jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyu Wang | Jianyi Wang | Xiaoyu Zhang | Tiantong Lan | Jingsheng Liu | Hao Zhang
Source: Foods (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Prediction of Esterification and Antioxidant Properties of Food-Derived Fatty Acids and Ascorbic Acid Based on Machine Learning: A Review This study is dedicated to summarizing and performing an in-depth analysis of the antioxidant properties of ascorbic acid fatty acid esters. The esterification reaction mechanism of ascorbic acid with palmitic acid, lauric acid, and oleic acid in food systems was elaborated in detail, and its antioxidant mechanism was discussed in depth. The free radical scavenging mechanism and oxidative inhibition effect of two mainstream determination methods, DPPH and ABTS, were analyzed. Esterification, as a core organic synthesis reaction, is widely used in the production of food antioxidants, pharmaceutical ingredients, chemical polymers, and cosmetic oil-based matrices. At the same time, in view of the wide application of machine learning as a multidisciplinary core technology, this paper selects free radical scavenging rate and esterification yield as characteristic parameters and normalizes the offspring into random forest model training to achieve accurate prediction of antioxidant performance. Finally, in the future, it is necessary to expand the data set, optimize the model structure, explore multi-model fusion to improve the prediction effect, and promote the application of machine learning in the screening design of new antioxidants and the optimization of green synthesis processes to promote the intelligent and sustainable development of food antioxidant research. Ascorbic acid ; Antioxidant ; Palmitic acid ; DPPH ; Oleic acid ; Fatty acid ; Mechanism (biology) Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Coconut Research and Applications
Authors: А. Н. Филанович | A. V. Lukoyanov | А. А. Повзнер
Source: Advanced Theory and Simulations (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

Phonon Anharmonicity and Low Lattice Thermal Conductivity of Hf <sub>2</sub> FeNiSb <sub>2</sub> and Ti <sub>2</sub> FeNiSb <sub>2</sub> Double Half‐Heusler Alloys: Density Functional Theory and Machine Learning Study Abstract Double half‐Heusler alloys are a new class of materials with low lattice thermal conductivity, promising for thermoelectric applications. Theoretical predictions of thermal conductivity based on phonon spectra are limited as they require significant computational resources. However, recently, universal machine learning interatomic potentials (UMLIPs) have emerged, which allow fast and accurate prediction of interatomic forces and, consequently, the phonon spectrum. In this study, phonon spectra, Grüneisen parameters, and lattice thermal conductivities of double half‐Heusler alloys Hf 2 FeNiSb 2 and Ti 2 FeNiSb 2 are investigated using established ab initio techniques and the recently developed UMLIP MACE. The results from various methods for calculating lattice conductivity, including solving the linearized Boltzmann transport equation, the Debye–Callaway method, and the Slack formula, are compared. It is demonstrated that a pretrained UMLIP based on the MACE architecture can accurately predict phonon spectra and lattice thermal conductivity of double half‐Heusler alloys without requiring significant computational resources. It is shown that acoustic phonons in Hf 2 FeNiSb 2 and Ti 2 FeNiSb 2 exhibit strong anharmonicity, which causes low lattice thermal conductivities (1.4 W (m·K) −1 for Ti 2 FeNiSb 2 and 1.9 W (m·K) −1 for Hf 2 FeNiSb 2 at 900 K). Phonon ; Thermal conductivity ; Anharmonicity ; Lattice (music) ; Thermoelectric materials ; Density functional theory ; Interatomic potential Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Hiroto Okuyama
Source: MEMBRANE (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine–Learning–Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes Adsorption ; Polymer ; Protein adsorption ; Materials science ; Chemical engineering ; Polymer science ; Chemistry ; Computer science ; Engineering ; Composite material ; Organic chemistry Polymer Surface Interaction Studies ; Dendrimers and Hyperbranched Polymers
Authors: D. C. Li
Source: Biofunctional Materials (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

An overview of AI in Biofunctional Materials The integration of artificial intelligence (AI) into biofunctional materials is transforming material design, synthesis, and optimization for medical applications. Machine learning and deep learning models now predict material properties (e.g., mechanical strength, degradation rate) with > 90% accuracy, dramatically reducing trial-and-error in scaffold and nanoparticle fabrication. AI-driven platforms accelerate surface functionalization strategies to enhance cell adhesion and drug loading, while generative models design stimuli-responsive hydrogels and smart polymers that mimic tissue mechanics. Case studies include rapid optimization of nanoparticle synthesis via Bayesian frameworks and the discovery of biodegradable stent materials through random forest screening. Despite remaining challenges in data quality and regulatory alignment, these advances underscore AI’s capacity to deliver high-performance, sustainable biomaterials and point toward an interdisciplinary roadmap for next-generation therapeutic solutions. Nanotechnology ; Biochemical engineering ; Materials science ; Engineering Machine Learning in Materials Science
Authors: Kaustubh J. Sawant | David Stockwell | Anthony D. DeBellis | L. A. D'Orazio | Philippe Sautet
Source: Angewandte Chemie (article, 2025-08-21)
Field: Chemistry — Inorganic Chemistry

Ensemble of Active Acid Sites on Amorphous Silica–Alumina Surfaces for Catalytic Cracking Abstract Amorphous silica–alumina are critical materials in catalysis, particularly for fluid catalytic cracking (FCC). However, the atomic scale understanding of the active sites has been challenging, because of the nonuniform atomic distribution and the material's amorphous nature. Here, we use density functional theory (DFT), machine learning potentials and sampling methods to investigate the relationship between structure and acidity in silica‐modified alumina. Under FCC conditions, we predict an ensemble of acid sites with diverse local structures and a spectrum of acid strengths, including zeolite‐like bridging Brønsted acid sites (BAS) and pseudo‐bridging silanol BAS. This distribution is influenced by surface structure, Si coverage, and extent of hydroxylation, shaped by synthesis methods and reaction conditions. Experiments using a model Si‐stabilized alumina catalyst confirm that Brønsted acidity increases with Si content, peaking at an optimal value before declining. These insights provide a foundation for designing efficient solid acid catalysts for industrial applications. Amorphous silica ; Fluid catalytic cracking ; Cracking ; Catalysis ; Amorphous solid ; Active site ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion
Authors: Hyun‐Jin Jung | Ji Won Yoon | S.H. Son | Gwo‐Jen Hwang | Sungho Lee
Source: Microscopy and Microanalysis (article, 2025-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Large-Area SEM Image Analysis of Semiconductors Using Deep Learning and Statistical Analysis Materials science ; Semiconductor ; Statistical analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Computer science ; Optoelectronics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Machine Learning in Materials Science
Source:  (review, 2025-03-06)
Field: Materials Science — Materials Chemistry

Review for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Photocatalysis ; Electrocatalyst ; Catalysis ; Carbon dioxide ; Atom (system on chip) ; Carbon atom ; Value (mathematics) ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Organic chemistry ; Physical chemistry ; Electrochemistry ; Machine learning ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mei Ma | Wei Ma | Le Gao | Zongguo Wang | Haibo Liu
Source: Chinese Physics B (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics Abstract Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates, substantially reducing the costs associated with extensive experimental trial-and-error processes. However, existing methods, limited by static structural descriptors such as chemical composition and lattice parameters, fail to account for atomic vibrations, which may introduce spurious correlations and undermine predictive reliability. Here, we propose a deep learning model termed integrating graph and dynamical stability (IGDS) for predicting the synthesizability of inorganic crystals. IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties. Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems, achieving precision and recall values of 0.916/0.863 for ternary compounds. By capturing both static structural descriptors and dynamic features, IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals. This approach bridges the gap between theoretical design concepts and their practical implementation, thereby streamlining the development cycle of new materials and enhancing overall research efficiency. Bridging (networking) ; Phonon ; Materials science ; Dynamics (music) ; Crystal (programming language) ; Condensed matter physics ; Computer science ; Physics Machine Learning in Materials Science
Authors: Nicolas Renaud
Source: ArXiv.org (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations In this paper, we present results obtained using QMCTorch, a modular framework for real-space Quantum Monte Carlo (QMC) simulations of small molecular systems. Built on the popular deep learning library PyTorch, QMCTorch is GPU-native and enables the integration of machine learning-inspired components into the wave function ansatz, such as neural network backflow transformations and Jastrow factors, while leveraging efficient optimization algorithms. QMCTorch interfaces with two widely used quantum chemistry packages - PySCF and ADF - which provide initial values for the atomic orbital exponents and molecular orbital coefficients. In this study, we present wavefunction optimizations for four molecules: $H_2$, $LiH$, $Li_2$, and $CO$, using various wavefunction ansätze. We also compute their dissociation energy curves and the corresponding interatomic forces along these curves. Our results show good agreement with baseline calculations, recovering a significant portion of the correlation energy. QMCTorch provides a modular and extendable platform for rapidly prototyping new wavefunction ansätze, evaluating their performance, and analyzing optimization outcomes. Wave function ; Modular design ; Quantum Monte Carlo ; Quantum ; Monte Carlo method ; Molecular dynamics ; Function (biology) ; Dissociation (chemistry) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Libina Bovan Thomas | Ali Risheh | Negin Forouzesh
Source:  (article, 2025-10-12)
Field: Materials Science — Materials Chemistry

Multi-Objective Loss Function For Free Energy Calculations Accurate estimation of binding free energy (ΔΔG) remains a critical challenge in drug discovery, directly influencing the development of effective therapeutics. Physics-based computational methods—such as molecular dynamics simulations and implicit solvent modeling—offer rigorous, atomistic insights into molecular recognition but are often constrained by computational costs and limited scalability. In contrast, deep learning models, particularly graph convolutional networks (GCNs), have demonstrated the ability to rapidly predict molecular properties by learning hierarchical representations from large-scale chemical data, yet frequently lack explicit incorporation of physical laws, leading to potential issues with interpretability and generalizability. Hybrid physics-guided neural networks seek to overcome these limitations by embedding physically meaningful features and constraints within deep learning architectures. In this study, we introduce a multi-objective loss function that simultaneously optimizes empirical error, structural similarity, and physical consistency by integrating molecular fingerprints with physics-based features such as electrostatic and van der Waals energies in a unified GCN framework. Applied to 65–75 host-guest systems, this approach yields substantial improvements in entropy (TΔS) prediction accuracy, reducing the mean absolute difference (MAD) from 5.80 to 1.07 kcal/mol, while also enhancing the accuracy and convergence of binding free energy (ΔΔG) predictions (MAD reduced to 1.54 kcal/mol), mitigating overfitting, and improving model transferability to larger complexes. These results demonstrate that principled loss function engineering is pivotal not only for accurate entropy estimation but also for enhancing the reliability and interpretability of binding free energy predictions in molecular machine learning models. Interpretability ; Molecular dynamics ; Entropy (arrow of time) ; Artificial neural network ; Function (biology) ; Transferability ; Graph ; Deep learning ; Physical system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ben Gao | Penghui Li | Di Zhang | Qian Tan | Wei Liu | Xunzhi Wang | Junxian Li | Shufei Zhang | Dongzhan Zhou | Yuqiang Li | Guoyin Yin
Source: CCS Chemistry (article, 2025-09-13)
Field: Materials Science — Materials Chemistry

LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions Modular design ; Task (project management) ; Artificial neural network ; Product (mathematics) ; Feature (linguistics) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Source:  (review, 2025-02-28)
Field: Materials Science — Materials Chemistry

Review for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Photocatalysis ; Electrocatalyst ; Carbon dioxide ; Catalysis ; Nanotechnology ; Atom (system on chip) ; Value (mathematics) ; Materials science ; Environmental science ; Chemical engineering ; Biochemical engineering ; Chemistry ; Computer science ; Engineering ; Physical chemistry ; Organic chemistry ; Electrochemistry ; Machine learning ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Qian Zhang (49333) | Hengyue Zhang (16639972) | Zhiyao Su (789471) | Yajing Sun (2569150) | Wenping Hu (763730)
Source: Figshare (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Discovering MolecularInsights in Organic Optoelectronicswith Knowledge-Informed Interpretable Deep Learning Deep learning holds significant promise for accelerating molecular screening and materials design. However, the black-box nature of current models limits their ability to generate fundamentally new chemical knowledge and insights. Here, we propose LUMIA (Learning and Understanding Molecular Insights with Artificial Intelligence), an innovative interpretable deep learning framework integrating chemistry-informed contrastive learning and Monte Carlo tree search (MCTS). LUMIA is pretrained on approximately 1.4 million organic molecules, using knowledge-informed augmentations that embed π-conjugation and substituent effects explicitly. This allows it to effectively capture hierarchical molecular representations aligned with chemical intuition. Critically, the explicit integration of chemical knowledge enables LUMIA to achieve state-of-the-art performance across multiple organic optoelectronic property prediction tasks. Leveraging its intrinsic interpretability through MCTS, LUMIA directly uncovers previously unexplored substructure patterns influencing reorganization energy, enabling rational molecular design beyond the training data set. Furthermore, LUMIA reveals novel chemical insights, including synergistic effects of substituent positions in pyrazole derivatives. This study highlights the pivotal role of knowledge embedding in interpretable deep learning, transforming molecular design, and accelerating chemical discovery. Interpretability ; Deep learning ; Embedding ; Cheminformatics ; Property (philosophy) ; Scheme (mathematics) ; Substituent Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Raghavendra Botta | A. Jain | Prakash Deep
Source:  (article, 2025-12-13)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning Approaches to Contour Extraction in Noisy SEM Images for Advanced OPC Modeling Accurate extraction of feature contours from low-contrast, noisy scanning electron microscope (SEM) images is critical for advanced optical proximity correction (OPC) in semiconductor manufacturing. Traditional CD-SEM (Critical Dimension SEM) methods provide high-resolution, site-specific measurements but are limited by operational overhead and insufficient 2D pattern coverage. Recent advances leverage deep learning—particularly convolutional neural networks such as HED, RCF, and DexiNed—to enable direct, single-pixel contour extraction from SEM imagery, overcoming the limitations of classic edge detection techniques.This work demonstrates that deep-learning-based pipelines substantially improve contour fidelity, model calibration, and robustness for complex semiconductor layouts, while simultaneously reducing data requirements and operational cost. The methods discussed are validated on multiple industrial datasets and show strong generalization across different process layers and noise conditions. By adopting these machine learning approaches, process engineers can achieve more reliable, automated OPC modeling, better design-to-manufacturing matching, and support the scaling needs of next-generation semiconductor technologies. Robustness (evolution) ; Deep learning ; Feature extraction ; Leverage (statistics) ; Convolutional neural network ; Pattern recognition (psychology) ; Optical proximity correction ; Artificial neural network ; Noise (video) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Juno Nam (15305652) | Miguel Steiner (19794862) | Max Misterka (22538611) | Soojung Yang (9615984) | Avni Singhal (20772328) | Rafael Gómez-Bombarelli (1933810)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

TransferableLearning of Reaction Pathways from GeometricPriors Identifying minimum-energy paths (MEPs) is a crucial application of molecular simulations to understand chemical reaction mechanisms but is computationally demanding. We introduce MEPIN, a scalable machine-learning method that predicts MEPs from reactant and product configurations, without relying on transition-state geometries or preoptimized reaction paths during training. The MEPIN task is defined as predicting the deviation between ground-truth MEPs and purely geometric interpolations along the reaction coordinates. The model predicts a continuous reaction path using a symmetry-broken equivariant neural network architecture that generates a flexible number of intermediate structures. MEPIN is trained on an energy-based objective, and we report efficiency gains of also using geometric priors from geodesic interpolation as initial interpolations or as pretraining objectives. The approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated in various small molecule reactions and [3 + 2] cycloadditions. Prior probability ; Geodesic ; Interpolation (computer graphics) ; Path (computing) ; Artificial neural network ; Scalability ; Equivariant map ; Orthogonality Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ashujot K. Dang | Emma Liu | Wei Zhi
Source:  (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Identifying SMILES from Molecular Structure Images Accurate extraction of Simplified Molecular Input Line Entry System (SMILES) representations from molecular structure images is crucial for computational chemistry and cheminformatics. This study presents a machine learning-based approach for converting graphical molecular representations into SMILES notation, addressing the challenges of chemical image recognition. We developed a deep learning model leveraging Tensorflow/Keras framework, cheminformatics tools such as RDKit, and Long Short- Term Memory (LSTM) networks for sequence learning. Trained on a curated dataset of molecular images, the model effectively learns structure-text relationships, enabling high-accuracy SMILES predictions. Our approach enhances chemical data digitization, improves dataset accuracy, and accelerates molecular property assessments and drug discovery, driving progress in pharmaceutical research and personalized medicine. By training and testing an image-captioning model, we ensure robust SMILES generation while maintaining high fidelity to molecular structures. Cheminformatics ; Fidelity ; Property (philosophy) ; Training set ; Chemical database ; Sequence (biology) ; Representation (politics) ; Deep learning ; Molecular descriptor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Adam Liebendorfer
Source: Scilight (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Machine-learning molecular simulations show role of atomic-scale roughness in hydrophilicity Simulations comparing aluminum oxide models with differing amounts of corrugation demonstrate the significance of hydroxyl groups in water interactions. Aluminum oxide ; Surface roughness ; Surface finish ; Oxide ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Chemical Physics Studies