jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksandar Kondinski | A. Oyarzun | Simon D. Rihm | Jiaru Bai | Sebastian Mosbach | Jethro Akroyd | Markus Kraft
Source: European Journal of Inorganic Chemistry (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Automated Assembly Modeling of Metal–Organic Polyhedra Assembly modeling has been achieved in knowledge AI systems for the automated inference of new and rational metal–organic polyhedra (J. Am. Chem. Soc. 2022, 144, 26, 11713–11728). This work presents an algorithm and data structure that extends the process of assembly modeling to the automated generation of structural information about metal–organic polyhedra, enabling automation of computational approaches to analyse trends in cavity and pore sizing. Distinct from string‐based tools for purely organic cages, the workflow positions inorganic, organic, and hybrid chemical building units directly in 3‐D and outputs geometries suitable for higher‐level geometry optimization calculations in one step. The structural geometries obtained from this work are semantically integrated as part of The World Avatar, a dynamic knowledge ecosystem. Polyhedron ; Workflow ; Chemistry ; Automation ; Process (computing) ; Computational science ; Computer science ; Database ; Mechanical engineering ; Geometry ; Programming language ; Engineering Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Dola Sundeep | Eswaramoorthy K Varadharaj | C. Chandrasekhara Sastry
Source: Scientific Reports (article, 2025-06-06)
Field: Materials Science — Polymers and Plastics

Fabrication of industrial grade F/g-C3N4 reinforced banana natural Fiber sustainable PCB substrates The rapid growth of the electronics industry has led to a significant environmental impact due to conventional printed circuit boards (PCBs) which contribute heavily to electronic waste. Addressing this issue requires sustainable alternatives that can replace traditional PCB materials without compromising performance. In this study we propose a novel sustainable PCB using banana fiber-reinforced natural fiber composite as the prepreg material combined with biodegradable resin engineered to meet IPC 4101 A/24 standards for fabrication of PCB laminates. The proposed composite exhibits enhanced thermal, mechanical, and electrical properties suitable for low-power applications validated through rigorous testing. To improve the mechanical, thermal and electrical properties we incorporated the 4-bromobenzonitrile functionalized graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>) nanoparticles into the banana composite. This reinforcement has shown a significant augment in flame resistance, dielectric constant and mechanical properties including tensile, flexural, and impact resistance which ensured durability for practical applications. A prototype of the fabricated sustainable PCB substrate was tested with a full-wave rectifier circuit demonstrating effective performance and suggesting its viability as a sustainable replacement for conventional plastic-based FR-1 PCBs in low-power circuits aligning with circular economy principles. Fabrication ; Fiber ; Dietary fiber ; Natural fiber ; Food science ; Materials science ; Composite material ; Chemistry ; Medicine Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Yaning Xie | Weiju Dai | Jingyi Yan | Zuhao Wang | Chao Tang
Source: Materials (article, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Simulation of the Thermodynamic Properties and Hydrophobicity of Polydimethylsiloxane Modified by Grafting Nano-SiO2 with Different Silane Coupling Agents Polydimethylsiloxane (PDMS) with good hydrophobicity and nano-SiO<sub>2</sub> with excellent thermal stability and mechanical properties are used as a composite coating for cellulose insulating paper in oil-immersed transformers, which effectively reduces the moisture generated by the thermal aging process, thus prolonging each transformer's service life. This study employed molecular dynamics simulations to investigate the effects of surface-modified nano-SiO<sub>2</sub> with different silane coupling agents (KH570 and KH151) on the thermodynamic properties and hydrophobicity of PDMS. Four groups of anhydrous models were constructed, namely, PDMS, P-SiO<sub>2</sub>, P-570, and P-151, as well as four corresponding groups of water-containing models: PDMS/H<sub>2</sub>O, P-SiO<sub>2</sub>/H<sub>2</sub>O, P-570/H<sub>2</sub>O, and P-151/H<sub>2</sub>O. The results demonstrate that incorporating silane-coupled nano-SiO<sub>2</sub> into PDMS enhances mechanical properties, <i>FFV</i>, <i>CED</i>, <i>MSD</i>, diffusion coefficient, interaction energy, and hydrogen bond count, with KH570-grafted composites exhibiting optimal thermomechanical performance and hydrophobicity. At a temperature of 343 K, KH570 modification increased the bulk modulus and <i>CED</i> by 26.5% and 31.0%, respectively, while reducing the water molecular diffusion coefficient by 24.7% compared to that of unmodified PDMS/SiO<sub>2</sub> composites. The extended KH570 chains occupy additional free volume, forming a larger steric hindrance layer, restricting molecular chain mobility, suppressing hydrogen bond formation, and establishing a low energy surface. Polydimethylsiloxane ; Grafting ; Materials science ; Nano- ; Silane ; Coupling (piping) ; Chemical engineering ; Polymer chemistry ; Nanotechnology ; Composite material ; Polymer ; Engineering Polymer Nanocomposites and Properties ; Surface Modification and Superhydrophobicity ; Silicone and Siloxane Chemistry
Authors: Y. Suzuki | Shin Usuki | M Nishizawa | Noritake Tanaka | Yoshitomo Suhara | Ichiro Yajima
Source: Anticancer Research (article, 2025-05-27)
Field: Chemistry — Organic Chemistry

Acyclic Retinoid Overcomes Vemurafenib Resistance in Melanoma Cells<i>via</i>Dual Inhibition of MAPK and PI3K/AKT/mTOR Pathways ACR exhibited potent anticancer effects on <i>BRAF</i> <sup>V600E</sup>-mutant and vemurafenib-resistant melanoma cells by dual-targeting of MAPK and PI3K/AKT/mTOR pathways, indicating its potential as a novel therapeutic agent for melanoma treatment. PI3K/AKT/mTOR pathway ; Vemurafenib ; Protein kinase B ; Cancer research ; MAPK/ERK pathway ; Retinoid ; Melanoma ; Medicine ; Chemistry ; Retinoic acid ; Biology ; Signal transduction ; Cell biology ; Metastatic melanoma ; Cell culture ; Genetics Synthesis and biological activity ; Melanoma and MAPK Pathways ; Retinoids in leukemia and cellular processes
Authors: Vera V. Olicheva | V. L. Beloborodov | Seyed Davood Sharifi | Anna Dubrovskaya | A. K. Zhevlakova | I. A. Selivanova | Igor R. Ilyasov
Source: International Journal of Molecular Sciences (article, 2025-06-13)
Field: Chemistry — Organic Chemistry

Dihydroquercetin and Related Flavonoids in Antioxidant Formulations with α-Tocopherol The concomitant utilization of flavonoids and α-tocopherol has the potential to establish a comprehensive antioxidant system that operates in both hydrophilic and lipophilic environments. The objective of this study was to examine the antioxidant interactions between dihydroquercetin, a flavonoid that has shown promise in various studies, as well as structurally related flavonoids, and α-tocopherol in various ratios. The antioxidant capacity was assessed using two ABTS<sup>•+</sup> radical-cation inhibition assays: a decolorization assay and a lag-time assay. The results of this study indicated that formulations of dihydroquercetin, quercetin, rutin, or morin with α-tocopherol exhibited additive or mildly subadditive interactions, independent of their ratios. A two-phase pattern was exhibited by the lag-time data, which, in comparison with individual components, allowed us to suggest that α-tocopherol appeared to dominate the initial phase of radical scavenging, while flavonoids became active in the later phase. This finding indicates that α-tocopherol may play a role in protecting flavonoids from premature oxidation in alcoholic media. The findings could prove valuable for the rational design of antioxidant formulations in the nutraceutical, cosmeceutical, and pharmaceutical industries. Additionally, the two-stage antioxidant behavior offers prospects for the formulation of straightforward, cost-effective analytical approaches to measure components in binary antioxidant formulations. Antioxidant ; Chemistry ; Rutin ; ABTS ; Morin ; Flavonoid ; Tocopherol ; Quercetin ; Food science ; Cosmeceutical ; Biochemistry ; Vitamin E ; Biology ; DPPH ; Biotechnology ; Medicine Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Antioxidant Activity and Oxidative Stress
Authors: Paris Christodoulou | Eftichia Kritsi | Georgia Ladika | Panagiota Tsafou | Konstantinos Tsiantas | Thalia Tsiaka | Panagiotis Zoumpoulakis | Dionisis Cavouras | Vassilia J. Sinanoglou
Source: Applied Sciences (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Estimation of Tomato Quality During Storage by Means of Image Analysis, Instrumental Analytical Methods, and Statistical Approaches The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R2 values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Postharvest ; Hue ; Orange (colour) ; Food science ; Environmental science ; Mathematics ; Chemistry ; Artificial intelligence ; Horticulture ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Leaf Properties and Growth Measurement
Authors: Tahereh Nematiaram | Zenon Lamprou | Yashar Moshfeghi
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach The two-dimensionality (2D) of charge transport significantly affects charge carrier mobility in organic semiconductors, making it a key target for materials discovery and design. Traditional quantum-chemical methods for calculating 2D are resource-intensive, especially for large-scale screening, as they require computing charge transfer integrals for all unique pairs of interacting molecules. We explore the potential of machine learning models to predict whether this parameter will fall within a desirable range without performing any quantum-chemical calculations. Using a large database of molecular semiconductors with known 2D values, we evaluate various machine-learning models using chemical and geometrical descriptors. Our findings demonstrate that the LightGBM outperforms others, achieving 95% accuracy in predictions. These results are expected to facilitate the systematic identification of high-mobility molecular semiconductors. Drug discovery ; Computer science ; Semiconductor ; Materials science ; Nanotechnology ; Chemistry ; Optoelectronics ; Biochemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Zhihong Wu | Lei Zhou | Peng‐Fei Hou | Yuyan Liu | R. Wang | Taoli Guo | Jincheng Liu
Source: JACS Au (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

A Machine Learning Interatomic Potential Data Set and Model for Catalysis with Local Fine-Tuning to Chemical Accuracy Heterogenous catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional density functional theory (DFT) methods. To address this, we introduce the catalytic large atomic model (CLAM), a comprehensive data set and pretrained model for machine learning interatomic potential, specifically designed for heterogeneous catalysis. CLAM is trained on a diverse data set that includes metal and alloy slabs with adsorbates, oxides, clusters, two-dimensional materials, and small molecules, ensuring high accuracy across a wide range of catalytic systems. Additionally, we introduce a "local fine-tuning" algorithm, which substantially improves the accuracy of machine learning interatomic potentials for structural optimizations and transition state searches. This approach achieves 94% prediction accuracy within chemical accuracy thresholds (<1 kcal/mol) for adsorption energies of small adsorbates on transition metal surfaces, while demonstrating 3.4× computational acceleration relative to conventional DFT calculations. Furthermore, the method maintains comparable performance in transition state searches, delivering 81% identification accuracy (<1 kcal/mol) with a 10.1× speed-up compared to standard DFT-based CI-NEB approaches. Furthermore, the pretrained CLAM model can accurately reproduce reported dynamic catalysis phenomena via molecular dynamics simulations without additional fine-tuning. Interatomic potential ; Set (abstract data type) ; Molecular dynamics ; Range (aeronautics) ; Density functional theory ; Identification (biology) ; Catalysis ; Experimental data Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Eduardo Aguilar-Bejarano | Ender Özcan | Raja K. Rit | Hongyi Li | Hon Wai Lam | Jonathan C. Moore | Simon Woodward | Grazziela P. Figueredo
Source: iScience (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis Optimization of metal-ligand asymmetric catalysts is usually done by empirical trials, where the ligand is arbitrarily modified, and the new catalyst is re-evaluated in the lab. This procedure is not efficient and alternative strategies are highly desirable. We propose the Homogeneous Catalyst Graph Neural Network (HCat-GNet), a machine learning model capable of aiding ligand optimization. This method trains models to predict the enantioselectivity of asymmetric reactions using only the SMILES representations of the participant molecules. HCat-GNet allows high interpretability indicating from which atoms the model gathers the most predictive information, thus showing which atoms within the ligand most affect the increase or decrease in the reaction's selectivity. The validation of the model's selectivity predictions is made using a new class of ligand for rhodium-catalyzed asymmetric 1,4-addition, demonstrating the ability of HCat-GNet to extrapolate into unknown chiral ligand space. Validation with other benchmark asymmetric reaction datasets demonstrates its generality when modeling different reactions. Interpretability ; Ligand (biochemistry) ; Computer science ; Chemistry ; Artificial intelligence ; Benchmark (surveying) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Li‐Cheng Xu | Miao‐Jiong Tang | Jisun An | Fenglei Cao | Qi Yuan
Source: Nature Machine Intelligence (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning Task (project management) ; Computer science ; Artificial intelligence ; Deep learning ; Engineering ; Systems engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ye min Thant | Sergei Manzhos | Manabu Ihara | Methawee Nukunudompanich
Source: Physchem (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often, multilayer (so-called “deep”) NNs are used. Considering that universal approximator properties hold for single-hidden-layer NNs, we compare here the performance of single-hidden-layer NNs (SLNN) with that of multilayer NNs (MLNN), including those previously reported in different applications. We consider three representative cases: the prediction of the band gaps of two-dimensional materials, prediction of the reorganization energies of oligomers, and prediction of the formation energies of polyaromatic hydrocarbons. In all cases, results as good as or better than those obtained with an MLNN could be obtained with an SLNN, and with a much smaller number of neurons. As SLNNs offer a number of advantages (including ease of construction and use, more favorable scaling of the number of nonlinear parameters, and ease of the modulation of properties of the NN model by the choice of the neuron activation function), we hope that this work will entice researchers to have a closer look at when an MLNN is genuinely needed and when an SLNN could be sufficient. Artificial neural network ; Layer (electronics) ; Computer science ; Artificial intelligence ; Feedforward neural network ; Machine learning ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Neural Networks and Applications ; Force Microscopy Techniques and Applications
Authors: Shengzhe Jia | Yiming Ma | Yuechao Cao | Zhenguo Gao | Sohrab Rohani | Junbo Gong | Jingkang Wang
Source: Engineering (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Progress of Machine Learning in Molecular Crystal Design and Crystallization Development Machine learning (ML) can optimize the research paradigm and shorten the time from discovery to application of novel functional materials, pharmaceuticals, and fine chemicals. Besides supporting material and drug design, ML is a potentially valuable tool for predictive modeling and process optimization. Herein, we first review the recent progress in data-driven ML for molecular crystal design, including property and structure predictions. ML can accelerate the development of the solvates, co-crystals, and colloidal nanocrystals, and improve the efficiency of crystal design. Next, this review summarizes ML algorithms for crystallization behavior prediction and process regulation. ML models support drug solubility prediction, particle agglomeration prediction, and spherical crystal design. ML-based in situ image processing can extract particle information and recognize crystal products. The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented. Finally, emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined. Crystallization ; Development (topology) ; Crystal (programming language) ; Materials science ; Computer science ; Nanotechnology ; Engineering ; Chemical engineering ; Mathematics ; Programming language Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Xin Liu | Keju Sun | Y.Z. Chen | Xiufang Ma | Changlin Yu | Zhenzhou Su | Hengfei He | Jianlin Lu | Hai‐Yan Su
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted computational screening of high-entropy alloy catalysts for HCOOH decomposition Machine-learning combined with DFT calculations and microkinetic modeling accelerates high-entropy alloy catalyst discovery for HCOOH decomposition. Catalysis ; Decomposition ; Alloy ; Density functional theory ; Carbon monoxide Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Adil Kabylda | J. Thorben Frank | Sergio Suarez Dou | Almaz Khabibrakhmanov | Leonardo Medrano Sandonas | Oliver T. Unke | Stefan Chmiela | Klaus-Robert Müller | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Sci. Adv. 10, eadn4397 (2024)]. This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semi-local interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions. SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the PBE0+MBD level of quantum mechanics, ensuring broad coverage of covalent and non-covalent interactions. Our approach is characterized by computational and data efficiency, scalability to 200 thousand atoms on a single GPU, and reasonable to high accuracy across the chemical space of organic (bio)molecules. SO3LR is applied to study units of four major biomolecule types, polypeptide folding, and nanosecond dynamics of larger systems such as a protein, a glycoprotein, and a lipid bilayer, all in explicit solvent. Finally, we discuss future challenges toward truly general molecular simulations by combining MLFFs with traditional atomistic models. Pairwise comparison ; Artificial neural network ; Computer science ; Artificial intelligence ; Force field (fiction) ; Molecular dynamics ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics