jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louise Andersson | Michele Roggia | Kittikorn Wangriatisak | Maria P. Gil | Karine Chemin | Sandro Cosconati | Paweł Kozielewicz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-25)
Field: Chemistry — Spectroscopy

AI-Driven Virtual Screening and First-in-Class Conformational Biosensor Enable the Discovery of a GPR183 Inverse Agonist ABSTRACT GPR183 is a chemotactic G protein-coupled receptor implicated in immune cell migration and various diseases. Here, we report the discovery of novel GPR183 inverse agonists using an artificial intelligence-driven virtual screening approach combined with biophysical sensors including a first-in-class conformational biosensor. From an initial screen of 70 compounds and a hit expansion, a selected candidate - compound 78 - demonstrated potent inhibition of GPR183’s constitutive and agonist-stimulated Gi activation as well β-arrestin2 recruitment. The binding of the compound to the receptor core was confirmed by studies using a new conformational sensor based on intramolecular BRET, molecular dynamics, and site-directed mutagenesis experiments. Finally, the compound 78 was able to block agonist-induced migration of peripheral blood mononuclear cells ex vivo . This work provides a novel chemical scaffold for GPR183 modulation. It introduces a new tool for dissecting receptor function, paving the way for therapeutic exploration in inflammatory, autoimmune, and neoplastic disorders. Inverse agonist ; Biosensor ; Agonist ; Inverse ; Virtual screening ; Class (philosophy) ; Chemistry ; Drug discovery ; Computer science ; Computational biology ; Nanotechnology ; Combinatorial chemistry ; Mathematics ; Artificial intelligence ; Materials science ; Biochemistry ; Receptor ; Biology Mass Spectrometry Techniques and Applications
Authors: Stef A.
H. Jansen (14592763) | Albert J. Markvoort (664315) | Freek V. de Graaf (22184014) | Martin G.T.A. Rutten (8827055) | Patricia Y. W. Dankers (8892086) | Ghislaine Vantomme (1785265) | Tom F. A. de Greef (7339526) | E. W. Meijer (1454104)
Source: Figshare (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Bayesian Optimizationfor Multicomponent SupramolecularSystems The diversity of noncovalent interactions makes the design space of multicomponent molecular systems highly complex. To efficiently explore supramolecular design space, data-driven strategies are needed. Here, we demonstrate a methodological framework for the targeted design of multicomponent molecular systems with noncovalent interactions using Bayesian optimization. Its effective applicability to supramolecular polymers is illustrated by three representative cases that reveal accelerated exploration of diverse multicomponent systems with a universal Bayesian optimization framework. The number of experiments required to arrive at optimal compositions is significantly reduced compared to random or uninformed sampling strategies, enabling the experimental study of high-dimensional design spaces. In this way, we can tune the formulation of intricate mixtures and achieve tailored macroscopic properties with minimal experimental effort. Our results show that Bayesian optimization is a general tool for developing multicomponent supramolecular systems with designed functionality. Bayesian optimization ; Supramolecular chemistry ; Bayesian probability ; Non-covalent interactions ; Design of experiments ; Sampling (signal processing) ; Complex system ; Optimal design Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer composites and self-healing
Authors: Sabyasachi Chakraborty (20904305) | Itay Almog (21415010) | Renana Gershoni-Poranne (1709998)
Source: Figshare (article, 2025-05-23)
Field: Chemistry — Organic Chemistry

COMPAS-4: A Data Set of (BN)<sub>1</sub> Substituted <i>Cata</i>-Condensed Polybenzenoid HydrocarbonsData Analysisand Feature Engineering Incorporation of a boron–nitrogen (BN) pair into polycyclic aromatic hydrocarbons is a common approach for modulating their electronic properties. However, a conceptual and quantitative framework rationalizing the observed effects has not been developed, and general structure–property relationships remain elusive. In this work, we perform a data-driven investigation that leads to concrete principles for rational design of (BN)<sub>1</sub>-PBHs with targeted properties. We construct a new chemical database, COMPAS-4, which contains the geometries and properties of all possible (BN)<sub>1</sub>-PBH isomers up to 6 rings, calculated at both the GFN1-xTB and density functional theory (DFT) (CAM-B3LYP/def2-SVP) levels of theory. We investigate the influence of BN-substitution on various molecular properties, including their molecular orbital energies and aromaticity, and define specific structural features that determine these properties. Notably, all of these features are chemically intuitive and simple to extract from the structure of the molecule, without any prior computation. We find that the most influential feature is the number of rings whose cyclic delocalization is disturbed as a result of the substitution. Feature (linguistics) ; Construct (python library) ; Simple (philosophy) ; Set (abstract data type) ; Delocalized electron ; Density functional theory ; Molecular orbital ; Complement (music) Organoboron and organosilicon chemistry ; Synthesis and Properties of Aromatic Compounds ; Machine Learning in Materials Science
Authors: Bala Bhardwaj | Prabhat Ranjan Tiwari | Keval Bharati | Rahul Pratap Singh | Avinash Chand Yadav | Kuwar Ankur Singh | Balvant Yadav | Rajkumari | Santosh Kumar
Source: ECS Journal of Solid State Science and Technology (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Magnesium-Doped Cobalt Ferrite as a Promising Nanomaterial for Humidity Sensing Applications Humidity has a substantial impact on human health and comfort, the integrity and quality of materials and products, etc. Humidity sensors are used in electronic skin, noncontact sensing, respiratory analysis, the Internet of Things, and artificial intelligence. Porosity, thermal and chemical stability, sensitivity throughout a broad humidity range, oxygen vacancies, and high resistivity make spinel ferrite a promising material for humidity-sensing applications. Herein, the sol-gel auto-combustion method was used to synthesize magnesium (Mg)-doped cobalt ferrite (CoFe 2 O 4 ) nanomaterials. Powder X-ray diffraction (PXRD), vibrating sample magnetometer (VSM), UV-Visible, field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX), and Fourier transform infrared spectroscopy (FTIR) techniques were used to characterize the samples. The PXRD divulges that the prepared nanomaterials have a cubic structure along with a single phase. FTIR spectra show the two prominent bands of tetrahedral and octahedral and confirm the spinel ferrite structure. FESEM and EDX exhibit the surface morphology and atomic composition. UV-Visible and VSM reveal the variation of band gap and magnetic characteristics. The optimized nanomaterials were used in designing a thin-film humidity sensor. The recovery time, response times, repeatability and hysteresis error of the devised sensor are 22.15 s, 21.26 s, 98.76%, and 6.11% respectively. Nanomaterials ; Spinel ; Fourier transform infrared spectroscopy ; Ferrite (magnet) ; Humidity ; Field emission microscopy ; Scanning electron microscope ; Powder diffraction Magnetic Properties and Synthesis of Ferrites ; Gas Sensing Nanomaterials and Sensors ; Electromagnetic wave absorption materials
Authors: Krishnamoorthy Santhosh | Parameswaran Jayaashree | Pavithra Thiraviyam | Sivaperumal Pitchiah
Source: Nanotechnology for Environmental Engineering (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Green synthesis of seagrass-derived nanoparticles: biomedical applications and environmental implications on aquatic DNA integrity Human health ; Drug delivery ; Nanoparticle ; Applications of nanotechnology ; Seagrass ; Sustainable production Nanoparticles: synthesis and applications ; Seaweed-derived Bioactive Compounds ; Marine Sponges and Natural Products
Authors: Shuolin Zhang | Tao Ye | Y. H. Yu | Jiongcheng Yan | Chao Song | Pengfei Zhai | Chao Zhou | Lei Feng
Source:  (article, 2025-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Recognition Method of Excitation Waveforms for Core Loss Analysis of Renewable Generation Converter Based on Magnetic Flux Density Peak Location and Feature-Optimized Random Forest The study of core loss characteristics is essential for improving the performance of renewable generation converters, and the classification and identification of excitation waveforms is fundamental to analyzing core loss characteristics. The excitation waveform is primarily characterized by the temporal distribution pattern of magnetic flux density. To accurately identify different excitation waveforms, it is necessary to extract feature variables that reflect the temporal distribution of the magnetic flux density and select an appropriate artificial intelligence algorithm to establish a classification and recognition model for excitation waveforms. However, current feature extraction methods have difficulty capturing deep features beyond traditional characteristics, which results in inaccurate classification outcomes. In response, a classification and recognition method for excitation waveforms based on magnetic flux density peak location and feature-optimized random forest is proposed. Firstly, the magnetic flux density peak location after being normalized along the time axis is extracted as a deep feature. Secondly, an excitation waveform classification model is established using artificial intelligence methods such as feature-optimized random forest. Finally, the effectiveness of the proposed method is verified using magnetic flux density sampling data from four types of core materials as examples. Waveform ; Random forest ; Renewable energy ; Excitation ; Feature (linguistics) ; Magnetic flux ; Flux (metallurgy) ; Core (optical fiber) ; Feature extraction ; Computer science ; Magnetic core ; Electrical engineering ; Artificial intelligence ; Physics ; Materials science ; Magnetic field ; Telecommunications ; Engineering ; Electromagnetic coil Magnetic Properties and Applications ; Power Systems and Renewable Energy ; High-Voltage Power Transmission Systems
Authors: Shaik Kareem Ahmmad | K. Rajagopal | Nazima Siddiqui | Mohd Abdul Muqeet | M. G. Patil | Ega Chandra Shekhar | Hima Bindu Pitta
Source: Results in Optics (article, 2025-07-01)
Field: Materials Science — Ceramics and Composites

Dielectric constant of boro silicate aluminum glasses using AI and radar sensor • First time dielectric constant study using BGT60TR13C radar on SiO₂–Na₂O–CaO–B₂O₃–Al₂O₃ glasses. • Na₂O (38 %) showed strongest impact on ε r , followed by SiO₂, CaO, B₂O₃, and Al₂O₃. • ANN gave top accuracy (R²=0.985), beating GBR and RFR in nonlinear prediction. • AI enables fast glass optimization, reducing tests and accelerating materials research. This study presents a comprehensive analysis of the dielectric constant (ε r ​) of glasses with varying chemical compositions, utilizing artificial intelligence (AI) predictions and experimental validation. An AI model, trained on over 100 glass compositions, was employed to predict ε r based on compositional inputs such as SiO 2 -Na 2 O-CaO-B 2 O 3 -Al 2 O 3 . For the first time, the BGT60TR13C radar sensor was adapted for non-contact dielectric constant measurements, offering a novel methodology for material characterization. To validate the AI predictions and sensor values, two additional experimental techniques were employed: an LCR meter for capacitance-based measurements and the parallel plate capacitor method. Results showed excellent agreement among all methods, confirming the reliability of AI predictions and the accuracy of the experimental techniques. Furthermore, the dielectric constant increased with higher concentrations of network modifiers and secondary network formers. This study highlights the integration of AI and advanced sensing technologies as a powerful hybrid framework for rapid and accurate material characterization, introducing the radar sensor as an innovative tool for dielectric measurements. Dielectric ; Radar ; Capacitor ; Constant (computer programming) ; Reliability (semiconductor) ; Capacitive sensing ; Aluminium ; Artificial neural network Glass properties and applications ; Microwave Dielectric Ceramics Synthesis ; Ferroelectric and Piezoelectric Materials
Authors: Sung Tae Yoo | Seonho Kim | Jae-Min Lee
Source:  (article, 2025-12-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large Language Model-Based Hierarchical Framework for Emerging Trends in Extreme Ultraviolet Research As artificial intelligence advances rapidly, demand for node scaling and improved device performance is increasing in semiconductors. This necessitates a systematic analysis of extreme ultraviolet (EUV) technology developments. This study introduces a hierarchical framework based on large language models (LLM) and applies it to 6,859 EUV-related papers published from 2015 to 2024. The framework combines clustering, summarization, evaluation, selection, and a decision stage guided by an LLM-based AI agent to build a hierarchical tree, and it then analyzes the hierarchy step by step to track emerging trends in the field. Emerging trends in EUV research include algorithmic metrology and overlay modeling, illumination engineering with pupil mapping, and advances in multi-beam mask writing along with the supplier ecosystem. This hierarchical framework is expected to not only determine the future direction of EUV technology but also broadly apply LLM-based methods to identify various emerging trends. Extreme ultraviolet lithography ; Hierarchy ; Extreme ultraviolet ; Node (physics) ; Overlay Ga2O3 and related materials ; Transition Metal Oxide Nanomaterials ; Photocathodes and Microchannel Plates
Authors: Aaron  David Smith | Ecaterina Bordos | Michael Devlin | Colin Hastie | Mark  J. Perkins | Vaughan  S. Langford
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Process Optimization in Pharmaceutical Hot-Melt Extrusion: Real-time Volatile Detection via SIFT-MS Combined with Multivariate Analysis Extrusion ; Thermogravimetric analysis ; Principal component analysis ; Rheology ; Polymer ; Chemical composition ; Softening ; Dispersion (optics) Polymer crystallization and properties ; Mass Spectrometry Techniques and Applications ; Rheology and Fluid Dynamics Studies
Authors: Hiroshi Morita
Source: NIPPON GOMU KYOKAISHI (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research Center of Computational Science and Materials Informatics for Designing Various High-functional Materials in AIST This paper introduces the Research Center for Computational Design of Advanced Functional Materials at the National Institute of Advanced Industrial Science and Technology. In recent years, the demand for data-driven approaches in materials development has grown significantly, prompting the establishment of this center to advance research in the design and analysis of functional materials within virtual spaces. The center brought together experts in simulation, informatics, and data platform development, who collaboratively conducted advanced studies aimed at pioneering and refining cutting-edge techniques. In terms of simulation, we possess the technology to simulate various materials such as inorganic, metallic, organic, polymeric materials, and fluid at various scale levels. As for informatics, the results of the NEDO Cho-Cho project can be cited. As for the data platform, we are constructing database systems and the technology of a secure computing for sharing. As the only large-scale research center dedicated to such a broad spectrum of technologies for materials design, the center represents a unique and invaluable resource. However, it has been determined that the center will transform into a successor research center starting in April 2025, heralding an exciting new phase of development. We are also looking forward to its future endeavors. Center (category theory) ; Informatics ; Computer science ; Engineering ; Chemistry Catalytic Processes in Materials Science
Authors: Mallari, Praveen | M., Muthuselvi | Parvathi, Venkatachalam Deepa | G.S., Hikku
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Green nanotechnology for environmental remediation: a sustainable approach to combat pollution Environmental pollution remains a persistent global challenge, with more than ~170 billion cubic metres of wastewater released per year and 90% of the global population is living in areas where concentrations exceeded the world health organisation air quality guideline for long term exposure to PM2.5. This review systematically examines the progress and prospects of green nanotechnology in environmental remediation, focusing on biogenic synthesis routes and application performance of various nanomaterials like metal and metal oxide nanoparticles, carbon-based nanostructures, bio-nanocomposites, and magnetic nanomaterials. The study integrates over 100 peer-reviewed sources to synthesise quantitative data and mechanistic insights. Findings reveal that green synthesised nanoparticles demonstrate employability in real-world applications in wastewater treatment, soil remediation, air pollution control, and oil spill cleanup. However, the review identifies unresolved concerns regarding nanoparticle fate, ecotoxicity, and lifecycle impacts, underscoring a critical gap in standardised assessment and regulatory governance. This work contributes a holistic synthesis framework linking material synthesis, mechanistic pathways, and environmental implications, thereby providing a data-driven roadmap for scaling green nanomaterials in real-world remediation. By aligning material design with circular economy principles and eco-safety evaluation, this review advances the conceptualisation of green nanotechnology as a sustainable and scalable solution to global environmental pollution challenges. Sustainability ; Sustainable development ; Applications of nanotechnology ; Pollution ; Environmental pollution ; Work (physics) ; Environmental quality ; Air pollution Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Graphene and Nanomaterials Applications
Authors: Muhammad
Hassan Shaikh (22371078) | Matthew P. Whalen (19352612) | Dai Q. Ho (13802810) | Aqiq Ishraq (14700505) | Collin Maurtua (21807259) | Kenji Watanabe (75040) | Takashi Taniguchi (331592) | Yafei Ren (13282178) | Anderson Janotti (4808076) | John Q. Xiao (2236846) | Chitraleema Chakraborty (1552813)
Source: Figshare (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Magnetic ProximityCoupling to Defects in a Two-DimensionalSemiconductor The ultrathin structure and efficient spin dynamics of two-dimensional (2D) antiferromagnetic (AFM) materials hold promising opportunities for ultrafast memory devices, artificial intelligence circuits, and advanced computing technology. For example, chromium thiophosphate (CrPS<sub>4</sub>) is one of the most promising 2D A-type AFM materials due to its robust stability in diverse environmental conditions and net out-of-plane magnetic moment in each layer, attributed to anisotropy in crystal axes (<i>a</i> and <i>b</i>). However, their net-zero magnetic moment poses a challenge for detecting the Néel state that is used to encode information. In this study, we demonstrate the detection of the Néel vector by detecting the magnetic order of the surface layer by employing localized defects in tungsten diselenide (WSe<sub>2</sub>). These defects are ideal candidates for optically active transducers to probe the magnetic order due to their narrow line width and high susceptibility to magnetic field (<i>B</i>-fields). We observed spin-polarized charge transfer in the heterostructure of bulk CrPS<sub>4</sub> and single-layer WSe<sub>2</sub>, indicating type-II band alignment as supported by density functional theory (DFT) calculations. In the A-type AFM regime, the intensity of both right-handed and left-handed circularly polarized light emanating from the sample remains constant as a function of the applied <i>B</i>-field, indicating a constant polarized transition behavior. Our results demonstrate an approach to optically characterizing the magnetic states of 2D bulk AFM material by using both localized and delocalized defect excitons as a probe, highlighting avenues for future research and technological applications. Magnetic moment ; Tungsten diselenide ; Magnetic field ; Magnetic force microscope ; Magnetic semiconductor ; Density functional theory ; Antiferromagnetism ; Magnetic anisotropy ; Semiconductor ; Magnetism 2D Materials and Applications ; Iron-based superconductors research ; Topological Materials and Phenomena
Authors: Muhammad Hassan Shaikh | Matthew P. Whalen | Dai Q. Ho | Aqiq Ishraq | Collin Maurtua | Kenji Watanabe | Takashi Taniguchi | Yafei Ren | Anderson Janotti | John Q. Xiao | Chitraleema Chakraborty
Source: ACS Nano (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Magnetic Proximity Coupling to Defects in a Two-Dimensional Semiconductor The ultrathin structure and efficient spin dynamics of two-dimensional (2D) antiferromagnetic (AFM) materials hold promising opportunities for ultrafast memory devices, artificial intelligence circuits, and advanced computing technology. For example, chromium thiophosphate (CrPS<sub>4</sub>) is one of the most promising 2D A-type AFM materials due to its robust stability in diverse environmental conditions and net out-of-plane magnetic moment in each layer, attributed to anisotropy in crystal axes (<i>a</i> and <i>b</i>). However, their net-zero magnetic moment poses a challenge for detecting the Néel state that is used to encode information. In this study, we demonstrate the detection of the Néel vector by detecting the magnetic order of the surface layer by employing localized defects in tungsten diselenide (WSe<sub>2</sub>). These defects are ideal candidates for optically active transducers to probe the magnetic order due to their narrow line width and high susceptibility to magnetic field (<i>B</i>-fields). We observed spin-polarized charge transfer in the heterostructure of bulk CrPS<sub>4</sub> and single-layer WSe<sub>2</sub>, indicating type-II band alignment as supported by density functional theory (DFT) calculations. In the A-type AFM regime, the intensity of both right-handed and left-handed circularly polarized light emanating from the sample remains constant as a function of the applied <i>B</i>-field, indicating a constant polarized transition behavior. Our results demonstrate an approach to optically characterizing the magnetic states of 2D bulk AFM material by using both localized and delocalized defect excitons as a probe, highlighting avenues for future research and technological applications. Magnetic moment ; Tungsten diselenide ; Magnetic field ; Density functional theory ; Magnetic force microscope ; Magnetic semiconductor ; Antiferromagnetism ; Semiconductor ; Delocalized electron ; Graphene ZnO doping and properties ; Magnetic properties of thin films ; Solidification and crystal growth phenomena