jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shihai Cao | Kaiyong Sun | Z.G. Tang | Yuntong Sun | Jong‐Min Lee
Source: The Chemical Record (review, 2025-09-01)
Field: Materials Science — Materials Chemistry

Photocatalytic Degradation of Emerging Pollutants Using Covalent Organic Frameworks Emerging contaminants (ECs), such as pharmaceuticals, microplastics, and endocrine disruptors, pose persistent threats to human and ecological health due to their refractory nature. Covalent organic frameworks (COFs) are attractive crystalline porous materials for photocatalytic environmental remediation. Their high surface area, tunable structures, and stability complement the sustainable and efficient nature of photocatalysis, demonstrating great potential for treating ECs. This review systematically summarizes COF-based photocatalysts for ECs degradation, highlighting the synergistic mechanisms and performance breakthroughs of hybrid systems in enhancing efficiency, spectral response, and stability. It reveals its unique advantages over traditional photocatalysts in the selective degradation of complex pollutant molecules. This study aims to comprehensively evaluate COF-based photocatalytic systems for ECs degradation and proposes that integrating theoretical calculation, machine learning, and the rational design, and synthesis of COFs will be a key future direction for developing multifunctional catalytic systems and constructing intelligent photocatalytic systems. With further advancement, COF-based photocatalytic technology is expected to achieve large-scale application in fields, such as advanced treatment of industrial wastewater, micropollutant purification of drinking water, and environmental remediation, providing a green and efficient solution for global ECs management. Photocatalysis ; Environmental remediation ; Pollutant ; Degradation (telecommunications) ; Sewage treatment ; Nanotechnology ; Materials science ; Environmental science ; Biochemical engineering ; Computer science ; Catalysis ; Environmental engineering ; Chemistry ; Contamination ; Engineering ; Organic chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Chi-Huan Tung | Lijie Ding | Ming‐Ching Chang | Guan-Rong Huang | Lionel Porcar | Yangyang Wang | Jan‐Michael Y. Carrillo | Bobby G. Sumpter | Yuya Shinohara | Changwoo Do | Wei‐Ren Chen
Source: The Journal of Chemical Physics (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions-two representative soft matter systems-we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems. Scattering ; Soft matter ; Inversion (geology) ; Computer science ; Reciprocal lattice ; Artificial intelligence ; Physics ; Geology ; Optics ; Colloid ; Engineering ; Diffraction Material Dynamics and Properties ; NMR spectroscopy and applications ; Advanced Neuroimaging Techniques and Applications
Authors: Filip Rękas | Marcin Chutkowski | Krzysztof Kaczmarski
Source: Journal of Chromatography A (article, 2025-03-03)
Field: Chemistry — Analytical Chemistry

Application of physics-informed neural networks to predict concentration profiles in gradient liquid chromatography Chemistry ; Chromatography ; High-performance liquid chromatography ; Artificial neural network ; Artificial intelligence ; Computer science Chromatography in Natural Products ; Analytical Chemistry and Chromatography ; Advanced Control Systems Optimization
Authors: Roman Bushuiev | Anton Bushuiev | Raman Samusevich | Corinna Brungs | Josef Šivic | Tomáš Pluskal
Source: ChemRxiv (preprint, 2025-04-10)
Field: Chemistry — Analytical Chemistry

Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing computational methods for predictions from mass spectra rely on limited spectral libraries and on hard-coded human expertise. Here, we introduce a transformer-based neural network pre-trained in a self-supervised way on millions of unannotated tandem mass spectra from our GNPS Experimental Mass Spectra (GeMS) dataset mined from the MassIVE GNPS repository. We show that pre-training our model to predict masked spectral peaks and chromatographic retention orders leads to the emergence of rich representations of molecular structures, which we name Deep Representations Empowering the Annotation of Mass Spectra (DreaMS). Further fine-tuning the neural network yields state-of-the-art performance across a variety of tasks. We make our new dataset and model available to the community and release the DreaMS Atlas -- a molecular network of 201 million MS/MS spectra constructed using DreaMS annotations. Tandem ; Computer science ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Carlos Cruz‐Cortés | Eli Fernández‐de Gortari | Rodrigo Aguayo‐Ortiz | Jaroslava Šeflová | Adam Ard | Martin C. Clasby | Justus Anumonwo | L. Michel Espinoza‐Fonseca
Source: ChemMedChem (article, 2025-01-23)
Field: Chemistry — Electrochemistry

Machine Learning‐Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a A key molecular dysfunction in heart failure is the reduced activity of the cardiac sarcoplasmic reticulum Ca<sup>2+</sup>-ATPase (SERCA2a) in cardiac muscle cells. Reactivating SERCA2a improves cardiac function in heart failure models, making it a validated target and an attractive therapeutic approach for heart failure therapy. However, finding small-molecule SERCA2a activators is challenging. In this study, we used a machine learning-based virtual screening to identify SERCA2a activators among 57,423 natural products. The machine learning model identified ten structurally related natural products from Zingiber officinale, Aframomum melegueta, Alpinia officinarum, Alpinia oxyphylla, and Capsicum (chili peppers) as SERCA2a activators. Initial ATPase assays showed seven of these activate SERCA at low micromolar concentrations. Notably, two natural products, Yakuchinone A and Alpinoid D displayed robust concentration-dependent responses in primary ATPase activity assays, efficient lipid bilayer binding and permeation in atomistic simulations, and enhanced intracellular Ca<sup>2+</sup> transport in adult mouse cardiac cells. While these natural products exert off-target effects on Ca<sup>2+</sup> signaling, these compounds offer promising avenues for the design and optimization of lead compounds. In conclusion, this study increases the array of calcium pump effectors and provides new scaffolds for the development of novel SERCA2a activators as new therapies for heart failure. SERCA ; Ryanodine receptor ; ATPase ; Heart failure ; Pharmacology ; Natural product ; Calcium ATPase ; Calcium in biology ; Chemistry ; Biochemistry ; Endoplasmic reticulum ; Intracellular ; Biology ; Medicine ; Enzyme ; Internal medicine Electrochemical Analysis and Applications ; Receptor Mechanisms and Signaling ; Semiconductor materials and devices
Authors: G. Narasimha | Dejia Kong | Paras Regmi | Rongying Jin | Zheng Gai | Rama K. Vasudevan | Maxim Ziatdinov
Source: npj Computational Materials (article, 2025-06-19)
Field: Materials Science — Materials Chemistry

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy Abstract Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects. Scanning tunneling microscope ; Property (philosophy) ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Electronic and Structural Properties of Oxides
Authors: Malte Grunert | Max Großmann | Erich Runge
Source: Nature Communications (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Machine learning climbs the Jacob’s Ladder of optoelectronic properties The use of machine learning (ML) as a powerful tool for the prediction of optoelectronic properties is still hampered by the inadequate level of the calculated training datasets, which are almost exclusively obtained within the independent-particle approximation (IPA). Drawing on Perdew's Jacob's ladder analogy in density functional theory, we demonstrate how ML can ascend from the IPA to the random phase approximation (RPA), figuratively climbing the second rung. We show that as few as 300 RPA calculations suffice to fine-tune a graph attention network initially trained on 10,000 IPA calculations. Its prediction accuracy approaches that of a network directly trained on our large database of around 6000 RPA spectra. Our results highlight how transfer learning even with a small amount of high-fidelity data significantly improves predicted optical properties. Moreover, by retraining on RPA data from materials with smaller unit cells, the model generalizes effectively to larger unit cells, demonstrating broad scalability. Scalability ; Random phase approximation ; Computer science ; Transfer of learning ; Density functional theory ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics ; Database Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Anna Vidal-López | Judith Mahringer | Aleix Comas‐Vives
Source: The Journal of Physical Chemistry C (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Key Descriptors of Single-Atom Catalysts Supported on MXenes (Mo<sub>2</sub>C, Ti<sub>2</sub>C) Determining CO<sub>2</sub> Activation CO2 activation is crucial to its upgrade to fuels and chemicals. In this work, we systematically studied CO2 cleavage on single-atom catalysts (SACs) based on metals M = (Fe, Ru, Co, Rh, Ni, Pd, Pt, Cu, Ag, Au) supported on Mo2COx (6/9 O ML) and Ti2COx (7/9 O ML) MXenes via Density Functional Theory (DFT) calculations and Bader charge analysis to provide insights into the charge redistribution among the metal, MXene, interface, and CO2 during the process. CO2 activation involves a two-step mechanism, adsorbing at the M–MXene interface where it bends and acquires a highly anionic character and then breaks, forming CO* and O*. The energy barriers analyzed for the CO2 activation on M/Mo2COx and M/Ti2COx surfaces show that Cu, Ni, Rh, and Pt on Mo2COx and Cu, Ru, and Rh on Ti2COx presented the lowest energy barriers. Comparing the two MXenes, the electrophilic nature of Mo atoms facilitates CO2 cleavage, while the Ti atoms distribute charge differently, hindering the CO2 activation process. The energy barriers toward CO2 activation on M/Mo2COx and M/Ti2COx surfaces show that Cu, Ni, Rh, and Pt on Mo2COx and Cu, Ru, and Rh on Ti2COx presented the lowest energy barriers. Mo2COx systems presented geometrical structures of the transition states that were more product-like aligning with the Hammond’s principle, implying exoenergetic processes and lower energy barriers in contrast to Ti2COx. Moreover, the CO2 activation on M/2D-Mo2C follows a Brønsted–Evans–Polanyi (BEP) relationship while M/2D-Ti2C breaks it, a crucial factor to identify better catalytic materials. The ExtraTreesRegressor machine learning algorithm effectively predicts adsorption and transition-state energies using a small set of descriptors. The findings underscore the importance of transition metal electronic states, charge transfer, and support structure effects for SACs on MXenes, providing valuable insights for the design of catalytic materials. This detailed analysis provides a deeper understanding of the mechanistic aspects of CO2 activation, highlighting the role of single-atom metals and their interaction with metal-carbide surfaces. MXenes ; Catalysis ; Key (lock) ; Atom (system on chip) ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Operating system ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: Chuanbing Cai | Yao Wen | Lei Yin | Ruiqing Cheng | Hao Wang | Xiaoqiang Feng | Liang Liu | Chao Jiang | Jun He
Source: Nature Communications (article, 2025-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-volatile electric-field control of room-temperature ferromagnetism in Fe3GaTe2 heterostructures Van der Waals multiferroic structures hold promises for advancing the development of low-power multifunctional nanoelectronics devices, but single-phase two-dimensional multiferroic materials are limited. In this study, we constructed a room-temperature P(VDF-TrFE)/Fe<sub>3</sub>GaTe<sub>2</sub> heterostructure (ferromagnetic layer thickness of 4.8 nm). and demonstrate significant bidirectional modulation of the Curie temperature upon application of ±90 V. Specifically, the Curie temperature decreased from 326 K to 247 K under +90 V and increased to 366 K under -90 V. Notably, we observed layer-dependent magnetic modulation, In 3-layer Fe<sub>3</sub>GaTe<sub>2</sub>, transitioning from negative to positive polarization increases Curie temperature, while thicker configurations show a decrease. This phenomenon originates from the competition between interlayer/intralayer magnetic exchange coupling driven by the electric field (density functional theory calculations), supporting non-volatile switching of the magnetization state, which is suitable for high-precision neural network computing. This discovery provides an innovative approach for developing low-power multifunctional nanoelectronics devices using two-dimensional magnetoelectric coupling structures. Ferromagnetism ; Electric field ; Heterojunction ; Condensed matter physics ; Materials science ; Engineering physics ; Environmental science ; Nanotechnology ; Optoelectronics ; Physics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Danyang Fu | Wei-Ze Yang | Meiquan Yang | Tianmei Yang | Yuan-Zhong Wang | Jinyu Zhang
Source: Food Bioscience (article, 2025-02-25)
Field: Chemistry — Analytical Chemistry

Based on metabolomics and fourier transforms near infrared spectroscopy characterization of Lanxangia tsaoko chemical profile differences among fruit types and development of rapid identification and nutrient prediction models Characterization (materials science) ; Identification (biology) ; Fourier transform infrared spectroscopy ; Metabolomics ; Fourier transform ; Infrared ; Nutrient ; Infrared spectroscopy ; Biological system ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Botany ; Nanotechnology ; Optics ; Mathematics ; Environmental chemistry ; Biology ; Chromatography ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Botanical Research and Applications ; Phytochemicals and Antioxidant Activities
Authors: Jisheng Yang | Bingzhen Wang | Quanling Zhao | Kun Liu | Liuting Mo | Haishun Du | Zhiyong Qin | Xuejun Pan
Source: SusMat (article, 2025-07-02)
Field: Materials Science — Polymers and Plastics

Sustainable Conductive Organohydrogel Strengthened by Lignin@Polypyrrole Core–Shell Nanoparticles for Multifunctional Wearable Electronics Abstract Conductive gels are utilized as wearable sensors in flexible electronic materials due to their human skin‐like adaptability. However, achieving high strength, durability, and sustainability simultaneously remains a challenge. In this study, a tough, durable, recyclable, green, and multifunctional semi‐interpenetrating network organohydrogel was developed and enhanced by lignin@polypyrrole core–shell nanoparticles (LP9). The semi‐interpenetrating network organohydrogel was constructed using environmentally friendly poly (vinyl alcohol) and bio‐based gelatin. The LP9 was synthesized via in‐situ polymerization of pyrrole on lignin nanoparticles, serving as rigid anchors to enhance the gel's properties and eliminate heterogeneity through hydrogen bonding. With 5% of LP9, the organohydrogel (5LP9) demonstrated a tensile strength of 2.5 MPa, elongation of 700%, conductivity of 432 mS/m, and a gauge factor of 1.7 with a good linearity, highlighting its excellent performance as an electronic conductive material. In addition, the organohydrogel exhibited remarkable environmental stability, antimicrobial properties, recyclability, and biocompatibility. When applied to human motion detection, voice recognition, and gesture recognition, the organohydrogel showcased excellent recognition ability, responsive functionality, and long‐term monitoring stability. These findings provide a theoretical foundation for developing green and programmable wearable sensors for human–machine interaction, incorporating deep learning such as letter‐writing recognition. Polypyrrole ; Materials science ; Wearable technology ; Nanotechnology ; Electronics ; Nanoparticle ; Lignin ; Wearable computer ; Core (optical fiber) ; Shell (structure) ; Electrical conductor ; Electrically conductive ; Composite material ; Polymer ; Engineering ; Chemistry ; Electrical engineering ; Organic chemistry ; Embedded system Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials
Authors: Jules Schleinitz | Alba Carretero‐Cerdán | Anjali Gurajapu | Yonatan Harnik | Gina Lee | Ashok Pandey | Anat Milo | Sarah E. Reisman
Source: Journal of the American Chemical Society (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates The development of machine learning models to predict the regioselectivity of C(sp<sup>3</sup>)-H functionalization reactions is reported. A data set for dioxirane oxidations was curated from the literature and used to generate a model to predict the regioselectivity of C-H oxidation. To assess whether smaller, intentionally designed data sets could provide accuracy on complex targets, a series of acquisition functions were developed to select the most informative molecules for the specific target. Active learning-based acquisition functions that leverage predicted reactivity and model uncertainty were found to outperform those based on molecular and site similarity alone. The use of acquisition functions for data set elaboration significantly reduced the number of data points needed to perform accurate prediction, and it was found that smaller, machine-designed data sets can give accurate predictions when larger, randomly selected data sets fail. Finally, the workflow was experimentally validated on five complex substrates and shown to be applicable to predicting the regioselectivity of arene C-H radical borylation. These studies provide a quantitative alternative to the intuitive extrapolation from "model substrates" that is frequently used to estimate reactivity on complex molecules. Regioselectivity ; Chemistry ; Leverage (statistics) ; Extrapolation ; Workflow ; Chemical space ; Experimental data ; Training set ; Molecular descriptor ; Artificial intelligence ; Data mining ; Biological system ; Computer science ; Quantitative structure–activity relationship ; Drug discovery ; Stereochemistry ; Database ; Organic chemistry Machine Learning in Materials Science ; Synthesis and Catalytic Reactions ; Organic Chemistry Cycloaddition Reactions
Authors: Hüseyin Ecevit | Türkan Altun
Source: Journal of Water Process Engineering (article, 2025-06-19)
Field: Chemistry — Organic Chemistry

Catalytic degradation of metronidazole by NaBH4 reduction using Ag/Fe3O4-bentonite nanocomposite: Artificial neural network modeling Bentonite ; Nanocomposite ; Degradation (telecommunications) ; Catalysis ; Reduction (mathematics) ; Chemical engineering ; Chemistry ; Materials science ; Nanotechnology ; Computer science ; Organic chemistry ; Engineering ; Mathematics Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Cloud Data Security Solutions