jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Maevskiy | Vitalii Kapitan | Andrey Ustyuzhanin
Source: Advanced Intelligent Systems (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review Recent progress in artificial intelligence (AI) has transformed methodologies across many areas of science. In materials research, AI has enabled more efficient multiscale modeling by linking atomic, mesoscale, and continuum scales with improved accuracy and reduced computational cost. This review examines AI‐based approaches in this context and discusses their relationship to conventional analytical and computational multiscale methods. Developments such as machine learning force fields, graph neural networks, and AI‐accelerated electronic structure prediction are assessed with respect to their capabilities and limitations. To illustrate the current state of the art in this field, available software, computational tools, and benchmarks are discussed. Applications in areas such as phase transitions, defect dynamics, and bulk property prediction are shown, with an emphasis on how AI enhances predictive capabilities. While highlighting the above‐mentioned recent advances, existing challenges and promising directions are also discussed. This review is intended for two audiences: For AI researchers, it demonstrates how physical and chemical constraints influence models’ development to ensure physical consistency, and for physicists, chemists, and materials scientists, it illustrates how AI can improve multiscale methods to solve previously inaccessible problems Multiscale modeling ; Context (archaeology) ; Artificial neural network ; Applications of artificial intelligence ; Computational model ; Computational intelligence ; Property (philosophy) ; Graph Machine Learning in Materials Science ; Model Reduction and Neural Networks ; X-ray Diffraction in Crystallography
Authors: Juan Esteban Bedoya-Rodríguez | Leon Escobar-Diaz | Stephen Hernández
Source: Physica A Statistical Mechanics and its Applications (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Phase diagrams construction using mean-field renormalization and neural network fitting Ising model ; Phase diagram ; Renormalization group ; Artificial neural network ; Parametric statistics ; Phase (matter) ; Phase transition Machine Learning in Materials Science ; Theoretical and Computational Physics ; Quantum many-body systems
Authors: Xi Liu | Da Zhou | Yuming Su
Source: ChemRxiv (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

AEP-Net: Angle-aware Electrostatic Potential Point Network for Molecular Property Prediction Molecular representation learning is a fundamental task in computational chemistry and drug discovery. While graph neural networks (GNNs) have significantly advanced molecular representation learning, they rely solely on conventional molecular graph, inherently failing to capture critical three-dimensional electronic structure, which are crucial determinants of molecular interactions. To address this, we propose the Angle-aware Electrostatic Potential point Network (AEP-Net), a novel paradigm that explicitly integrates high-resolution 3D Electrostatic Potential (ESP) represented as point clouds. AEP-Net represents molecules as collections of Atomic-centered Electrostatic-potential Subgraphs (AES). We design an angle-aware convolution mechanism that explicitly captures 3D spatial geometric features through learnable directional bases. Each AES processes its internal ESP points via a field-aware interaction layer, employing a triple weighting mechanism based on angular similarity, spatial proximity, and electrostatic values to capture fine-grained local electrostatic patterns. Features from different AESs are aggregated at the atomic level through global pooling, preserving the integrity of the local ESP environment while enabling a scalable architecture. Subsequently, a hierarchical hybrid network deeply integrates multi-scale features at the point cloud, atomic, and molecular levels, Representation (politics) ; Weighting ; Scalability ; Point (geometry) ; Convolution (computer science) ; Graph ; Property (philosophy) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yujie Wang | Xuechen Zhang | Sitan Ye | Guohong Yu | Mostafa Gouda | Xiaoli Li | Yong He
Source: Future Foods (article, 2026-01-28)
Field: Chemistry — Analytical Chemistry

Dual-branch CNN-based fusion of computer vision and near-infrared spectroscopy for quantitative prediction: A case of black tea processing • A dual-branch CNN-based fusion framework was proposed for quantitative analysis. • CV outperformed NIRS in the prediction accuracy of black tea components. • Mid fusion of features extracted by CNN achieved the highest predictive accuracy. • S-G-SNV/MSC showed advantages in reducing noise caused by tea leaf particles. • It’s an in-situ low-cost solution for quality monitoring during tea processing. The captivating flavor and potential health benefits of black tea are developed during its unique processing stages. At present, the assessment of black tea quality relies on the sensory evaluation of tea masters. However, there is a lack of quality monitoring methods suitable for industrial-scale production of black tea. This study presents a convolutional neural network (CNN)-based multimodal fusion approach integrating near-infrared spectroscopy (NIRS) and computer vision (CV) for the non-destructive quantification of components in black tea processing, including moisture, polyphenols (including catechins), and chlorophyll. A total of 600 tea samples were collected from four batches and six processing stages, with standard chemical methods used as reference measurements. Spectral preprocessing was applied, and traditional machine learning models (PLSR, SVR) were compared against deep learning architectures (1DCNN, 2DCNN). The results showed that CNN models significantly outperformed traditional machine learning. The image-based 2DCNN model outperformed the NIRS-based 1DCNN. Furthermore, the dual-branch CNN-based fusion of NIRS and CV achieved higher predictive accuracy, yielding the highest residual predictive deviation (RPD) values for the prediction of moisture (11.0774), polyphenols (4.1432), catechins (3.3912), and chlorophyll (7.8479). Overall, this study provides a rapid, cost-effective and on-site deployable solution for quality monitoring during black tea processing, facilitating targeted control of tea quality. Black tea ; Convolutional neural network ; Fusion ; Sensor fusion ; Artificial neural network ; Polyphenol ; Pattern recognition (psychology) ; Residual ; Predictive modelling Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Smart Agriculture and AI
Authors: Samrat Chakraborty
Source: ChemRxiv (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Generalizing the Geometry–Property Noncorrelation: ADescriptor Informativeness Index for Organic Molecules The recent study by Joy, Kraus, Ricks, and Ess (Organometallics 2025, 44, 1566–1575) revealed a striking finding for transition metal chemistry: across 686 experimentally char acterized Pd-Me complexes, the Pd-C bond length shows no correlation whatsoever with bond dissociation energy (R2 = 0.00), despite perfectly predicting the intact bond’s force constant (R2 = 0.92) and electron density (R2 = 0.99). This observation, which challenges a cornerstone of chemical intuition, raises a compelling question about its generality beyond transition metal systems. In the present work, we introduce the Descriptor Informativeness Index (DII), a quantitative metric that measures the fraction of achievable predictive accuracy retained by a given descriptor subset relative to a full-feature machine learning model. We apply this metric to 11,736 organic molecules from the QM9 dataset across six quantum chemical properties and two structurally distinct molecule classes. Our analysis reveals that organic molecules retain substantial geometric informativeness (DIIgeo = 0.33–0.80), in clear contrast to the Pd-Me system (DIIgeo ≈ 0.00). However, geometric informativeness is not uniform: it is systematically lower in aromatic molecules (mean DIIgeo = 0.465) than in aliphatic molecules (mean DIIgeo = 0.637), with the largest suppression observed for electronic properties. These results contextualize the elegant findings of Joy et al. within a broader spectrum of geometry–property relationships, suggesting that the degree to which geometric descriptors inform molecular properties is governed by the interplay between delocalization and the nature of the target property Molecule ; Delocalized electron ; Metric (unit) ; Organic molecules ; Quantitative structure–activity relationship ; Quantum chemical ; Characterization (materials science) ; Bond-dissociation energy ; Dissociation (chemistry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Omid Mahmoudi | Mi‐Hyun Kim
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting electronic properties of molecules: a stacking ensemble model for HOMO and LUMO energy estimation The energies of the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) are important determinants of molecular reactivity and stability. Traditional quantum chemical (QC) methods for calculating the HOMO and LUMO energies face drawbacks, including their costliness and long computational time. Therefore, machine learning is well-positioned to act as a catalyst for QC predictions. In this study, we report a stacking ensemble model named HLP-Stack (HOMO-LUMO predictor <i>via</i> stacking) to predict the energy values using molecular descriptors and the QM9 dataset. The stacking achieved robust predictive performance superior to any single model by combining 2D/3D descriptors of the QM9 dataset. It achieved high predictive performance on the test set (<i>R</i> <sup>2</sup> ≈ 9.999 × 10<sup>-1</sup>, RMSE ≈ 3.219 × 10<sup>-4</sup> Hartree (<i>E</i> <sub>h</sub>) for HOMO; <i>R</i> <sup>2</sup> ≈ 9.999 × 10<sup>-1</sup>, RMSE ≈ 1.903 × 10<sup>-4</sup> <i>E</i> <sub>h</sub> for LUMO), outperforming individual baseline models. Feature selection using the SelectKBest algorithm with mutual information regression identified the most influential descriptors. To ensure these descriptors did not trivially encode HOMO or LUMO energies, we performed correlation analysis between each descriptor and the target properties. SHAP Tree Explainer analysis further revealed the feature contribution of each feature to model predictions. In addition, analysis of molecular topology and functional groups highlighted trends in aromaticity and ring structures, and their impact on electronic behavior. Finally, HOMO-LUMO gap analysis demonstrated how molecular structure and functionalization affect electronic properties. Stacking ; HOMO/LUMO ; Energy (signal processing) ; Ensemble forecasting ; Electronic structure Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Soumyabir Guha Guha
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Ab Initio Accelerated Discovery of Mixed-Cation Perovskites for >33% Efficiency: A Hybrid DFT-MD Framework The thermodynamic instability of organic-inorganic halide perovskites remains the singular bottleneck preventing the displacement of silicon photovoltaics. This research presents a "God-tier" computational framework unifying Density Functional Theory (DFT) with Machine Learning Force Fields (MLFF) to autonomously discover a hyper-stable perovskite stoichiometry with a theoretical efficiency exceeding 33% (Shockley-Queisser limit approach). By bridging the accuracy of the PBEsol functional with the temporal scale of Classical Molecular Dynamics (MD), we simulate degradation pathways over microseconds—a feat impossible with standard ab initio MD. The study identifies a triplecation lattice (Cs_{0.05}FA_{0.85}MA_{0.10}Pb(I_{0.97}Br_{0.03})_3) that suppresses the \alpha-to-\delta phase transition via entropy stabilization. This work leverages massively parallel Fortran kernels for electronic structure, Python for graph neural network potential training, and Java for real-time lattice visualization, offering a blueprint for the next generation of energy materials. Density functional theory ; Ab initio ; Bottleneck ; Massively parallel ; Python (programming language) ; Molecular dynamics ; Phase transition ; Perovskite (structure) ; Lattice (music) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Shaoxuan Yuan | Zhiwen Zhu | Jiayi Lu | Liangliang Cai | Qiang Sun
Source: Journal of Materials Informatics (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Computer vision for efficient object detection and segmentation in molecular image analysis Image recognition, classification, and analysis of large sets of high-resolution molecular images are time-consuming and labor-intensive, even for human experts, due to the lack of standardized approaches. In recent years, machine learning has emerged as a powerful tool for automating image data analysis in materials science. In this work, we developed a computer vision program for efficient object detection and instance segmentation, offering a fast alternative to manual molecular image analysis. By integrating You Only Look Once version 9 (YOLOv9) with an incremental learning strategy and hyperparameter optimization, the system enables accurate detection, classification, and segmentation of molecular species across diverse scanning tunneling microscopy datasets. Our results demonstrate robust performance and minimal forgetting rates across multiple molecular categories, enabling scalable and updatable surface image analysis workflows. We anticipate that computer vision methods will see increasing applications in image data analysis within the field of on-surface chemistry. Object detection ; Segmentation ; Image segmentation ; Image (mathematics) ; Feature detection (computer vision) ; Field (mathematics) ; Machine vision ; Hyperparameter ; Scalability Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Soumyabir Guha Guha
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Ab Initio Accelerated Discovery of Mixed-Cation Perovskites for >33% Efficiency: A Hybrid DFT-MD Framework The thermodynamic instability of organic-inorganic halide perovskites remains the singular bottleneck preventing the displacement of silicon photovoltaics. This research presents a "God-tier" computational framework unifying Density Functional Theory (DFT) with Machine Learning Force Fields (MLFF) to autonomously discover a hyper-stable perovskite stoichiometry with a theoretical efficiency exceeding 33% (Shockley-Queisser limit approach). By bridging the accuracy of the PBEsol functional with the temporal scale of Classical Molecular Dynamics (MD), we simulate degradation pathways over microseconds—a feat impossible with standard ab initio MD. The study identifies a triplecation lattice (Cs_{0.05}FA_{0.85}MA_{0.10}Pb(I_{0.97}Br_{0.03})_3) that suppresses the \alpha-to-\delta phase transition via entropy stabilization. This work leverages massively parallel Fortran kernels for electronic structure, Python for graph neural network potential training, and Java for real-time lattice visualization, offering a blueprint for the next generation of energy materials. Density functional theory ; Ab initio ; Bottleneck ; Massively parallel ; Python (programming language) ; Molecular dynamics ; Phase transition ; Perovskite (structure) ; Lattice (music) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Xinhu Sha | Xu Guo | Ziyu Chen | Daiqian Xie | Yanzi Zhou
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Efficient deep learning framework for multiscale simulations in enzymes via linear response approximation The unprecedented development of machine learning (ML) provides opportunities to accelerate QM/MM calculations. However, the ML/MM frameworks face challenges in achieving unified chemical interpretability in QM/MM electrostatic interactions and transferability across unlearned MM environments. In this work, utilizing linear response approximation, the QM/MM electrostatic interactions are decoupled from the QM energy, as the response of the QM electrostatic potentials to the MM environment. Starting from this point, we have proposed an MM atom electrostatic field-induced, recursively embedded atom neural network to reproduce the QM/MM electrostatic energy. Our framework exhibits high efficiency and powerful transferability across different environments, enabling stable ML/MM simulations on the nanosecond scale and reproducing the free energies predicted by QM/MM-MD within an error range of 0.5 kcal mol-1 for various solvents and homologous enzymes, which is expected to provide a new paradigm for accurate and rapid prediction of the free energies in enzyme reactions. Interpretability ; Transferability ; Electrostatics ; Range (aeronautics) ; Deep learning ; Artificial neural network ; Atom (system on chip) ; Nanosecond Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Abderaouf Haithem Aroua | Aymen HAMIDI | Merouane BOUKANDOURA | Khadija BRAKTA | Redouane MEZIARI
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Automated Digitalization and Molecular Enrichment of Industrial Chemical Documentation: A French-Language NLP and Cheminformatics Pipeline Industrial chemical manufacturers maintain extensive technical documentation — product specifications, raw material datasheets, and formulation records — that encode decades of domain knowledge yet remain inaccessible to modern machine learning methods when authored in local languages as scanned or unstructured digital files. We present an automated pipeline for converting French-language industrial chemical documentation into ML-ready molecular datasets. Applied to 809 pages of technical documentation from a North African industrial chemical manufacturer (ENAD SHYMECA, Algeria) — producing detergents, cosmetics, and automotive chemical products — the pipeline processes 382 raw extracted strings — of which 333 are real ingredients (the remaining 49 are noise: water entries, metadata fragments, and mixture compositions) — through four stages: (1) regex-based extraction of ingredient tables and physicochemical specifications from Markdown-converted OCR text; (2) cascaded INCI normalization of the 333 real ingredients via a 322-entry domain-specific synonym map (Stage 0), resolving 315 names, exact matching against the EU CosIng database (24,094 entries, Stage A), and fuzzy token-sorted matching (threshold 82%, Stage B), achieving 100% normalization coverage without requiring LLM assistance; (3) molecular enrichment via the PubChem PUG REST API, recovering canonical SMILES for 255 of 260 chemically definable compounds (98.1%; 5 unresolvable entries are legitimate polymeric surfactants and pigment mixtures); and (4) molecular descriptor computation yielding 208 RDKit and 973 Mordred 2D descriptors for 99 unique molecules after quality filtering (NaN threshold 20%, median imputation to 100% completeness). Pipeline reproducibility is validated on 53 Open Beauty Facts products (1,518 ingredient instances), achieving 80.5% INCI coverage. The pipeline code and a 322-entry French-to-INCI synonym dictionary (Table S1) are released as open-source software to enable replication by other French-language manufacturers. Formula concentration data are not released due to confidentiality obligations with the industrial partner. Documentation ; Pipeline (software) ; Cheminformatics ; Metadata ; Raw data ; Normalization (sociology) ; Imputation (statistics) ; DrugBank Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Ludia T. Khusainova | Konstantin S. Kolegov
Source: The European Physical Journal E (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Identification of 2D colloidal assemblies in images: a threshold processing method versus machine learning Identification (biology) ; Artificial neural network ; Field (mathematics) ; Colloidal particle ; Software ; Image processing ; Particle (ecology) ; Deep learning Machine Learning in Materials Science ; Advanced Computational Techniques in Science and Engineering ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Xingyu Guo | Cheng Gui | Zhenbin Wang
Source: arXiv (Cornell University) (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

Are Universal Potentials Ready for Alkali-Ion Battery Kinetics? Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in capturing these high-energy landscapes remains uncertain. Here, we systematically benchmark state-of-the-art uMLIPs, including M3GNet, CHGNet, MACE, SevenNet, GRACE, and Orb, against DFT baselines for cathodes and solid electrolytes. We find that the Orb-v3 family excels in static migration barrier predictions (MAE $\approx$ 75--111 meV), driven primarily by architectural refinements. Conversely, for dynamic transport, the GRACE model trained on the OMat24 dataset demonstrates superior fidelity in reproducing ion diffusivities and structural correlations. Our results reveal that while architectural sophistication (e.g., equivariance) is beneficial, the inclusion of high-temperature, non-equilibrium training data is the dominant driver of kinetic accuracy. These findings establish that modern uMLIPs are sufficiently robust to serve as zero-shot surrogates for high-throughput kinetic screening of next-generation energy storage materials. Battery (electricity) ; Sophistication ; Benchmark (surveying) ; Reliability (semiconductor) ; High fidelity ; Kinetic energy Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials