jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huan Li | Junjie Leng | Cailong Zhou | Hao Wu | Lichun Dong | Zemin Feng
Source: Industrial & Engineering Chemistry Research (article, 2025-09-02)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted High-Throughput Screening of Covalent Organic Frameworks for Propane/Propylene Adsorption-Separation A high-throughput computational screening (HTCS) approach for identifying covalent organic frameworks (COFs) for C3H8/C3H6 adsorption-separation was developed by combining machine learning (ML) and grand canonical Monte Carlo (GCMC) simulation. COFs from two experimental data sets were first evaluated using GCMC simulation to measure the performance metrics of C3H8 adsorption capacity and C3H8/C3H6 selectivity and to extract 28 geometric and chemical feature descriptors. The obtained values for the feature descriptors and performance metrics were used to train various ML models. The best-performing interpretable ML model was determined by using the AutoML method. This selected ML model was used to screen potential COF candidates from a hypothetical data set. The most promising COFs were then validated and analyzed using GCMC simulation, revealing that gravimetric surface area and void fraction (ϕ) are the two primary factors affecting the C3H8/C3H6 adsorption-separation performance of COFs, and that the extra trees regressor model exhibits the highest accuracy in predicting COF performance. By using the developed HTCS approach, 20 top-performing COFs with strong adsorption-separation efficiency and regenerability were identified, and most of them are 3D COFs with qdl, clh, and qzl topologies. Finally, 4 of the top-performing COFs were experimentally synthesized, with the best achieving a C3H8 uptake of 60.5 mL g–1 and a separation factor of 3.67. This study demonstrates that the ML-assisted HTCS approach can enable the efficient identification of high-performance COFs for C3H8/C3H6 separation and establish a generalizable framework for high-throughput screening of functional materials. Propane ; Covalent bond ; Adsorption ; Throughput ; High-throughput screening ; Separation (statistics) ; Chemistry ; Chemical engineering ; Metal-organic framework ; Organic chemistry ; Computer science ; Telecommunications ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Ionic liquids properties and applications
Authors: Jingkun Shen | Lucy E. Walker | Kevin Ma | James D. Green | Hugo Bronstein | Keith T. Butler | Timothy J. H. Hele
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Learning radical excited states from sparse data Emissive organic radicals are currently of great interest for their potential use in the next generation of highly efficient organic light emitting diode (OLED) devices and as molecular qubits. However, simulating their optoelectronic properties is challenging, largely due to spin-contamination and the multiconfigurational character of their excited states. Here we present a data-driven approach where, for the first time, the excited electronic states of organic radicals are learned directly from experimental excited state data, using a much smaller amount of data than typically required by Machine Learning. We adopt ExROPPP, a fast and spin-pure semiempirical method for the calculation of the excited states of radicals, as a surrogate physical model for which we learn the optimal set of parameters. To achieve this we compile the largest known database of organic radical geometries and their UV-vis data, which we use to train our model. Our trained model gives root mean square and mean absolute errors for excited state energies of 0.24 and 0.16 eV respectively, improving hugely over ExROPPP with literature parameters. Four new organic radicals are synthesised and we test the model on their spectra, finding even lower errors and similar correlation as for the training set. This paves the way for the high throughput discovery of next generation radical-based optoelectronics. Excited state ; Chemistry ; Computer science ; Physics ; Atomic physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Julian A. Hueffel | Quentin P. Bindschaedler | Francesco Sala | Franziska Schoenebeck
Source: Journal of the American Chemical Society (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Empowering Reactivity Predictions through Noise-Based Data Augmentation Data scarcity is a key obstacle in the pursuit to capitalize on the predictive powers of the A.I. in problems related to bond-making and -breaking at the molecular level. While the generation of artificial data from real data points (known as "data augmentation") is a widely pursued strategy employed in, for example, image or speech recognition or health data evaluations, among others, to artificially expand existing data sets, it is currently unknown whether this strategy is applicable to reactivity problems at the molecular level, where predictive models are exquisitely sensitive to steric, electronic, and structural nuances. Here, we systematically evaluated the power of data augmentation for a diverse set of reactivity questions ranging from the prediction of activation barriers to stereoselectivities of catalytic transformations. We demonstrate that introducing Gaussian noise to existing data points, which is completed in under a second for a full data set, can dramatically enhance the predictive performance. It can enable model training in low-data regimes where otherwise no meaningful model could be built and achieves accuracy comparable to models built on full data sets, while needing only a fraction of the data. The approach substantially lowers the number of necessary experiments (by 20-50%), conserving time, energy, and resources, while advancing the integration of machine learning in molecular reactivity challenges. Noise (video) ; Data set ; Predictive power ; Reactivity (psychology) ; Set (abstract data type) ; Scarcity ; Synthetic data ; Machine learning ; Computer science ; Artificial intelligence ; Chemistry ; Data mining ; Image (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Tao Wu | Lei Zhou | Yiying Zhao | Hengnian Qi | Yuanyuan Pu | Chu Zhang | Yufei Liu
Source: Artificial Intelligence Review (article, 2025-08-20)
Field: Chemistry — Analytical Chemistry

Applications of deep learning in tea quality monitoring: a review Abstract Tea is a popular beverage which can offer numerous benefits to human health and support the local economy. There is an increasing demand for accurate and rapid tea quality evaluation methods to ensure that the quality and safety of tea products meet the customers’ expectations. Advanced sensing technologies in combination with deep learning (DL) offer significant opportunities to enhance the efficiency and accuracy for tea quality evaluation. This review aims to summarize the application of DL technologies for tea quality assessment in three stages: cultivation, tea processing, and product evaluation. Various state-of-the-art sensing technologies (e.g., computer vision, spectroscopy, electronic nose and tongue) have been used to collect key data (images, spectral signals, aroma profiles) from tea samples. By utilizing DL models, researchers are able to analyze a wide range of tea quality attributes, including tea variety, geographical origin, quality grade, fermentation stage, adulteration level, and chemical composition. The findings from this review indicate that DL, with its end-to-end analytical capability and strong generalization performance, can serve as a powerful tool to support various sensing technologies for accurate tea quality detection. However, several challenges remain, such as limited sample availability for data training, difficulties for fusing data from multiple sources, and lack of interpretability of DL models. To this end, this review proposes potential solutions and future studies to address these issues, providing practical considerations for tea industry to effectively uptake new technologies and to support the development of the tea industry. Computer science ; Quality (philosophy) ; Deep learning ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Yingqi Wang | Zhirong Liu
Source: Advanced Functional Materials (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Toward Efficient and Accurate Prediction of Phonon‐Limited Carrier Mobility by First Principles Abstract Carrier mobility refers to the ability of charge carriers to move through a material in response to an applied electric field, which has a significant effect on the performance of electronic devices. Understanding and predicting carrier mobility is a fundamental yet difficult subject from a theoretical and computational perspective. In this study, the theory and first‐principles calculation methods for determining carrier mobility are reviewed. The Boltzmann transport theory and its various approximations are introduced, as well as ab initio implementations and some common analytical models for electron‒phonon coupling (EPC), which is the most important factor limiting carrier mobility. Within this theoretical framework, landmark studies on predicting the mobility of 2D materials are surveyed. Additionally, polaron transport at strong EPCs beyond Boltzmann theory is discussed. This review contains up‐to‐date related advances, including magnetotransport calculations, long‐range electron‒dynamical quadrupole coupling, machine‐learning accelerated prediction of EPC matrix elements, high‐throughput screening of high‐mobility materials, and first‐principles polaron calculations. It is aspired that this review will keep condensed matter theorists abreast of the latest advancements in transport theories and first‐principles methods while also empowering users of first‐principles calculation packages to predict transport properties correctly by fostering an understanding of the underlying physical mechanisms. Polaron ; Transport theory ; Electron mobility ; Charge carrier ; Boltzmann equation ; Coupling (piping) ; Charge (physics) ; Ab initio 2D Materials and Applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Taruna Likhariya | Pragnesh N. Dave
Source: Applied Organometallic Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Tri‐Transition Metal‐Substituted Lanthanum‐Based Perovskite Formulation for Catalytic Application in AP ABSTRACT Lanthanum‐based tri‐transition metal‐based perovskite structure was synthesized LaMn x Co y Fe z O 3 ( x + y + z = 1) in three different compositions. IR spectroscopy shows the existence of M‐O bond peak. The XRD patterns of pure cubic phase show an average crystalline size of 11.7 nm. Raman study provides insights into the various modes of vibration within the perovskite structure. BET surface area of the calcined material was 15.5 m 2 /g. N 2 adsorption curve signifies the porous nature of the adsorbent material. Its catalytic behavior towards ammonium perchlorate (AP) was established by TGA analysis, which shows 84 °C, 81 °C, and 94 °C decrease in the DSC peak, DTG peak, and DTA peak decomposition temperature of AP. Artificial neural networks were applied to forecast the TG curves and activation energy values. Chemistry ; Lanthanum ; Perovskite (structure) ; Transition metal ; Catalysis ; Metal ; Inorganic chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Chenyu Tian | Yifei Lu | Hengduo Xie | Yufan Yu | liangfu lu
Source: Scientific Reports (article, 2025-01-31)
Field: Chemistry — Analytical Chemistry

Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality cigars. The content and proportion of nicotine and other composite substances of cigar leaves have a crucial impact on their quality and vary greatly with the time of harvest. Hyperspectral remote sensing technology has been widely used in the field of crop monitoring because of its advantages of large area coverage, fast information acquisition, short cycle turnover, strong real-time performance and high efficiency. Therefore, it is important to accurately monitor nicotine content of field crops in a timely manner in the production of high-quality cigar leaf. To this end, this study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition. MSC, SG, and SNV were combined and applied to the raw data. The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. An inversion model of the content of nicotine was established, and the model was evaluated for accuracy. The main research conclusions are as follows: (1) With the increase in the rate of application of nitrogen fertilizer, the nicotine content of cigar leaves increased; (2) Processing data by the CARS, SPA, and UVE methods reduces the degree of data redundancy and information co-linearity in the screening of the content of nicotine sensitive bands; (3) The MSC-SNV-SG-CARS-BP model has the best predictive accuracy on the nicotine content. The prediction accuracy of the testing set was R<sup>2</sup> = 0.797, RMSE = 0.078,RPD = 2.182. Hyperspectral imaging ; Computer science ; Environmental science ; Nicotine ; Remote sensing ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Cong Wang | Valerio Mariani | Frédéric Poitevin | Matthew Avaylon | Jana Thayer
Source: Frontiers in High Performance Computing (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments. Software deployment ; Pipeline (software) ; Training (meteorology) ; End-to-end principle ; Deep learning ; Computer science ; Scale (ratio) ; Segmentation ; Artificial intelligence ; Real-time computing ; Cartography ; Geography ; Software engineering ; Operating system Machine Learning in Materials Science ; Advanced X-ray Imaging Techniques ; Enzyme Structure and Function
Authors: Paraskevi Gkeka | Fredrik Svensson | Carlos Roca | Marcel J. de Groot | Steven V. Jerome
Source: Journal of Medicinal Chemistry (review, 2025-05-20)
Field: Materials Science — Materials Chemistry

Computational Hit Finding: An Industry Perspective Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future. Chemistry ; Perspective (graphical) ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Sports Analytics and Performance ; Advanced Multi-Objective Optimization Algorithms
Authors: Yanjing He | Shitong Zhang | Chongli Zhong
Source: Separation and Purification Technology (article, 2025-02-23)
Field: Chemistry — Inorganic Chemistry

Unlocking the potential of ionic liquids in Anion-Pillared MOFs for enhanced He/H2 separation Performance: A combined computational screening and Machine learning study Ionic liquid ; Ion ; Chemistry ; Computer science ; Chemical engineering ; Nanotechnology ; Materials science ; Engineering ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Li Tong | Lü Wei | Yuguang Fang | Yuhan Cui | Xi Wang | Yu Li
Source: Environmental Pollution (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

Risk identification of human health, ecotoxicity, and degradation products of azo dyes: development of a priority control list Hazardous waste ; Prioritization ; Traceability ; SAFER ; Degradation (telecommunications) ; Identification (biology) ; Forced degradation Dye analysis and toxicity
Authors: Huili Sun | Weijie Liu | Haonan Xing | Xiaoqing Liu | Hai Wang | Mengyao Gao | Kun Chen | Tianyu Xia | Yongfa Cheng | Haizhong Guo
Source: ACS Nano (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Mechano-thermo-acoustic Multifunctional Sensor Based on an MXene/Silk Fibroin Aerogel Traditional sensors often suffer from limited material properties, fixed structural configurations, and a lack of tunability and three-dimensional (3D) porous architectures necessary to meet the growing demands for multifunctionality, miniaturization, and seamless integration. Here, a flexible sensor based on a 3D MXene/silk fibroin (SF) aerogel to meet the demands for multifunctionality was prepared. Due to the high electrical conductivity and superior elasticity of the layered porous MXene/SF aerogel formed by a rapid gas foaming process, the sensor is capable of monitoring multimodal signals, including mechanical, thermal, and acoustic stimuli. The sensor achieves a high sensitivity (103.14 kPa<sup>-1</sup>), a minimum detection limit (1.96 Pa), a fast response time/recovery time (110.92 ms/77.81 ms), and good cyclic stability (>10,000 cycles). In addition, the sensor exhibits excellent repeatability and recognition capability for temperature changes. More importantly, it effectively distinguishes sound signals from different languages, achieving a language recognition accuracy of 96.58% by machine learning recognition. This work provides insights and approaches that can provide inspiration for the future development of high-performance and multifunctional integrated flexible sensors. Aerogel ; Fibroin ; Porosity ; Sensitivity (control systems) ; Response time ; Elasticity (physics) ; Photonics MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Clara Mariana Gonçalves Lima | Paula Giarolla Silveira | Renata Ferreira Santana | Eugénio da Piedade Edmundo Sitoe | Renata Cristina Ferreira Bonomo | Henrique Douglas Melo Coutinho | Jolanta Wawrzyniak | V. Anjos | Maria José Valenzuela Bell | José Luís Contado | Gökhan Zengin | Roney Alves da Rocha
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-02-28)
Field: Chemistry — Analytical Chemistry

Leveraging infrared spectroscopy for cocoa content prediction: A dual approach with Kohonen neural network and multivariate modeling Chemistry ; Multivariate statistics ; Self-organizing map ; Artificial neural network ; Dual (grammatical number) ; Spectroscopy ; Multivariate analysis ; Content (measure theory) ; Chemometrics ; Infrared spectroscopy ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Chromatography ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Michael O’Brien | David Silva-Sánchez | Geoffrey Woollard | Kwanghwi Je | Sonya M. Hanson | Daniel Needleman | Pilar Cossio | Erik H. Thiede | Miro A. Astore
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-24)
Field: Materials Science — Surfaces, Coatings and Films

cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image simulation library for building computational data analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM. Python (programming language) ; Leverage (statistics) ; Vectorization (mathematics) ; Inference ; Software deployment ; Image processing ; Computational model ; Experimental data ; Image formation Electron and X-Ray Spectroscopy Techniques