jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiongyu Wang | Yuxuan Liao | Ting Xie | Ruixi Chen | Jennifer C. Lai | Zhimin Zhang | LU Hong-mei
Source: Journal of Chemometrics (article, 2025-07-01)
Field: Chemistry — Spectroscopy

Accurate and Rational Collision Cross Section Prediction Using Voxel‐Projected Area and Deep Learning ABSTRACT Ion mobility spectrometry–mass spectrometry (IMS‐MS) enables rapid acquisition of collision cross section (CCS), a critical physicochemical property for analyte characterization. Despite CCS being theoretically defined as the rotationally averaged projected area of 3D atomic spheres, existing models have underutilized this geometric insight. Here, we present a projected area–based CCS prediction method (PACCS). It integrates voxel‐projected area approximation, graph neural network (GNN)–extracted features, and m/z to achieve accurate and rational CCS prediction. A voxel‐based algorithm efficiently calculates molecular projected areas by leveraging Fibonacci grids sampling and discretizing 3D conformers into voxel grids. PACCS demonstrates exceptional performance, achieving a median relative error (MedRE) of 1.03% and a coefficient of determination ( R 2 ) of 0.994 on the test set. External test set against AllCCS2, GraphCCS, SigmaCCS, CCSbase, and DeepCCS highlights the superiority of PACCS, with 80.1% of predictions exhibiting &lt; 3% error. Notably, PACCS exhibits broad applicability across diverse molecular types, including environmental contaminants ( R 2 = 0.954–0.979) and structurally complex phycotoxins ( R 2 = 0.961), highlighting the superiority of PACCS in robustness and versatility. Computational efficiency is enhanced via parallelization, enabling large‐scale CCS database generation (e.g., 5.9 million entries for ChEMBL within 10 h). Ablation studies confirm the pivotal role of voxel‐projected areas (Pearson correlation coefficients &gt; 0.988), while stability analyses reveal minimal sensitivity to conformational variability (standard deviation of R 2 is 0.00003). PACCS provides an open‐source, scalable solution for expanding CCS databases, advancing compound identification in metabolomics and environmental analysis. Computer science ; Scalability ; Voxel ; Robustness (evolution) ; Ion-mobility spectrometry ; Algorithm ; Test set ; Artificial intelligence ; Mass spectrometry ; Chemistry ; Database Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Claude Cunningham | Susan Ortner
Source: Journal of Nuclear Materials (review, 2025-07-15)
Field: Materials Science — Materials Chemistry

A Critical Review of Machine Learning Analyses of Reactor Pressure Vessel Embrittlement and Hardening Embrittlement ; Reactor pressure vessel ; Hardening (computing) ; Pressure vessel ; Materials science ; Metallurgy ; Nuclear engineering ; Composite material ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yikelamu Jilili | Jiang Wang | Xiang Qin | Yumiao Ma | Weijun Zhen | Chunyun Guo | Wei Xu | Long You | Li Wang
Source: Langmuir (article, 2025-01-01)
Field: Materials Science — Biomaterials

Natural Degradation Behavior of Poly(lactic acid) Nanocomposite Films and the Adsorption Behavior of Degraded Products on Cu(II) In this study, the degradation behavior of poly(lactic acid) nanocomposite films (PLA/Hec-g@PS) under extreme natural environments was investigated, and the degraded PLA based films were applied to adsorb Cu(II). During the early and midstages of degradation, the surface roughness and crack propagation rate of PLA/Hec-g@PS films were significantly lower than those of PLA films. This could be due to the fact that Hec-g@PS enhanced the interaction forces between C-O-C + CH<sub>3</sub> and C═O in the PLA chains, thereby mitigating the degradation of PLA. Neural network predictions indicated that the molecular weight of PLA films decreased to 30% after 1344 days, whereas PLA/Hec-g@PS films reached the same reduction in 1451 days, extending the lifespan of PLA by 1.08 times. The environmental impact of PLA/Hec-g@PS was further assessed by their adsorption behavior toward Cu(II). It was found that PLA films adsorbed 244.51 μg/g of Cu(II), while PLA/Hec-g@PS films adsorbed 372.63 μg/g of Cu(II). The isotherm adsorption model showed that the maximum adsorption capacities (<i>q</i><sub>m</sub>) of PLA and PLA/Hec-g@PS were 326.60 μg/g and 441.51 μg/g, respectively. This improvement offers PLA based films new possibilities for applications in wastewater treatment and soil remediation. Nanocomposite ; Degradation (telecommunications) ; Adsorption ; Chemical engineering ; Materials science ; Chemistry ; Lactic acid ; Polymer chemistry ; Composite material ; Organic chemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Bone Tissue Engineering Materials
Authors: F. Quispe-Huaynasi | F. Roig | N. Holanda | V. Loaiza-Tacuri | Romualdo Eleutério | C. B. Pereira | S. Daflon | Vinicius M. Placco | R. Lopes de Oliveira | Federico Sestito | P. K. Humire | M. Borges Fernandes | A. Kanaan | Claudia Mendes de Oliveira | T. Ribeiro | W. Schoenell
Source: The Astronomical Journal (article, 2025-05-23)
Field: Chemistry — Analytical Chemistry

An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data Abstract Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify candidates for spectroscopic follow-up using data from the Southern Photometric Local Universe Survey (S-PLUS). First, using an anomaly detection technique based on an autoencoder model, we select a large sample of objects (∼19,000) whose Spectral Energy Distribution is not well reconstructed by the model after training it on a well-behaved star sample. Then, we apply the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to the 66 color measurements from S-PLUS, complemented by information from the SIMBAD database, to identify stellar populations. Our analysis reveals 69 carbon-rich star candidates that, based on their spatial and kinematic characteristics, may belong to the CH or carbon-enhanced metal-poor categories. Among these chemically peculiar candidates, we identify four as likely carbon dwarf stars. We show that it is feasible to identify three primary white-dwarf (WD) populations: WDs with hydrogen-dominated atmospheres, WDs with neutral helium-dominated atmospheres, and the WDs main sequence binaries (WD + MS). Furthermore, by using eROSITA X-ray data, we also highlight the identification of candidates for very active low-mass stars. Finally, we identified a large number of binary systems using the autoencoder model, but did not observe a clear association between the overdensities in the t-SNE map and their orbital properties. Physics ; Outlier ; Unsupervised learning ; Spectral energy distribution ; Energy (signal processing) ; Distribution (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Astronomy ; Spectral line ; Computer science ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Protein Structure and Dynamics
Authors: Sourav Misra | Dhritiman Saha | Sanjoy Debnath | Ranjeet Singh
Source: Microchemical Journal (article, 2025-07-29)
Field: Chemistry — Analytical Chemistry

Identification and classification of blended fibres using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometric analysis Attenuated total reflection ; Fourier transform infrared spectroscopy ; Infrared ; Fourier transform ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Infrared spectroscopy ; Fourier transform spectroscopy ; Diffuse reflectance infrared fourier transform ; Chemistry ; Optics ; Mathematics ; Chromatography ; Physics ; Organic chemistry ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Industrial Vision Systems and Defect Detection
Authors: Sajid Imran | Wenhao Wang | Yuan Gao | Jun Zhu
Source: Organic & Biomolecular Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

NO-modulated triplet ground state and two-state antiaromaticity in BN-doped cyclobutadienes: a combined DFT and machine learning study Controlling aromaticity across electronic states is crucial for designing novel species. While aromaticity typically could be achieved in either the lowest singlet state (S<sub>0</sub>) or the lowest triplet state (T<sub>1</sub>), dual-state aromaticity or antiaromaticity remains less developed. Herein, we demonstrate that NO-substitution uniquely induces antiaromaticity in both S<sub>0</sub> and T<sub>1</sub> states of 1,2-BN-doped cyclobutadiene (1,2-BN-CBD), initially nonaromatic in S<sub>0</sub> and weakly aromatic in T<sub>1</sub>. Unlike attachment to nitrogen or carbon, NO bonding to boron (2) induces two-state antiaromaticity, as confirmed by Nucleus-Independent Chemical Shift (NICS), Electron Localization Function (ELF<sub>π</sub>), NICS-grid, and Isomerization Stabilization Energy (ISE) analyses. Furthermore, compounds with NO at boron (2 and 10) exhibit triplet ground states. Spin density mainly localizes on NO, driving antiaromaticity in T<sub>1</sub>. Principal Interaction Orbital (PIO) and Principal Interacting Spin Orbital (PISO) analyses reveal that exocyclic BN double bond formation enforces planarization and enables localization in the S<sub>0</sub> and T<sub>1</sub> states, leading to two-state antiaromaticity in 2. K-means clustering combined with principal component analysis (one of the most commonly used unsupervised machine learning algorithms) classified BN-doped CBDs based on their electronic and structural properties, uniquely isolating 2 due to its distinct substituent positions and aromaticity behaviors. These findings highlight an important role of the substituent position in tuning electronic and aromatic properties. Antiaromaticity ; Aromaticity ; Substituent ; Singlet state ; Ground state ; Density functional theory ; Cyclobutadiene ; Triplet state ; Electronic structure Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Fullerene Chemistry and Applications
Authors: Bin Yuan | Chen Zhang | Chaoqun Ji | Gefeng Liu | Xiaoyi Li | Shanshan Gong | Xiangsheng Huang | Aijin Shen | Xiaonong Li | Yanfang Liu
Source: Analytical Chemistry (article, 2025-02-05)
Field: Chemistry — Spectroscopy

HSQCid: A Powerful Tool for Paving the Way to High-Throughput Structural Dereplication of Natural Products Based on Fast NMR Experiments Structural dereplication is an essential step in the study of natural products (NPs). The number of discovered NPs is so large that efficient dereplication is highly desirable. NMR spectroscopy is still the gold standard for structural identification. <sup>13</sup>C NMR spectrum is an effective molecular fingerprint, but their acquisition is time-consuming, especially for mass-limited NPs. Several alternative methods or tools have been proposed but have never reached general use for some reasons. Here, a new artificial intelligence tool, HSQCid, using contrastive learning between <sup>1</sup>H-<sup>13</sup>C HSQC spectra and structures, is proposed for effective structural identification. Two structure encoders are compared, and the graph neural network is preferred over the Transformer. In this way, 80% and 20% of about 400,000 predicted data could be used for training and testing, respectively. Besides, with 17,971 experimental data as external test data, the top-1 and top-10 accuracies reach 74.5% and 94.8%, respectively. Top-1 accuracy increases by at least 12% when combined with other easily obtainable structure features, such as the total number of hydrogens connected to carbons from <sup>1</sup>H NMR spectra. Further data analysis shows that the filters by structure features nearly eliminate the influence (>10%) of the difference between predicted and experimental data. Surprisingly, the influence of the number or the ratio of nonprotonated carbons on the identification accuracy is only significant in specific and rare cases (2.65%). Furthermore, the benchmark method, which matches <sup>13</sup>C peaks, is compared and is markedly inferior to the proposed method, with or without structural features. The HSQCid code is available online. It is believed that HSQCid contributes to paving the way for high-throughput or highly effective structural dereplication of NPs. Chemistry ; Biological system ; Heteronuclear single quantum coherence spectroscopy ; Nuclear magnetic resonance spectroscopy ; Stereochemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced NMR Techniques and Applications
Authors: Kateřina Plachká | Veronika Pilařová | Taťána Gazárková | František Švec | Jean‐Christophe Garrigues | Lucie Nováková
Source: Analytical Chemistry (article, 2025-01-21)
Field: Chemistry — Spectroscopy

AI-Enhanced Understanding of Retention Interactions in Supercritical Fluid Chromatography: Neural Network Insights into Retention on Selected Non-Polar Stationary Phases The retention behavior in supercritical fluid chromatography (SFC) remains a complex and poorly understood phenomenon despite the development of various models to explain retention mechanisms. This study aims to deepen the understanding of retention by investigating three distinct stationary phases: high-strength silica octadecyl (HSS C18 SB), charged surface hybrid pentafluorophenyl (CSH PFP), and porous graphitic carbon (PGC) as a nonsilica-based phase. Three mobile phase compositions, i.e., CO<sub>2</sub>/methanol, CO<sub>2</sub>/methanol +10 mmol/L NH<sub>3</sub>, and CO<sub>2</sub>/methanol +2% H<sub>2</sub>O, were investigated using an extensive set of analytes characterized by over 200 molecular descriptors. Artificial neural networks were employed to analyze the influence of these descriptors on retention behavior, revealing the most significant molecular features that increase or decrease retention on each column with the three different mobile phases. This complex evaluation of the large set of experimental data enabled to link specific analyte properties to retention interactions in SFC, including the interaction of analytes with partial positive charge with silanol groups on the HSS C18 SB column when using methanol + H<sub>2</sub>O as the organic modifier. The flexibility of the alkyl chain in the HSS C18 SB column is also affected by the composition of the organic modifier, which alters retention mechanisms, especially when NH<sub>3</sub> is used as an additive. This highlights the critical role of the mobile phase composition in modulating the behavior of nonpolar stationary phases. Completely different interaction mechanisms were observed for the PGC column when comparing methanol with and without additives, suggesting possible modifications to the planar structure and surface polarizability of the PGC phase. Statistical evaluation of data collected over a year of column usage demonstrated distinct long-term retention stability trends. The HSS C18 SB column exhibited the greatest stability with methanol + H<sub>2</sub>O, whereas significant retention decreases were observed with methanol + NH<sub>3</sub> modifier, particularly for CSH PFP and, unexpectedly, also for PGC. These findings provide crucial insights into the long-term retention behavior and aging of SFC columns, with practical implications for optimizing SFC conditions and improving column lifetime. Chemistry ; Silanol ; Supercritical fluid chromatography ; Methanol ; Supercritical fluid ; Analyte ; Chromatography ; Phase (matter) ; Column chromatography ; Alkyl ; Hydrophilic interaction chromatography ; High-performance liquid chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Advanced Chemical Sensor Technologies
Authors: Shingo Urata | Federica Lodesani
Source: Materialia (article, 2025-07-07)
Field: Materials Science — Ceramics and Composites

Certification of boroxol ring population in vitreous B<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si133.svg" display="inline" id="d1e376"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>O<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si134.svg" display="inline" id="d1e384"><mml:msub><mml:mrow/><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math> glass using theoretical simulations Materials science ; Scalable Vector Graphics ; Population ; Ring (chemistry) ; Computer graphics (images) ; Computer science ; World Wide Web ; Organic chemistry ; Chemistry ; Demography Glass properties and applications ; Catalysis and Oxidation Reactions ; Phase-change materials and chalcogenides
Authors: Rohan S. Adhikari | Sri Vishnuvardhan Reddy Akepati | Matthew R. Carbone | Asritha Polu | Hyeong Jin Kim | Yugang Zhang | Arthi Jayaraman
Source: Journal of Applied Crystallography (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE We use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes ( e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting `strong', `weak' and `no' features. Diminishing features ( e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model ( e.g. the lmfit analytical model for polydisperse spheres) to these `weak' and `no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution ( e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles. Small-angle X-ray scattering ; Scattering ; Dispersity ; Small-angle scattering ; Materials science ; SPHERES ; Nanoparticle ; Characterization (materials science) ; Biological system ; Particle (ecology) ; Statistical physics ; Optics ; Physics ; Nanotechnology ; Polymer chemistry X-ray Diffraction in Crystallography ; Soil and Unsaturated Flow ; Machine Learning in Materials Science
Authors: Jiahui Zhang | J. Zhao | Jesper Byggmästar | Erkka J. Frankberg | A. Kuronen
Source: Scientific Reports (article, 2025-03-19)
Field: Materials Science — Materials Chemistry

Large-scale atomistic study of plasticity in amorphous gallium oxide with ab-initio accuracy Compared to the widely investigated crystalline polymorphs of gallium oxide ([Formula: see text]), knowledge about its amorphous state is very limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate the formation and plastic behavior of amorphous [Formula: see text] (a-[Formula: see text]). Amorphization of gallium oxide melt is successfully observed at ultrahigh cooling rates, including a distinct glass transition. The glass transition temperature is evaluated to range from 1234 to 1348 K at different cooling rates. Structural analysis shows similarities between a-[Formula: see text] and amorphous alumina (a-[Formula: see text]) in many aspects, including pair distribution function, coordination distribution, and bond angle distribution. In the tension simulations, highly plastic behavior at room temperature is observed, highly comparable to a-[Formula: see text]. Based on multiple quantitative characterization results, we show that a-[Formula: see text] exhibits a higher nucleation rate of localized plastic strain events compared to a-[Formula: see text], which can increase the material's resistance to shear banding formation during deformation. Amorphous solid ; Materials science ; Nucleation ; Ab initio ; Amorphous metal ; Glass transition ; Plasticity ; Atomic units ; Gallium ; Length scale ; Oxide ; Coordination number ; Crystallography ; Thermodynamics ; Physics ; Chemistry ; Metallurgy ; Composite material ; Polymer Material Dynamics and Properties ; Machine Learning in Materials Science ; Thermal properties of materials