jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ka Lu | Panpan Zhou | Yong‐Qiang Tu | Fu‐Min Zhang | Xiao‐Ming Zhang | Kai Li | Kun Fang | Yunpeng Wang | Zi-Hao Li | Jiaqi Li
Source: Nature Communications (article, 2025-03-19)
Field: Chemistry — Organic Chemistry

In-depth insight into structure-reactivity/regioselectivity relationship of Lewis acid catalyzed cascade 4πe-cyclization/dicycloexpansion reaction The Lewis acid-catalyzed tittle reaction of 1,3-dicycloalkenlidine ketones is recognized as so far the shortest and most effective 1-step method for construction of angular tricyclic scaffolds, which are extensively found in bioactive terpenoids. Here, a further kinetic study of this reaction with 30 reaction examples is carried out using in situ IR technology and DFT calculation. That enables the creation of well-fitted linear relationships of lnk/(ΔG<sub>1</sub><sup>‡</sup>/T), ΔG<sub>1</sub><sup>‡</sup>/ΔG<sub>2</sub>, ln(k/k<sub>H</sub>)/σ<sub>p</sub>, reflecting the structure's effect on reactivity/selectivity, and validating the reaction mechanism. Particularly highlighted is that substituents C1-R<sup>1</sup>/C3-R<sup>2</sup> activate this reaction in the order: alkyl ≈ aryl >> aryl S-, halogen, alkyl O-, and alkyl N-. While electron-withdrawing R<sup>1</sup>/R<sup>2</sup> will inactivate this reaction. When R<sup>1</sup> = R<sup>2</sup> = Me and m = 4, the reactivity of n-membered substrates follow the order of ring's size: 3 > 4 > 7 > 6 > 5. Then, DFT calculations combined with machine learning algorithms establish a prediction model for first cycloexpansion (i e. regioselectivity). Electron-donating R<sup>1</sup>/R<sup>2</sup> can direct preferentially the first cycloexpansion of its near ring in the order: alkyl > aryl > halogen ≈ alkyl O- > alkyl N- > aryl S-, which can be fitted into the relationship as ΔΔG/(ΔΔG-R<sup>1</sup>, ΔΔG-R<sup>2</sup>, ΔΔG-m, ΔΔG-n) or ΔΔG/(m-rse, m-ra, n-rse, n-ra, R<sup>1</sup>-σ<sub>p</sub>, R<sup>2</sup>-σ<sub>p</sub>). When R<sup>1</sup> and R<sup>2</sup> show the similar electronic effect, the first cycloexpansion of m/n takes place in the order of ring's size: 4 > 5 > 6, 7, 8 > 3. Six examples are successfully validated by model prediction and then experiment. In this work, structure-reactivity relationship and regioselectivity predicting model are established. Regioselectivity ; Cascade ; Reactivity (psychology) ; Lewis acids and bases ; Catalysis ; Cascade reaction ; Chemistry ; Organic chemistry ; Medicine ; Chromatography Catalytic C–H Functionalization Methods ; Cyclopropane Reaction Mechanisms ; Catalytic Alkyne Reactions
Authors: C. A. Rivera-Romero | Elvia R. Palacios-Hernández | Jorge Muñoz‐Minjares | Osbaldo Vite-Chávez | Roberto Olivera-Reyna | Iván Alfonso Reyes-Portillo
Source: Biosystems Engineering (article, 2025-03-13)
Field: Chemistry — Analytical Chemistry

Recognition in the early stage of powdery mildew damage for cucurbits plants using spectral signatures Powdery mildew ; Stage (stratigraphy) ; Biology ; Developmental stage ; Botany ; Horticulture ; Psychology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote Sensing and Land Use
Authors: Peter Stockinger | Cornel Niederhauser | Sebastien Farnaud | Rebecca Buller
Source: Computational and Structural Biotechnology Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computational analysis reveals temperature-induced stabilization of FAST-PETase More than 10 % of global solid waste consists of poly(ethyleneterephthalate) (PET). Among other techniques, PET hydrolases (PETases) can be used to depolymerize this plastic. However, wildtype PETases exhibit poor specific activities and insufficient thermostability, limiting their use in depolymerization processes which require high temperatures. In 2022, machine learning-aided enzyme engineering of a PETase stemming from the bacterium <i>Ideonella sakaiensis</i> (<i>Is</i>PETase) resulted in a more functional, active, stable, and tolerant variant (FAST-PETase). To rationalize the molecular basis of FAST-PETase's improved thermal stability, we performed comparative Constraint Network Analysis (CNAnalysis) and Molecular Dynamics (MD) simulations of wildtype <i>Is</i>PETase (WT-PETase) and FAST-PETase at 30°C and 50°C identifying thermolabile sequence stretches in the wildtype enzyme. Further analysis of the backbone flexibility revealed that all mutations of FAST-PETase affected these critical regions. Counterintuitively, the <i>in-silico</i> analyses additionally highlighted that the flexibility of these regions decreased at 50°C in FAST-PETase, instead of exhibiting increased flexibility at higher temperature as would be expected from thermodynamic considerations. This effect was confirmed by physical energy calculations, which suggest that temperature-dependent conformational changes of FAST-PETase decrease the free energy of unfolding (ΔG(stability)) and rigidify the enzyme at elevated temperatures enhancing stability. Looking forward, these findings might help guide the rational engineering of protein thermostability and contribute to our understanding of the thermal adaptation of thermophilic enzymes. Computer science ; Chemistry ; Environmental science Enzyme Structure and Function ; Enzyme Catalysis and Immobilization ; Advanced Proteomics Techniques and Applications
Authors: Iqra Shahbaz | Lihong Li | Suriya Rehman | Muhammad Tahir | Zhiyu Yao | Sally Yuan | Zhongyuan Xiang | Yanlin Song
Source: Device (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Deep-learning-assisted tympanic-modeled lead-free rod-based piezoelectric transducer for acoustic sensing Piezoelectricity ; Acoustics ; Transducer ; Lead (geology) ; Materials science ; Geology ; Physics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Xinlei Gu | Yifan Shan | Wenyu Lu | Hao Pan | Shasha Huang | Xuepeng Xiang | Haijun Fu | Kan Zhang | Shijun Zhao
Source: Acta Materialia (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

A robust criterion for designing superhard high-entropy transition metal diborides Materials science ; Transition metal ; Entropy (arrow of time) ; Thermodynamics ; Statistical physics Diamond and Carbon-based Materials Research ; High Entropy Alloys Studies ; Fullerene Chemistry and Applications
Authors: Ehsan Fartash Naeimi | K. Selvi | İbrahim İnanç | N. Ungureanu
Source: Polymers (article, 2025-03-23)
Field: Materials Science — Polymers and Plastics

Mechanical and Thermal Behavior of Hemp-Reinforced Starch/Agar Biocomposites: Insights from Finite Element Simulation and Machine Learning Models The increasing effects of plastic pollution have led to the study of eco-friendly and biodegradable alternatives. The present study concerns the production and characterization of biocomposite films from starch, agar, and alkaline/peroxide-treated hemp fiber powder. In total, nine films were fabricated with variable ratios of starch-agar (0, 0.5, and 0.75) and hemp fiber (0, 15, and 30 wt.%). The physical, mechanical, and thermal properties of these films were evaluated. Tensile tests demonstrated that adding 30% hemp fiber increased Young's modulus, while 15% fiber decreased tensile strength. In the SAH group, adding 1.5 g of agar significantly improved tensile strength and Young's modulus, especially in the SAH30 sample. Finite element simulations of tensile tests showed remarkable agreement with experimental data. Machine learning models (SVM and GP) were used to predict tensile strength, with the SVM model using the RBF kernel showing the highest accuracy (R<sup>2</sup> = 0.938). Impact tests indicated that resistance was improved by agar, with the SAH group showing optimal stress distribution and energy absorption. Steady-state and transient thermal analyses showed that hemp fiber increased thermal resistance, and heat stress depended mainly on composition, especially agar and fiber. This research accentuates the potential of hemp fiber and agar to improve the properties of starch-based films and thereby opens routes toward sustainable material development. Starch ; Finite element method ; Materials science ; Composite material ; Thermal ; Agar ; Polymer science ; Mechanical engineering ; Structural engineering ; Engineering ; Thermodynamics ; Chemistry ; Food science ; Geology Natural Fiber Reinforced Composites ; Polysaccharides Composition and Applications ; biodegradable polymer synthesis and properties
Authors: Sotir Sotirov | Evdokia Sotirova | Rosen Dinkov | Dicho Stratiev | Ivelina Shishkova | Iliyan Kolev | Georgi Argirov | Georgi Georgiev | Veselina Bureva | Krassimir Atanassov | Radoslava Nikolova | Anife Veli | Svetoslav Nenov | Denis D. Stratiev | Svetlin Vasilev
Source: Fuels (article, 2025-06-04)
Field: Chemistry — Analytical Chemistry

Heavy Fuel Oil Quality Dependence on Blend Composition, Hydrocracker Conversion, and Petroleum Basket The production of very-low-sulfur residual fuel oil is a great challenge for modern petroleum refining because of the instability issues caused by blending incompatible relatively high-sulfur residual oils and ultra-low-sulfur light distillates. Another obstacle in the production of very-low-sulfur residual fuel oil using hydroprocessing technology is the contradiction of hydrodesulfurization with hydrodemetallization, as well as the hydrodeasphaltization functions of the catalytic system used. Therefore, the production of very-low-sulfur residual fuel oil by employing hydroprocessing could be achieved by finding an appropriate residual oil to be hydroprocessed and optimal operating conditions and by controlling catalyst system condition management. In the current study, data on the characteristics of 120 samples of heavy fuel oils produced regularly over a period of 10 years from a high-complexity refinery utilizing H–oil vacuum residue hydrocrackers in its processing scheme, the crude oils refined during their production, the recipes of the heavy fuel oils, and the level of H–oil vacuum residue conversion have been analyzed by using intercriteria and regression analyses. Artificial neural network models were developed to predict the characteristics of hydrocracked vacuum residues, the main component for the production of heavy fuel oil. It was found that stable very-low-sulfur residual fuel oil can be manufactured from crude oils whose sulfur content is no higher than 0.9 wt.% by using ebullated bed hydrocracking technology. The diluents used to reduce residue viscosity were highly aromatic FCC gas oils, and the hydrodemetallization rate was higher than 93%. Petroleum ; Composition (language) ; Environmental science ; Quality (philosophy) ; Petroleum engineering ; Chemistry ; Waste management ; Engineering ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Heat transfer and supercritical fluids ; Biodiesel Production and Applications
Authors: Jiashu Liang | Martin Head‐Gordon
Source: Journal of Chemical Theory and Computation (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Gold-Standard Chemical Database 137 (GSCDB137): A Diverse Set of Accurate Energy Differences for Assessing and Developing Density Functionals We present GSCDB137, a rigorously curated benchmark library of 137 data sets (8377 entries) covering main-group and transition-metal reaction energies and barrier heights, (intra- and intermolecular) noncovalent interactions, dipole moments, polarizabilities, electric-field response energies, and vibrational frequencies. Legacy data from GMTKN55 and MGCDB84 have been updated to today's best reference values; redundant or low-quality points were removed, and many new, property-focused sets were added. Testing 29 popular density functional approximations (DFAs) confirms the expected Jacob's-ladder hierarchy overall but also reveals notable exceptions: functional performance for frequencies and electric-field properties correlates poorly with that for other ground-state energetics. ωB97M-V and ωB97X-V are the most balanced hybrid meta-GGA and hybrid GGA, respectively; B97M-V and revPBE-D4 lead the meta-GGA and GGA classes. Double hybrids lower mean errors by about 30% versus their hybrid analogues but demand careful frozen-core, basis set, and spin contamination treatment. GSCDB137 offers a comprehensive, openly documented platform for rigorous validation of DFA and universal machine learning potentials, and training of the next generation of exchange-correlation functionals. Benchmark (surveying) ; Set (abstract data type) ; Hierarchy ; Basis (linear algebra) ; Data set ; Hybrid functional ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Pei Song | Zhikai Gao | Tiren Peng | Zhiguo Wang | Sen Lu | Zepeng Jia | Zishan Luo | Hong Cui | Weizhi Tian | Rong Feng | Lingxia Jin | Hongkuan Yuan
Source: Applied Surface Science (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Single-atom dissolution at the MN4/MXene interface and electric field-driven adsorption mechanisms: Unraveling catalytic descriptors using machine learning Adsorption ; Dissolution ; Atom (system on chip) ; Electric field ; Catalysis ; Interface (matter) ; Materials science ; Field (mathematics) ; Chemical physics ; Chemistry ; Physical chemistry ; Computer science ; Physics ; Quantum mechanics ; Organic chemistry ; Mathematics ; Parallel computing MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Nanomaterials for catalytic reactions
Authors: Xiaowen Xu
Source: Trends in Food Science & Technology (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

pH-Responsive fluorescent hydrogels for food anticounterfeiting, freshness monitoring and preservation: Mechanisms, fabrication strategies, and emerging applications Self-healing hydrogels ; Food spoilage ; Fluorescence ; Food industry ; Food safety Carbon and Quantum Dots Applications ; Hydrogels: synthesis, properties, applications ; Nanocomposite Films for Food Packaging
Authors: Janine Lässer | Doris E. Braun
Source: Crystal Growth & Design (article, 2025-02-14)
Field: Materials Science — Materials Chemistry

Exploring Coformer Substitution in Cocrystallization: Griseofulvin and Phenol Derivatives This study investigates the cocrystallization of griseofulvin with phenolic coformers, highlighting its feasibility and variability. In addition to the previously reported cocrystal of griseofulvin with 4-t-butylphenol (1:1), the experimental screening identified three new cocrystals: with phenol (2:5), 4-t-amylphenol (1:1), and 2,4,6-trichlorophenol (2:3). Phenols with carbon substituents in the ortho or meta positions failed to form cocrystals, likely due to steric hindrance and electron-donating effects. In contrast, phenols with chlorine substituents, particularly in the ortho and para positions, demonstrated enhanced cocrystallization potential, driven by the electron-withdrawing effects that promote hydrogen bonding. The 2:5 phenol cocrystal required optimized conditions for isolation and exhibited instability under ambient conditions due to coformer sublimation, a tendency also observed for the other cocrystals. While challenging, sublimation facilitated the determination of stoichiometric ratios, which varied from 1:1 to 2:3 and 2:5. Furthermore, this study provides a data set of cocrystal-forming and noncocrystal-forming combinations as a rigorous test case for virtual cocrystal prediction. Among the tested methods, crystal structure prediction proved the most reliable, identifying all observed cocrystal combinations and, together with powder X-ray diffraction, offering insights into the experimental coformer and cocrystal structures. Future integration of CSP with machine learning could accelerate prediction speed and accommodate a broader range of stoichiometric ratios. Overall, this work highlights the complexity and potential of cocrystallization. Griseofulvin ; Substitution (logic) ; Phenol ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Computer science Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Cultural Heritage Materials Analysis
Authors: Yingbo He | X. Mao | Yonder Berencén | Jiahao Gao | Yanhong Long | Qi Sun | Yuan Pan | Qunrui Deng | С Ран | Dongxiang Luo | Xinke Liu | Tao Zheng | Duan-Yang Liu | Wei Gao
Source: Advanced Functional Materials (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Bidirectional Self‐Powered Photoresponse in GeS <sub>x</sub> Se <sub>1‐x</sub> /WSe <sub>2</sub> van der Waals Heterostructures for Advanced Neuromorphic Vision Systems Abstract Bidirectional optoelectronic devices, capable of emulating the neurobiological functions of retinal neurons, hold great promise for artificial neuromorphic vision systems. However, a systematic approach to achieving a wide range of modulation of bidirectional photoresponse properties remains unexplored. Here, the modulation of bidirectional photoresponse characteristics in 2D GeS x Se 1‐x /WSe 2 (0 ≤ x ≤ 1) van der Waals heterostructures (vdWHs) through alloy engineering is demonstrated. In GeS 0.2 Se 0.8 /WSe 2 vdWHs, a highly reconfigurable responsivity ranging from 289.84 to −934.11 mA W −1 with bidirectional photoresponse is achieved—nearly two orders of magnitude greater than that of binary GeSe/WSe 2 vdWH (0.9 to −17.34 mA W −1 ). Additionally, the GeS 0.2 Se 0.8 /WSe 2 device exhibits a linearly gate‐driven bidirectional responsivity within an optimal voltage window of 20 V. This tunability in polarity photoresponse performance is attributed to the band alignment‐induced band offset evolution and Fermi level variations driven by changes in sulfur content. Furthermore, when integrated with spiking neural networks, the GeS 0.2 Se 0.8 /WSe 2 vdWH device enables dynamic vision system simulations, achieving exceptional motion perception with a recognition accuracy of ≈99%. This advancement in reconfigurable bidirectional optoelectronic devices paves the way for high‐accuracy and high‐efficiency neuromorphic vision sensors. Neuromorphic engineering ; Responsivity ; Heterojunction ; Modulation (music) ; Ranging ; van der Waals force ; Memristor ; Biasing 2D Materials and Applications ; Advanced Memory and Neural Computing ; Photoreceptor and optogenetics research
Authors: Moses M. Solomon | Ubani Oluwaseun Amune | Xiaomeng He | Hairun Wang | Jun Guang He | Di Hu | Fiseha Berhanu Tesema | Hainam Do | Аbdelkаrim Аit Mаnsour | R. Salghi | Savıour A. Umoren
Source: Carbon (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Combined experimental, theoretical, &amp; machine learning studies of anticorrosion properties of hydroxyethyl cellulose, l-glutamic acid, &amp; potassium acesulfame-derived carbon dots Potassium ; Cellulose ; Chemistry ; Materials science ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Engineering Corrosion Behavior and Inhibition ; ZnO doping and properties ; Anodic Oxide Films and Nanostructures
Authors: Sungmin Jeong | S.-J. Cho | Suyong Lee
Source: LWT (article, 2025-01-20)
Field: Chemistry — Analytical Chemistry

Integrative hyperspectral imaging and artificial intelligence approaches for identifying sucrose substitutes and assessing cookie qualities This study employed a combined approach utilizing hyperspectral imaging and artificial intelligence to identify sugar substitutes (allulose and kestose) and predict the quality characteristics of cookies formulated with these substitutes. When the hyperspectral data of sweeteners were applied to four machine learning models, the classification performance was enhanced when sweeteners were in powdered form and in cookies, compared to when they were in solution and cookie dough. Especially, the stochastic gradient descent model demonstrated noticeable performance regardless of the sample form. Furthermore, the confusion matrix revealed a tendency to classify sucrose/kestose and fructose/allulose, which have relatively similar molecular structures, suggesting the potential of hyperspectral analysis to recognize structural differences in sugars within complex food matrices. To predict the quality characteristics (spread factor and hardness) of cookies formulated with sugar substitutes, the hyperspectral data of sweeteners in solution were subjected to a multilayer perceptron model of which performance was evaluated under nine individual and combined hyperparameter conditions. The results indicated that the model performance was optimized under conditions of three hidden layers and a sigmoid activation function. Notably, the model demonstrated excellent predictive performance, achieving accuracy levels exceeding 0.93 for both spread factor and hardness. • AI combined with hyperspectral imaging (HSI) classified sweeteners in various forms. • Hyperspectral signature was weakly identified in the solution form. • Classification performance increased in SGD and SVM models after optimization. • Nine hyperparameters were reviewed to predict cookie qualities from solution. • Grid search technique enabled precise prediction of cookie qualities (R 2 > 0.93). Hyperspectral imaging ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sung Wook Moon | Soohaeng Yoo Willow | Tae Park | Seung Kyu Min | Chang Woo Myung
Source: Journal of Chemical Theory and Computation (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed quantum-classical (MQC) methods, such as trajectory surface hopping (TSH), and their capacity to model correlated electron-nuclear dynamics efficiently, difficulties persist in managing nonadiabatic dynamics. Specifically, singularities at CIs and double-valued coupling elements result in discontinuities that disrupt the smoothness of predictive functions. Partial solutions have been provided by learning diabatic Hamiltonians with phaseless loss functions to these challenges. However, a definitive method for addressing the discontinuities caused by CIs and double-valued coupling elements has yet to be developed. Here, we introduce the phaseless coupling term, Δ<sup>2</sup>, derived from the square of the off-diagonal elements of the diabatic Hamiltonian in the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS, briefly SSR)(2,2) formalism. This approach improves the stability and accuracy of the MLP model by addressing the issues arising from CI singularities and double-valued coupling functions. We apply this method to the penta-2,4-dieniminium cation (PSB3), demonstrating its effectiveness in improving MLP training for ML-based nonadiabatic dynamics. Our results show that the Δ<sup>2</sup>-based ML-ESMD method can reproduce ab initio ESMD simulations, underscoring its potential and efficiency for broader applications, particularly in large-scale and long-time scale ESMD simulations. Degrees of freedom (physics and chemistry) ; State (computer science) ; Spin (aerodynamics) ; Physics ; Phase (matter) ; Dynamics (music) ; Statistical physics ; Quantum mechanics ; Computer science ; Algorithm ; Thermodynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality