jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A.H. Mirza | Nawaf Alampara | Sreekanth Kunchapu | Martiño Ríos-García | Benedict Emoekabu | Aswanth Krishnan | Tanya Gupta | Mara Schilling-Wilhelmi | Macjonathan Okereke | Anagha Aneesh | Mehrdad Asgari | J. Eberhardt | Amir Mohammad Elahi | Hani M. Elbeheiry | M.V. Gil | Christina Glaubitz | Maximilian Greiner | Caroline T. Holick | Tim Hoffmann | Abdelrahman Ibrahim | Lea C. Klepsch | Yannik Köster | Fabian Alexander Kreth | Jakob Meyer | Santiago Miret | Jan Matthias Peschel | Michael Ringleb | Nicole C. Roesner | J. Schreiber | Ulrich S. Schubert | Leanne M. Stafast | A. D. Dinga Wonanke | Michael Pieler | Philippe Schwaller | Kevin Maik Jablonka
Source: Nature Chemistry (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capabilities of LLMs, which would be required to improve models and mitigate potential harm. Here we introduce ChemBench, an automated framework for evaluating the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of chemists. We curated more than 2,700 question-answer pairs, evaluated leading open- and closed-source LLMs and found that the best models, on average, outperformed the best human chemists in our study. However, the models struggle with some basic tasks and provide overconfident predictions. These findings reveal LLMs' impressive chemical capabilities while emphasizing the need for further research to improve their safety and usefulness. They also suggest adapting chemistry education and show the value of benchmarking frameworks for evaluating LLMs in specific domains. Benchmarking ; Harm ; Process (computing) ; Value (mathematics) ; Chemistry ; Management science ; Computer science ; Cognitive science ; Psychology ; Engineering ; Social psychology ; Machine learning ; Programming language ; Management Machine Learning in Materials Science ; Topic Modeling ; Artificial Intelligence in Healthcare and Education
Authors: Ze Xu | Xiaoming Shi | Yixuan Liu | Danyang Wang | Hao‐Cheng Thong | Yuqi Jiang | Zijie Sha | Zhao Li | Fang‐Zhou Yao | Xianxian Cai | Han Huang | Zhanpeng Xu | Xinyu Jin | Chen‐Bo‐Wen Li | Xin Zhang | Xiaowei Ren | Zhihao Dong | Chaofeng Wu | Peter Kabakov | Fangyuan Zhu | Feng Chen | Peng Tan | Hao Tian | Haozhi Sha | Rong Yu | Ben Xu | Wen Gong | Xiaohui Wang | Jing‐Feng Li | Stephen J. Skinner | Ming Li | Houbing Huang | Shujun Zhang | Ke Wang
Source: Nature Materials (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

High electrostrain in a lead-free piezoceramic from a chemopiezoelectric effect Lead (geology) ; Materials science ; Composite material ; Geology Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Dielectric materials and actuators
Authors: Zhicheng Cai | Hyojung Kim
Source: npj 2D Materials and Applications (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Recent advances in MXene gas sensors: synthesis, composites, and mechanisms Abstract MXenes, a class of two-dimensional (2D) transition metal carbides and nitrides, have emerged as promising materials for high-performance gas sensors due to their unique combination of high electrical conductivity, tunable surface chemistry, and large specific surface area. This review highlights recent advances in MXene-based gas sensing materials, covering their synthesis strategies, structure–property relationships, and applications in both pristine and composite forms. We discuss conventional hydrofluoric acid etching and recent fluorine-free methods for MXene synthesis, and examine how these approaches affect surface terminations, interlayer spacing, and defect profiles relevant to gas sensing. The gas-sensing performance of pristine MXenes and their composites with metal oxides, transition metal dichalcogenides (TMDs), rGO, and conductive polymers is systematically analyzed, along with the underlying sensing mechanisms. Finally, current challenges and future perspectives for MXene-based gas sensors are addressed, with emphasis on enhancing sensitivity, selectivity, and ambient stability for next-generation sensing platforms. Composite material ; Materials science MXene and MAX Phase Materials ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Anil Kumar Meher | Akli Zarouri
Source: Molecules (review, 2025-01-17)
Field: Chemistry — Spectroscopy

Environmental Applications of Mass Spectrometry for Emerging Contaminants Emerging contaminants (ECs), encompassing pharmaceuticals, personal care products, pesticides, and industrial chemicals, represent a growing threat to ecosystems and human health due to their persistence, bioaccumulation potential, and often-unknown toxicological profiles. Addressing these challenges necessitates advanced analytical tools capable of detecting and quantifying trace levels of ECs in complex environmental matrices. This review highlights the pivotal role of mass spectrometry (MS) in monitoring ECs, emphasizing its high sensitivity, specificity, and versatility across various techniques such as Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and High-Resolution Mass Spectrometry (HR-MS). The application of MS has facilitated the real-time detection of volatile organic compounds, the comprehensive non-targeted screening of unknown contaminants, and accurate quantification in diverse matrices including water, soil, and air. Despite its effectiveness, challenges such as matrix interferences, a lack of standardized methodologies, and limited spectral libraries persist. However, recent advancements, including hybrid MS systems and the integration of artificial intelligence (AI), are paving the way for more efficient environmental monitoring and predictive modeling of contaminant behavior. Continued innovation in MS technologies and collaborative efforts are essential to overcome existing challenges and ensure sustainable solutions for mitigating the risks associated with emerging contaminants. Mass spectrometry ; Environmental chemistry ; Contamination ; Bioaccumulation ; Environmental science ; Biochemical engineering ; Environmental monitoring ; Complex matrix ; Chemistry ; Chromatography ; Environmental engineering ; Engineering ; Biology ; Ecology Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Haiyue Sun | Ying He | Shunda Qiao | Chu Zhang | Yufei Ma
Source: Photoacoustics (article, 2025-02-11)
Field: Chemistry — Spectroscopy

Highly sensitive H2S-LITES sensor with 80 m fiber-coupled multi-pass cell based on optical path multiplexing technology This paper presented a highly sensitive light-induced thermoelastic spectroscopy (LITES) sensor for detecting hydrogen sulfide (H₂S) gas. Optical path multiplexing (OPM) technology was implemented within the fiber-coupled multi-pass cell (MPC) to realize ∼ 80 m gas absorption optical path length (OPL), compensating the weak absorption coefficient of H<sub>2</sub>S in the near-infrared band. A circle-head quartz tuning fork (QTF) with resonant frequency of ∼ 9.5 kHz was adopted in the H₂S-LITES sensor to enhance the energy accumulation time and the detection ability. Compared with commercial QTF, signal-to-noise ratio (SNR) based on circle-head QTF was improved by a factor of 3.36 times. Minimum detection limit (MDL) for H<sub>2</sub>S detection was measured to be 238.8 ppb. When the integration time of the system was increased to 250 s, the MDL could be improved to 19.8 ppb. Multiplexing ; Optical fiber ; Optoelectronics ; Path (computing) ; Materials science ; Electronic engineering ; Telecommunications ; Computer science ; Engineering ; Computer network Spectroscopy and Laser Applications ; Photonic and Optical Devices ; Advanced Fiber Optic Sensors
Authors: Yu Pan | Bin He | Xiaolong Feng | Fan Li | Dong Chen | Ulrich Burkhardt | Claudia Felser
Source: Nature Materials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A magneto-thermoelectric with a high figure of merit in topological insulator Bi88Sb12 High thermoelectric performance is generally achieved by synergistically optimizing two or even three of the contradictorily coupled thermoelectric parameters. Here we demonstrate magneto-thermoelectric correlation as a strategy to achieve simultaneous gain in an enhanced Seebeck coefficient and reduced thermal conductivity in topological materials. We report a large magneto-Seebeck effect and high magneto-thermoelectric figure of merit of 1.7 ± 0.2 at 180 K and 0.7 T in a single-crystalline Bi<sub>88</sub>Sb<sub>12</sub> topological insulator. This result fills a gap of a high performance below 300 K and is promising for low-temperature thermoelectric applications. The large magneto-Seebeck response is attributed to the ultrahigh mobility and the Dirac band dispersion. The application of a low magnetic field to achieve a high thermoelectric performance can be extended to topological materials with similar features that are rapidly emerging because it synergistically optimizes the thermoelectric parameters. Thermoelectric effect ; Figure of merit ; Seebeck coefficient ; Materials science ; Topological insulator ; Thermoelectric materials ; Condensed matter physics ; Thermal conductivity ; Topology (electrical circuits) ; Magneto ; Thermoelectric generator ; Engineering physics ; Optoelectronics ; Physics ; Thermodynamics ; Electrical engineering ; Composite material ; Power (physics) Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: Chunhui Xie | Haoke Qiu | Lu Liu | Yang You | Hongfei Li | Yunqi Li | Zhao‐Yan Sun | Jiaping Lin | Lijia An
Source: SmartMat (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, and models. They can target various definitions, distributions, and correlations of concerned physical parameters in given polymer systems, and have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries, thus facilitating mechanism exploration, target prediction, high‐throughput screening, optimization, and rational and inverse designs. This article summarizes representative progress in the recent two decades focusing on the design, preparation, application, and sustainable development of polymer materials based on the exploration of key physical parameters in the composition–process–structure–property–performance relationship. The integration of both data‐driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented. Despite the construction and application of robust ML models, strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth. The challenges and prospects are then presented. We believe that the innovation in polymer materials will thrive along the development of ML approaches, from efficient design to sustainable applications. Computer science ; Cognitive science ; Data science ; Engineering ethics ; Psychology ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Longhai Zhang | Qiming Bing | Qin Huang | Liang Yu | Haobo Li | Dehui Deng
Source: Matter (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence for catalyst design and synthesis Catalysis ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Dahyun Daniel Lim | Alberto Ibarra | Jeong-Woo Lee | Jiyoung Jung | Wonjoon Choi | Grace X. Gu
Source: Science Advances (article, 2025-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A tunable metamaterial microwave absorber inspired by chameleon’s color-changing mechanism A metamaterial absorber capable of swiftly altering its electromagnetic response in the microwave range offers adaptability to changing environments, such as tunable stealth capabilities. Inspired by the chameleon's ability to change color through the structural transformation of photonic lattice crystals, which shift the bandgaps of reflection and transmission of visible light, we designed a crisscross structure that transforms from an expanded to a collapsed form. This transformation enables a switch between broadband absorption and peak transmission in the microwave range (4 to 18 gigahertz). The structure, optimized through data-driven design, is mechanically actuated by the rotation of interlinked trusses. This mechanism changes the entire array's response, allowing it to remain undetected by an external radar or to transmit an internal radar signal to a near-field receiver when needed. The mechanical actuation and the shifting electromagnetic response of the arrayed structure are demonstrated. Metamaterial ; Microwave ; Mechanism (biology) ; Metamaterial absorber ; Optoelectronics ; Computer science ; Materials science ; Optics ; Physics ; Telecommunications ; Tunable metamaterials Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Madhu Puttegowda
Source: Discover Applied Sciences (article, 2025-04-25)
Field: Materials Science — Polymers and Plastics

Eco-friendly composites: exploring the potential of natural fiber reinforcement Eco-friendly fiber-reinforced polymer composites are attracting substantial attention as sustainable alternatives to conventional synthetic composites. This comprehensive review examines the historical use, key drivers for adoption, varieties of natural fibers, applications, processing techniques, life cycle analysis, and future trends and challenges of natural fiber composites. Renewable resources, including jute, flax, hemp, and kenaf, are the source of natural fiber composites, which provide significant environmental advantages. These benefits include reduced carbon emissions, reduced energy consumption, and biodegradability. They offer cost-effective solutions for a diverse array of applications in industries including aerospace, construction, consumer products, automotive, marine, and medical, as well as support of rural economies. Compression molding, rotomolding, injection molding, extrusion, pultrusion, vacuum infusion, and hand layup are among the processing techniques that have substantially improved the properties and performance of these composites. Yet, obstacles persist, including the necessity for standardized testing and regulations, moisture absorption, and the variability of fiber properties. As they address these issues, continuous research and development are essential for the successful and widespread adoption of natural fiber composites. In comparison to synthetic alternatives, the life cycle analysis of natural fiber composites emphasizes their sustainability, underscoring their potential to contribute to sustainable development objectives. Enhancing durability, optimizing fiber-matrix compatibility, incorporating nanotechnology, and enhancing mechanical properties are among the future trends in this field. Natural fiber composites can leverage these advancements to play a critical role in fostering a sustainable future, reducing environmental impact, and meeting the changing demands of various industries. This review emphasizes the potential and relevance of natural fiber composites in shaping the future of material science and engineering. Reinforcement ; Composite material ; Natural fiber ; Fiber ; Materials science ; Natural (archaeology) ; Synthetic fiber ; Environmentally friendly ; Geology ; Ecology ; Biology Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Sustainable Design and Development
Authors: Yufei Ma | Xiaorong Sun | Haiyue Sun | Ying He | Shunda Qiao
Source: PhotoniX (article, 2025-08-28)
Field: Chemistry — Spectroscopy

An ultra-highly sensitive LITES sensor based on multi-pass cell with ultra-dense spot pattern designed by multi-objective algorithm Abstract Multi-pass cell (MPC) with long optical path length (OPL) and high ratio of optical path length to volume (RLV) can significantly enhance the detection performance of light-induced thermoelastic spectroscopy (LITES) sensor and facilitate system integration. In this paper, an ultra-highly sensitive LITES sensor based on a MPC with ultra-dense spot pattern designed by multi-objective algorithm of parallel nondominated sorting genetic algorithm II (PNSGA-II) was reported for the first time. Five MPCs featuring different dense spot patterns were generated using this PNSGA-II algorithm. The experimental measured OPLs with an excellent RLV of &gt; 20 cm −2 closely matched the theoretical results, validating the PNSGA-II algorithm and our MPC calculation model as reliable guidance for designing MPCs with superior performance. An acetylene (C 2 H 2 )-LITES sensor system was constructed using the designed MPC with an actual OPL of 80.14 m. A self-designed round-head quartz tuning fork (QTF) with resonant frequency of 9.5 kHz was used as the detector. The C 2 H 2 -LITES sensor demonstrated good linear response to C 2 H 2 concentration. The achieved minimum detection limit (MDL) for the C 2 H 2 -LITES sensor was 4.78 ppb, which has a 3.45-fold enhancement when compared to the standard commercial QTF with resonant frequency of 32.768 kHz. Furthermore, according to Allan deviation analysis, the MDL could improve to 891 ppt at an average time of 200 s, demonstrating its ultra-highly sensitive detection performance. Hot spot (computer programming) ; Computer science ; Algorithm ; Computer network Spectroscopy and Laser Applications ; Advanced Fiber Optic Sensors ; Photonic and Optical Devices
Authors: Xu Han | Xin-De Wang | M. Xu | Feng Zhen | Baozhen Yao | Peng-Jie Guo | Ze-Feng Gao | Zhong-Yi Lu
Source: Chinese Physics Letters (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

AI-Driven Inverse Design of Materials: Past, Present, and Future Abstract The discovery of advanced materials is a cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic-structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence (AI) technology in computer science has enabled the effective characterization of the implicit association between material properties and structures, thus forming an efficient paradigm for the inverse design of functional materials. Significant progress has been achieved in the inverse design of materials based on generative and discriminative models, attracting widespread interest from researchers. Considering this rapid technological progress, in this survey, we examine the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining challenges for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers. Computer science ; Generative grammar ; Inverse ; Management science ; Artificial intelligence ; Mathematics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Supramolecular Self-Assembly in Materials
Authors: Yedluri Anil Kumar | Tholkappiyan Ramachandran | Avijit Ghosh | Abdullah G. Al‐Sehemi | Nandarapu Purushotham Reddy | Md Moniruzzaman
Source: Journal of Energy Storage (review, 2025-04-08)
Field: Materials Science — Materials Chemistry

A review on in-plane ordered MXenes-based materials in addressing challenges faced by biosensor applications MXenes ; Nanotechnology ; Biosensor ; Biochemical engineering ; Materials science ; Computer science ; Environmental science ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Asif Gulraiz | Azizeh J Al Bastaki | Khulood Magamal | Mina Subhi | A. Hammad | Abdulrahman Allanjawi | Syed Sajjad Haider Zaidi | Haris M. Khalid | Abdulla Ismail | Ghulam Amjad Hussain | Zafar Said
Source: iScience (review, 2025-02-04)
Field: Materials Science — Materials Chemistry

Energy advancements and integration strategies in hydrogen and battery storage for renewable energy systems The long term and large scale energy storage operations require quick response time and round-trip efficiency, which are not feasible with conventional battery systems. To address this issue while endorsing high energy density, long term storage, and grid adaptability, the hydrogen energy storage (HES) is preferred. This proposed work makes a comprehensive review on HES while synthesizing recent research on energy storage technologies and integration into renewable energy (RE) applications. The proposed research also identifies critical challenges related to system optimization, energy management strategies, and economic viability while featuring emerging technologies like artificial intelligence (AI) and machine learning (ML) for energy management. The proposed survey also discusses key advancements in battery technologies (lithium-ion, Ni-Cd, Ni/MH, and flow batteries) that are examined alongside innovations in HES methods. The proposed survey utilizes an extensive list of publications to date in the open literature to canvass and portray various developments in this area. Renewable energy ; Energy storage ; Hydrogen storage ; Battery (electricity) ; Hydrogen fuel ; Energy (signal processing) ; Environmental science ; Computer science ; Hydrogen ; Engineering physics ; Process engineering ; Nanotechnology ; Engineering ; Chemistry ; Materials science ; Fuel cells ; Electrical engineering ; Chemical engineering ; Physics ; Power (physics) Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research