jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Poštulka | Petr Slavı́ček | Johannes Kästner | Germán Molpeceres
Source: Astronomy and Astrophysics (article, 2025-04-07)
Field: Chemistry — Spectroscopy

Diffusive versus non-diffusive paths to interstellar hydrogen peroxide Context. Radical chemical reactions on cosmic dust grains play a crucial role in forming various chemical species. Among different radicals, the hydroxyl (OH) is one of the most important, with a rather specific chemistry. Aims. The goal of this work is to simulate the recombination dynamics of hydroxyl radicals and the subsequent formation of hydrogen peroxide (H 2 O 2 ). Methods. We employed neural-network potentials trained on ONIOM(QM/QM) data, combining multi-reference (CASPT2) and density functional theory calculations. This approach allowed us to model the recombination of hydroxyl radicals on ice surfaces with high computational efficiency and accuracy. Results. Our simulations reveal that the initial position of the radicals plays a decisive role in determining recombination probability. We found that the formation of a hydrogen bond between radicals competes with the formation of hydrogen peroxide, reducing the recombination efficiency, which is contrary to what was expected. This competition reduces the recombination probability for radicals that are initially formed approximately 3 Å apart. Recombination probabilities also depend on the kinetic energy of the added radicals, with values around 0.33 for thermal radicals and a wide range of values between 0.33 and 1.00 for suprathermal OH radicals. Conclusions. Based on our calculations, we provide recommendations for introducing OH radical recombination into kinetic astrochemical models, differentiating between thermal and suprathermal radicals. The recombination behaviour varies significantly between these two cases: while thermal radicals are sometimes trapped in hydrogen-bonded minima, the case of suprathermal radicals varies with the added energy. Our most important conclusion is that OH radical recombination probability cannot be assumed to be 1.0 for a wide variety of cases. Physics ; Astrophysics ; Interstellar medium ; Molecular cloud ; Hydrogen peroxide ; Dynamics (music) ; Molecular dynamics ; Astrochemistry ; Statistical physics ; Hydrogen molecule ; Interstellar cloud ; Hydrogen ; Astronomy ; Astrobiology ; Chemical physics ; Stars ; Galaxy ; Quantum mechanics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies
Authors: Ranjana Chowdhury | Biswajit Ghose | Deep Choudhuri
Source: Langmuir (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Uncovering the Role of Critical Bonds in the Thermomechanical Response of Zn<sub>2</sub>(1,4-benzenedicarboxylate)<sub>2</sub>(1,4-diazabicyclo[2,2,2]octane) Using <i>Ab Initio</i> Simulations and Physics-Constrained Neural Networks Metal-organic frameworks (MOFs) are susceptible to harsh conditions involving high temperatures, mechanical loading, host/guest chemical interactions, or a combination thereof due to the disruption of underlying bonds. Here, we probed the thermomechanical response of Zn<sub>2</sub>(BDC)<sub>2</sub>(DABCO) (BDC = 1,4-benzenedicarboxylate and DABCO = 1,4-diazabicyclo[2,2,2]octane) MOF or Zn-DMOF, whose metal nodes are connected to two BDC and DABCO linkers via Zn-O and N-Zn bonds, respectively. We have examined the relative contributions of such bonds toward Zn-MOF's thermomechanical response at 200 and 300 K, by training a physics-constrained neural network with <i>ab initio</i> molecular dynamics (AIMD)-derived interatomic vibration data, and quantitatively estimated interatomic bond strengths within a local environment in the MOF body. We quantitatively show that N-Zn and Zn-O bonds near the Zn-based metal nodes are weaker than their surroundings. These findings were followed up by separately annealing Zn-DMOF at 920 K-AIMD (above its thermal decomposition temperature), mechanically straining it to failure using 0 K-DFT, and examining host/guest interactions in aqueous and acidic environments with 300 K-AIMD. Together, they indicated that structural instability in Zn-DMOF was initiated by the disruption of N-Zn and Zn-O bonds under harsh conditions and that Zn-O bonds are weaker than N-Zn bonds at <i>T</i> ≥ 300 K. Broadly, we show that bond strength estimates are a reasonable indicator of Zn<sub>2</sub>(BDC)<sub>2</sub>(DABCO)'s performance at high temperatures and mechanical loading, and demonstrate the viability of employing AIMD simulations and physics-constrained neural networks to quantify interatomic bond strengths of hybrid organic-inorganic materials. Ab initio ; Octane ; Chemistry ; Crystallography ; Ab initio quantum chemistry methods ; Materials science ; Thermodynamics ; Computational chemistry ; Molecule ; Organic chemistry ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research
Authors: Zubair Sadiq | Wenhong Yang | Weishen Yang | Wen‐Hua Sun
Source: Journal of Computational Chemistry (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Multi‐Task Learning in Homogeneous Catalysis: A Case Study for Predicting the Catalytic Performance in Ethylene Polymerization This study focuses on training a multi-task learning (MTL) type machine learning (ML) model to predict diverse catalytic performance of 195 bis(imino)pyridine transition metal complexes toward ethylene polymerization, with comparison to their single-task learning (STL) counterparts. The CatBoost MTL model outperforms all other models, showing predictions and generalization errors for the properties of catalytic activity (R<sub>t</sub> <sup>2</sup>=0.741, R<sup>2</sup> = 0.985, Q<sup>2</sup> = 0.600), molecular weight (R<sub>t</sub> <sup>2</sup>=0.873, R<sup>2</sup> = 0.997, Q<sup>2</sup> = 0.846), molecular weight distribution (R<sub>t</sub> <sup>2</sup>=0.831, R<sup>2</sup> = 0.999, Q<sup>2</sup> = 0.839), and melting temperature (R<sub>t</sub> <sup>2</sup>=0.813, R<sup>2</sup> = 0.992, Q<sup>2</sup> = 0.625) of the produced polymer. The interpretation of the model reveals that complexes with electron-donating groups, simple alkyl groups (such as methyl groups etc.), and a higher degree of unsaturation (presence of double or triple bonds) positively influence the predicted properties. Subsequently, providing insights into the underlying mechanisms of variation in catalytic performance, new complexes are designed with superior catalytic performances. Catalysis ; Polymerization ; Degree of unsaturation ; Chemistry ; Alkyl ; Ethylene ; Homogeneous ; Task (project management) ; Molar mass distribution ; Polymer ; Pyridine ; Polymer chemistry ; Organic chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Shimanta Das | Chongze Hu
Source: Mechanics of Materials (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

Deciphering grain-boundary composition-structure-mechanical property relationships via interfacial property diagrams Materials science ; Property (philosophy) ; Grain boundary ; Boundary (topology) ; Composite material ; Mechanics ; Microstructure ; Mathematical analysis ; Mathematics Microstructure and mechanical properties ; Advanced materials and composites ; Composite Material Mechanics
Authors: Maicon Pierre Lourenço | Rishabh Shukla | Mosayeb Naseri | Daya Gaur | Utkarsh Singh | Sergey Gusarov | Dennis R. Salahub
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Seeking metal–organic frameworks for hydrogen storage using classical and quantum active learning Metal-organic frameworks (MOFs) are porous materials with applications from chemical sensing to gas storage and separation. The development of MOFs for hydrogen storage is highly desired. Hydrogen is a clean source of energy and storing it within MOFs depends on their structure, stability, synthesis design and adsorption characteristics. The design space to obtain MOFs with suitable target properties can be aided by artificial intelligence methods that use few data for new discovery, such as active learning (AL) and the recent quantum AL (QAL) method. In this work, AL and QAL methods for MOF design were developed and tested in the search for MOFs and experimental conditions (temperature and pressure) that have enhanced hydrogen storage capability. Our aim is to explore the performance of these techniques in finding this optimum material within an experimental MOF dataset obtained from the literature. The AL methods investigated in this work use artificial neural networks, support vector regression, and classical and quantum Gaussian processes (GP and QGP) as regression models for inference. Different uncertainty quantification methods for the regression models as well as different acquisition functions for decision making were considered to select the next MOF to be measured by further "experiments". The QAL performance in finding the optimum material and experimental conditions is reported. QAL uses QGP with a projected quantum kernel with a feature map with entanglement. A network graph method was developed to analyze the AL and QAL performance for MOF search. The results obtained from applying AL and QAL in this known data set indicate that it is possible to search for MOFs with enhanced properties for hydrogen storage with very few data, with the ability to distinguish the optimum MOF and conditions from similar ones. This finding highlights the potential of classical and quantum "machines" (<i>i.e.</i> AL and QAL methods) to indicate new MOFs to be synthesized with enhanced adsorption properties. Artificial neural network ; Quantum ; Quantum chemical ; Porosity ; Hydrogen ; Gaussian ; Adsorption ; Hydrogen storage Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Machine Learning and ELM
Authors: Tianliang Li | Xiao Zhang | Xinting Liu | Lixing Lin | Yuze Ren | Zeyu Li | Yingying Chen | Zhenzhen Li | Lingyan Feng
Source: Chemical Engineering Journal (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

Nanozymes design via cost-sensitive machine learning for antibacterial applications Computer science ; Nanotechnology ; Process engineering ; Engineering ; Materials science Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Aries Aisporna | Bill Webb | Winnie Uritboonthai | Linh Hoang | Elizabeth Billings | Corey Hoang | Robert S. Plumb | Gary Siuzdak
Source: Analytical Chemistry (article, 2025-10-28)
Field: Chemistry — Spectroscopy

Adaptive Modeling of Tandem Mass Spectrometry Data: Creation of the METLIN 960K MRM Database Multiple Reaction Monitoring (MRM) remains the gold standard for quantitative mass spectrometry but continues to be constrained by the limited availability of high-quality transitions and collision energy (CE) values for many biologically and chemically relevant molecules. Here, we present the METLIN 960K MRM library, a 960,000-compound transition resource derived entirely from empirically acquired MS/MS data. MRM transitions were generated in both positive and negative ionization modes using an empirical spline-based pipeline refined by AI BioSync, an XCMS enhancement that provides a framework of AI and machine-learning tools designed to decipher spectral data for biological and analytical relevance. Central to this approach is spline fitting of CE-dependent intensity profiles from experimental MS/MS data collected at four discrete energies (0, 10, 20, and 40 eV), enabling continuous CE modeling and precise prediction of optimal fragmentation conditions. Supervised learning models were used within AI BioSync to refine spline fitting across diverse chemical classes, improving reproducibility and predictive accuracy. Validation across more than 100 authentic compounds, including rare metabolites and diverse small molecules, demonstrated robust detection down to 1 nM, confirming both sensitivity and scalability. This framework also holds immediate applicability for preclinical drug development studies, where authentic metabolite and impurity standards are often unavailable. Unlike prior methods reliant on <i>in silico</i> fragmentation or heuristic rules, all transitions are derived directly from experimental MS/MS data using absolute intensities. The resulting precursor <i>m</i>/<i>z</i>-centric METLIN 960K MRM library (https://metlin.scripps.edu) greatly expands the chemical space accessible to targeted quantitation, providing a scalable, vendor-independent path for sensitive and specific molecular detection across research, clinical, and applied applications. Fragmentation (computing) ; Mass spectrometry ; Selected reaction monitoring ; Data acquisition ; Tandem mass spectrometry ; Chemical database ; Drug discovery ; Data quality ; Heuristic Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Sara Davoudi | Christopher S. Henry | Christopher S. Miller | Farnoush Banaei‐Kashani
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-28)
Field: Materials Science — Materials Chemistry

EC-Bench: A Benchmark for Enzyme Commission Number Prediction Abstract Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation. We introduce EC-Bench, a benchmark for EC number prediction, consisting of 1) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), 2) existing and novel accuracy and efficiency performance metrics, and 3) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative frame-work. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely “exact EC number prediction”, “EC number completion” and (partial or additional) “EC number recommendation”. We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy. Benchmark (surveying) ; Commission ; Computer science ; Business ; Geography ; Finance ; Cartography Enzyme Structure and Function ; Metalloenzymes and iron-sulfur proteins ; Amino Acid Enzymes and Metabolism
Authors: Cheng‐Chu Chung | Ruipeng Li | Gabriel M. Veith | Honghu Zhang | Fernando Camino | Ming Lu | Nikhil Tiwale | Sheng Zhang | Kevin G. Yager | Yu‐chen Karen Chen‐Wiegart
Source: Small (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment Thin-film solid-state metal dealloying (thin-film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high-throughput characterization of thermal treatment parameters is demonstrated using a laser-based thermal treatment to create temperature gradients on single thin-film samples of Nb-Al/Sc and Nb-Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X-ray multimodal and high-throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified. While the oxidation may influence nanostructure formation during thin-film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. This approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin-film SSMD systems with targeted nanostructures. Materials science ; Thin film ; Nanostructure ; Nanotechnology ; Characterization (materials science) ; Thermal treatment ; Composite material Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: King Yin Ho | Fengqi Zhang | Xuefei Miao | Niels van Dijk | E. Brück | Yang Ren
Source: MetalMat (article, 2025-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress in Magnetoelastic Fe<sub>2</sub>P‐type Materials for Magnetocaloric Cooling ABSTRACT Solid‐state magnetocaloric cooling attracts increasing attention as a novel technique for its high efficiency in energy conversion and its sustainability. Several high‐quality magnetocaloric materials (MCMs) have been found, but among these MCMs, (Mn,Fe) 2 (P,Si)‐based materials are one of the most promising candidates for its giant magnetocaloric effect (GMCE), noncritical components, and tuneable Curie temperature ( T C ) around room temperature. To further accelerate its practical market applications, a lot of studies have been made to enhance the GMCE, tune T C, and reduce the thermal hysteresis (Δ T hys ). This review mainly focuses on introducing the latest efforts to optimize the GMCE performance of this material family, including doping with different elements, microstructural improvements, and practical manufacturing techniques. Besides that, we further suggest possible approaches to boost the material engineering and highlight promising efforts of applying machine learning in the search for the optimal composition and structure of (Mn,Fe) 2 (P,Si)‐based MCMs. This bridges the gap between fundamental research and engineering to advance the magnetocaloric energy conversion technology closer to real applications. Magnetic refrigeration ; Materials science ; Type (biology) ; Metallurgy ; Engineering physics ; Engineering ; Physics ; Magnetization ; Geology ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity
Authors: Alexey P. Maltsev | Anastasiia V. Iosimovska | Ilya V. Chepkasov | Artem R. Oganov
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Structure transformations and ionic conductivity in germanides of sodium and potassium Using DFT and machine learning, this study explores Na/K germanides' structure and conductivity; Na–Ge exhibits superior ionic conductivity due to interconnected diffusion, making it a promising anode material. Potassium ; Sodium ; Ionic bonding ; Ionic conductivity ; Conductivity ; Chemistry ; Inorganic chemistry ; Materials science ; Ion ; Physical chemistry ; Electrolyte ; Organic chemistry Solid-state spectroscopy and crystallography ; Crystal Structures and Properties ; X-ray Diffraction in Crystallography
Authors: Piotr Cysewski | Tomasz Jeliński | Maciej Przybyłek | Natalia Gliniewicz | Marcel Majkowski | Michał Wąs
Source: International Journal of Molecular Sciences (article, 2025-10-16)
Field: Chemistry — Spectroscopy

Navigating the Deep Eutectic Solvent Landscape: Experimental and Machine Learning Solubility Explorations of Syringic, p-Coumaric, and Caffeic Acids Efficiently identifying suitable solvents for active pharmaceutical ingredients (APIs) is critical in drug formulation, yet the vast number of possible solvent-solute combinations presents a significant experimental challenge. This study addresses this by developing a robust machine learning (ML) model for accurately predicting the solubility of three phenolic acids (syringic, p-coumaric, and caffeic) in various deep eutectic solvents (DESs), integrating both experimental and computational investigations. Measured solubility data showed that the choline chloride combined with triethylene glycol in a 1:2 molar ratio was the most efficient system for the dissolution of the studied APIs. Different ML models, utilizing nu-Support Vector Regression (nuSVR) as the core regressor and based on descriptor sets derived from COSMO-RS (Conductor-like Screening Model for Real Solvents) computations, were systematically evaluated. A novel methodology termed DOO-IT (Dual-Objective Optimization with ITerative feature pruning) was employed to address the common challenges of model development with limited, high-value datasets. The final optimal 10-descriptor nuSVR model, selected from an exhaustive, multi-run search, demonstrated outstanding predictive power, offering a highly reliable computational tool for guiding experimental screening, significantly accelerating the exploration of DES-based formulations. This research also provides a strong foundation for future machine learning-guided discovery of chemicals, offering an effective and transferable framework for developing QSPR models for various chemical systems. Choline chloride ; Solubility ; Deep eutectic solvent ; Eutectic system ; Triethylene glycol ; Interpretability ; Support vector machine ; Solvent Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Viktoriia Bakal | Оlga I. Gusliakova | Anastasia Kartashova | Мariia S. Saveleva | Valentina O. Plastun | Polina A. Demina | Ilya O. Kozhevnikov | Alexey A. Serdobintsev | Ekaterina S. Prikhozhdenko
Source: Analytical Methods (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of silver nanoparticles on polyacrylonitrile nonwoven substrates with the silver mirror reaction for effective SERS detection of 4-MBA and glucose We present a simple, fast, and efficient method for the production of silver nanoparticles (AgNP) for surface-enhanced Raman spectroscopy (SERS) applications. The method utilizes the classical silver mirror reaction, with ascorbic acid serving as the reducing agent, to produce AgNP on polyacrylonitrile non-woven substrates. Different protocols were used to functionalize the substrates with AgNP <i>in situ</i>. Optimization of the substrate modification procedure led to SERS-substrates with an enhancement factor (EF) of up to 10<sup>6</sup> (4-MBA detection) for samples treated with 1 M solutions of AgNO<sub>3</sub> and NH<sub>3</sub>·H<sub>2</sub>O. The obtained substrates were further modified with glucose oxidase (GOx), and glucose sensing was carried out in a concentration range from 0.5 mM to 10 mM. Machine learning models, such as random forest and gradient boosting, were used for the analysis of Raman data. The resulting substrates have potential applications in various fields of analytical chemistry. Polyacrylonitrile ; Silver nanoparticle ; Surface modification ; Nanotechnology ; Materials science ; Nanoparticle ; Chemical engineering ; Polymer ; Composite material ; Engineering Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection