non-integer values of thermal conductance can arise as a manifestation of mundane equilibration dynamics as opposed to underlying non-trivial topology. MAJORANA ; Quantum Hall effect ; Quantum ; Thermal conductivity ; Conductance ; Thermal ; Conductance quantum ; Charge (physics) Phase-change materials and chalcogenides ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Sebastian Miles | Francesco Zatelli | A. Mert Bozkurt | Michael Wimmer | Chun-Xiao Liu
Source: Physical review. B./Physical review. B (preprint, 2026-01-21)
Field: Materials Science — Materials Chemistry

Braiding Majoranas in a linear quantum dot–superconductor array: Mitigating the errors from Coulomb repulsion and residual tunneling Exchanging the positions of two non-Abelian anyons transforms between many-body wave functions within a degenerate ground-state manifold. This behavior is fundamentally distinct from fermions, bosons and Abelian anyons. Recently, quantum dot-superconductor arrays have emerged as a promising platform for creating topological Kitaev chains that can host non-Abelian Majorana zero modes. In this work, we propose a minimal braiding setup in a linear array of quantum dots consisting of two minimal Kitaev chains coupled through an ancillary, normal quantum dot. We focus on the physical effects that are peculiar to quantum dot devices, such as interdot Coulomb repulsion and residual single electron tunneling. We find that the errors caused by either of these effects can be efficiently mitigated by optimal control of the ancillary quantum dot that mediates the exchange of the non-Abelian anyons. Moreover, we propose experimentally accessible methods to find this optimal operating regime and predict signatures of a successful Majorana braiding experiment. Quantum tunnelling ; Superconductivity ; Quantum dot ; Condensed matter physics ; Residual ; Coulomb ; Coulomb blockade ; Physics ; Quantum ; Quantum mechanics ; Voltage ; Electron ; Computer science ; Algorithm Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Xiaoyu Wu | Rui Zheng | Quan Liu | Jianwen Jiang
Source: Advanced Functional Materials (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning (Adv. Funct. Mater. 14/2026) Synthesizable Metal–Organic Frameworks In a futuristic MOF factory, an AI scientist inspects frameworks flowing along a production line. Floating blueprints and free-energy maps represent a molecular-dynamics-informed TabPFN model, which screens vast hypothetical MOF libraries and highlights synthesizable, experimentally actionable candidates. More information can be found in the Research Article by Jianwen Jiang and co-workers (10.1002/adfm.202519565). Deep learning ; Blueprint ; Drug discovery ; Knowledge extraction Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Akshat Chaudhari | Janghoon Ock | Amir Barati Farimani
Source: Communications Materials (article, 2026-03-26)
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 provides a pathway to streamline and enhance materials science workflows. This paper introduces MatSciAgent, a multi-agent framework supporting tasks such as materials data retrieval, continuum simulation, crystal structure generation, and molecular dynamics simulation. At its core is a master agent that interprets user queries, identifies the task type, and delegates to task-specific agent(s) equipped with tools. Leveraging databases such as Materials Project and MatWeb, the framework retrieves and summarizes materials data with grounded, factual responses, addressing limitations of vanilla LLMs. When a target material is absent from databases, a generative agent can propose plausible crystal structures. For simulations, specialized agents extract parameters to perform continuum and molecular dynamics simulations using existing software or custom code. MatSciAgent demonstrates stability, with parameter extraction achieving 100% success across five runs and materials extraction consistent in 9 of 10 runs. Its modular design ensures seamless extensibility to evolve as new capabilities are integrated. Integrating large language models with domain-specific tools can streamline computational materials science workflows by enhancing efficiency and accuracy. Here, the authors present MatSciAgent, a multi-agent framework that automates tasks like data retrieval and simulations, demonstrating high stability and adaptability, thus promising significant advancements in materials research and development. Modular design ; Computational model ; Modeling language ; Field (mathematics) ; Key (lock) ; Abstraction Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nanoporous metals and alloys
Authors: Daniil A. Boiko | Mikhail V. Polynski
Source: Chemistry - A European Journal (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Pondering the Future of Chemical Research amid the Wider Adoption of Artificial Intelligence Technologies The rapid adoption of artificial intelligence (AI) and machine learning (ML) in chemistry coincides with increasing structural pressures on academic research, including funding constraints, talent competition, and changing attitudes toward scientific careers. In this Perspective, we argue that this combination of trends may reshape how and by whom chemical knowledge is produced, rather than simply increase research productivity. We discuss recent developments in the automation of experimentation and self-driving labs, ML-based modeling and digital twins, and the use of large language models for literature search, manuscript preparation, and review, and place them against the current financial and social pressures on universities. We outline these trends in the hope of softening the transition for the chemical research community and urging researchers, institutions, and funders to make their research ecosystems more resilient. Finally, we discuss possible shifts in the composition and structure of research groups and in the balance between universities, industry, and government laboratories, raising the central question: who will produce chemical knowledge in the research landscape changed by the wider adoption of AI technologies? Government (linguistics) ; Automation ; Social life ; Sociology of scientific knowledge ; Research program ; Balance (ability) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Chang Shu | Ledu Wang | Xiaoju Yang | Wenao Xie | Peixuan Xie | Xiao Wang | Xuan Yang | Jingyi Rao | Kewei Wang | Linjiang Chen | Bien Tan | Xiaoyan Wang
Source: Nature Communications (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models The photosynthetic production of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H<sub>2</sub>O<sub>2</sub> generation. However, most existing COF photocatalysts yield H<sub>2</sub>O<sub>2</sub> at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language model-driven design strategy for the targeted synthesis of high-performance COF photocatalysts. By analyzing a curated corpus of 355 peer-reviewed articles on COF-based photocatalysis with a language model-driven knowledge extraction pipeline, we extract and structure over 11,000 chemical relationships related to building block identity, linkage robustness, and H<sub>2</sub>O<sub>2</sub> yield. Guided by this artificial intelligence-derived knowledge base, we identify 4,4',4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b':5,6-b″]trithiophene-2,5,8-tricarbaldehyde as optimal building blocks and thiazole as the preferred linkage motif for constructing a robust, photocatalytic COF. The resulting Thz-COF achieve a high H<sub>2</sub>O<sub>2</sub> concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents), with a solar-to-chemical energy conversion efficiency of 1.39%. Photocatalysis ; Hydrogen peroxide ; Covalent bond ; Aqueous solution ; Thiazole ; Block (permutation group theory) ; Catalysis ; Covalent organic framework ; Green chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Advanced battery technologies research
Authors: Ye Yu | Zikai Xie | Man Luo | Adam Redfearn | Arianna Brandolese | Chenxi Sheng | Professor Andrew P Dove | Xiaolong Zhang | Linjiang Chen | Jun Jiang
Source: Journal of the American Chemical Society (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid–Base Catalysts for PET Glycolysis The depolymerization of polyethylene terephthalate (PET) through efficient chemical recycling remains a central challenge in plastic waste valorization, in part because the catalyst landscape is vast and sparsely explored. Here, we present an artificial intelligence (AI)-driven discovery framework that integrates Bayesian optimization (BO), large language models (LLMs), and high-throughput robotics to accelerate the search for Lewis acid-base catalysts for PET glycolysis. Starting from a literature-guided baseline, BO used LLM-derived semantic embeddings of chemical knowledge to navigate a high-dimensional space of 11,160 candidate pairs, identifying promising candidates beyond the initial state of the art. The LLM then analyzed the experimental results to generate interpretable, data-driven hypotheses that guided further experiments and enabled inductive, human-led extrapolation beyond the predefined search space. This workflow yielded a zinc pivalate/<i>N</i>,<i>N'</i>-diethylethylenediamine catalyst delivering 95% bis(2-hydroxyethyl) terephthalate (BHET) yield in 20 min, with robust performance upon scale-up and on postconsumer PET. Mechanistic analysis supports a synergistic dual-site activation mode and informs transferable design principles. All experiments were executed on a fully autonomous AI-Chemist platform with automated reaction setup and nuclear magnetic resonance (NMR) spectroscopic analysis. Together, these results show how automation-AI-human collaboration can progress from optimization to out-of-sample discovery in large, underexplored chemical spaces. Polyethylene terephthalate ; Workflow ; Catalysis ; Depolymerization ; Chemical space ; Bayesian optimization ; Yield (engineering) Machine Learning in Materials Science ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Andrew L Ferguson | Marisa LaFleur | Lars Ruthotto | Jesse Thaler | Yuan-Sen Ting | Pratyush Tiwary | Soledad Villar
Source: Machine Learning Science and Technology (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

The future of artificial intelligence and the mathematical and physical sciences (AI+MPS) Abstract This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS. Transformative learning ; Workforce ; Position paper ; Workforce development ; Snapshot (computer storage) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Genetics, Bioinformatics, and Biomedical Research