jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muskan Leharwani | Harshita Singhai | Umme Hani | Vanitha Innocent Rani | Garima Gupta | Khang Wen Goh | Umesh Kumar Patil | Prashant Kesharwani
Source: Inorganic Chemistry Communications (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Herbal carbon dots for wound healing: Bridging traditional phytomedicine with advanced Nanotherapeutics Phytomedicine ; Antimicrobial ; Nanomedicine ; Heteroatom ; Nanobiotechnology ; Nanoparticle Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Curcumin's Biomedical Applications
Authors: Masayuki KATO | Fumio NAKASHIMA | Naoya Ohtsuka | Yukina NISHIOKA | Atsuto Izumiseki | Takeshi Fujinami | Shunya Oishi | Toshiyasu Suzuki | Norie Momiyama
Source: The Journal of Organic Chemistry (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Data-Integrated Elucidation of Structure–Activity Relationships toward the Rational Design of Perfluoroiodoarene-Based Halogen-Bond Donor Catalysts Understanding and predicting catalyst performance from structural and electronic information remains a central challenge in organocatalysis. Here, we present a data-integrated framework that quantitatively combines experimental, computational, and machine learning (ML) approaches to elucidate the structure-activity relationships of halogen-bond (XB) donor catalysts based on perfluoroiodoarene cores. Single-crystal X-ray diffraction and chloride-binding analyses revealed that linker-containing two-point donors exhibit significantly stronger binding and higher catalytic activity than one-point donors. Through density functional theory calculations and an ML regression analysis integrating crystallographic and electronic descriptors, the Gibbs free energy change (Δ<i>G</i>) and binding constant (<i>K</i>) were identified as primary determinants of activity. Meanwhile, a Shapley additive explanation analysis highlighted the σ/π-hole potentials and nucleophilicity (<i>N</i> value) as additional electronic factors. This integrated experimental-computational-ML approach enables the quantitative extraction of key electronic factors governing XB donor catalysis and provides a physically interpretable framework for extending noncovalent-interaction-driven organocatalysis beyond XB formation, encompassing hydrogen- and chalcogen-bond donor systems. Catalysis ; Rational design ; Density functional theory ; Nucleophile ; Gibbs free energy ; Electronic effect ; Electronic structure Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Crystallography and molecular interactions
Authors: M. Hušák | František Fňukal
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Towards a double verified (experimental + DFT) reference structure database Any database used for structure verification or for machine learning (ML) force field development must be at first 100% reliable itself.In our work we focused on structure verification of pharmaceutical molecular crystals, so our main focus is the use of Cambridge Structure Database (CSD).The existing automatic validation tools like the checkCIF and PLATON software can verify the consistency of experimental data, but they cannot fully check the "chemical sense" of the structures [1].From this point of view the data in the CSD cannot be used for data verification or for ML purposes directly without additional filtering and verification. Consistency (knowledge bases) ; Field (mathematics) ; Software verification ; Focus (optics) ; Software ; Data structure ; Point (geometry) ; Data verification Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Diego Alexander Garzón | Lauri Himanen | Luisa Andrade | Sascha Sadewasser | José A. Márquez
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

ML-guided screening of chalcogenide perovskites as solar energy materials Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here, we present a fully data-driven and experimentally grounded screening and ranking framework to assess the stability and experimental feasibility of chalcogenide perovskites, integrating interpretable analytical descriptors, machine-learning models, and sustainability metrics. Using a curated experimental dataset of halide and chalcogenide compounds, we derive a new tolerance factor via the SISSO (sure independence screening and sparsifying operator) algorithm that more accurately distinguishes perovskite-forming compositions than established tolerance-factor-based screening criteria. This descriptor is combined with generative crystal structure prediction, composition-based bandgap estimation, and machine-learning-based feasibility assessment to systematically explore a wide chemical space of hypothetical chalcogenide perovskites. The resulting candidates are further evaluated using sustainability indicators, enabling multi-objective ranking tailored to both single-junction and tandem photovoltaic architectures. Beyond identifying several promising and previously unexplored chalcogenide perovskites, this work demonstrates a transferable screening strategy for chemically constrained materials spaces that balances optoelectronic performance, experimental viability, and long-term sustainability. Chalcogenide ; Photovoltaic system ; Ranking (information retrieval) ; Band gap ; Photovoltaics ; Realization (probability) ; Tandem ; Perovskite (structure) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Samuel Del Fré | Andrée De Backer | Christophe Domain | Ludovic Thuinet | Charlotte S. Becquart
Source: Computational Materials Science (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Unsupervised machine-learning pipeline for data-driven defect detection and characterisation: Application to displacement cascades Autoencoder ; Displacement (psychology) ; Outlier ; Anomaly detection ; Pipeline (software) ; Unsupervised learning ; Projection (relational algebra) ; Pattern recognition (psychology) ; Cluster analysis Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Yan Zhang | Jiani Tan | Qing Mu | Joshua S. Fu | Li Kwan Li
Source:  (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Supplementary material to "Quantifying the impact of input data-induced dataset shift on machine learning model applications: A case study of regional reactive nitrogen wet deposition" Nitrogen ; Predictive modelling ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Air Quality Monitoring and Forecasting ; Hydrological Forecasting Using AI
Authors: Selma Bulut | Beyza Buzol Mülayim
Source: Journal of Innovative Engineering and Natural Science (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Artificial intelligence in textile and fashion design: a bibliometric analysis Textile and fashion design are among the disciplines where technological advancements are rapidly integrated, with artificial intelligence (AI) playing an increasingly vital role. AI-driven design software enhances fabric texture, color harmony, and pattern creation, while smart textiles and algorithms enable personalized clothing production. Additionally, AI-based analyses are being widely utilized in fashion trend forecasting and sustainable material development. Understanding the evolving research landscape in this field is crucial for future innovations. This study conducts a bibliometric analysis of 856 research articles on textile and fashion design published between 1980 and 2024. Using the Web of Science database and VOSviewer software, the study evaluates literature trends, influential studies, key authors, and emerging keywords. The results indicate that academic interest in this field peaked in 2024, with the most cited study being Dong, K.; Peng, X.; and Wang, ZL’s research on fiber- and fabric-based nanogenerators for wearable electronics and AI applications. China emerges as the leading contributor to AI-driven fashion research, while Chengkuo Lee stands out as a key figure in the field. Keyword analysis highlights strong associations with concepts such as machine learning, artificial intelligence, computer vision, deep learning, and convolutional neural networks. The shift in 2024 towards keywords like “electronic textiles,” “biomarkers,” and “LLM” (large language models) reflects the growing intersection of AI, intelligent materials, and wearable health technologies. These findings underscore the increasing importance of AI in shaping the future of textile and fashion design, necessitating interdisciplinary collaborations. Despite the study’s limitation of relying solely on the Web of Science database and not conducting content analysis, its findings provide a comprehensive overview of AI's evolving role in the sector. This research offers a valuable foundation for future studies, guiding researchers and industry professionals in leveraging AI technologies for innovative design, sustainable production, and enhanced consumer experiences. Clothing ; Field (mathematics) ; Wearable technology ; Wearable computer ; Key (lock) ; Bibliometrics ; Textile ; Big data Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Fashion and Cultural Textiles
Authors: Jordi Morales-Vidal | Zan Lian | Thaylan Pinheiro Araújo | Sharon Mitchell | Javier Pérez-Ramírez | Núria López
Source: ACS Physical Chemistry Au (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Phases and Architectures in Metal/Metal Oxide Systems Driven by Strong Metal–Support Interactions Metal oxide supported metal catalysts are widely applied in industrial processes. Many of these materials dynamically evolve under reducing atmospheres, leading to metal nanoparticles partially or fully encapsulated by metal oxide shells, impacting catalytic performance. This phenomenon is known as strong metal-support interaction (SMSI) and is thermodynamically driven. However, understanding the metal/metal oxide interfaces derived from the broad and flexible compositional space and the large structural changes in SMSI structures is difficult to monitor experimentally. Here, we use density functional theory together with machine learning interatomic potentials and global minima optimization to investigate SMSI by building a set of interfaces between common catalytic metals (Ni, Pd, Pt) and reducible metal oxides (r-TiO<sub>2</sub>, CeO<sub>2</sub>, In<sub>2</sub>O<sub>3</sub>) at different reduction levels. Phase diversity arises from the competition between the formation of different metal oxides or binary alloys, while the local properties of the suboxide layers are responsible for the final architecture and composition determining the electronic properties of the material. Two descriptors related to the competition between alloy and oxide formation are proposed to elucidate the phase diversity. Our work provides a systematic approach to advance the design of SMSI-based catalytic materials by offering insights into the atomic-level architecture of the metal/metal oxide interfaces. Oxide ; Suboxide ; Catalysis ; Metal ; Density functional theory ; Phase (matter) ; Phase space ; Perovskite (structure) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Mingxin Xu | Yilin Zhao | Hui Li | PW Ayers | Dandan Liu | Qingchun Wang | Dongbo Zhao
Source: Entropy (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Predicting the Redox Potentials and Hammett Parameters of Quinone Derivatives with the Information-Theoretic Approach Accurately and efficiently predicting redox potentials and Hammett constants using simple density-based functions derived from information-theoretic approach (ITA) quantities remains an unresolved challenge. In this work, we employ two recently proposed protocols, DL(ITA) (deep learning) and QML(ITA) (quantum machine learning), to a broad range of quinone derivatives with available experimental data. The molecular electrostatic potential (MEP) at the nucleus of the acidic atom and the sum of valence natural atomic orbital (NAO) energies are used within a linear regression (LR) framework to assess the first redox potentials and Hammett parameters of these quinone derivatives. The DL(ITA) protocol enables the construction of a transferable model trained on quinone derivatives that can be applied to both quinone and non-quinone systems. Interestingly, the QML(ITA) model exhibits superior performance compared to the DL(ITA) approach. Moreover, the structure of the QML(ITA) method suggests that it may be readily implemented on real quantum hardware in the near future. Quinone ; Redox ; Valence (chemistry) ; Quantum chemistry ; Quantum ; Exponential function ; Atom (system on chip) Machine Learning in Materials Science ; Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Yunhao Fan | Gia-Wei Chern
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Equivariant Neural Networks for Force-Field Models of Lattice Systems Machine-learning (ML) force fields enable large-scale simulations with near-first-principles accuracy at substantially reduced computational cost. Recent work has extended ML force-field approaches to adiabatic dynamical simulations of condensed-matter lattice models with coupled electronic and structural or magnetic degrees of freedom. However, most existing formulations rely on hand-crafted, symmetry-aware descriptors, whose construction is often system-specific and can hinder generality and transferability across different lattice Hamiltonians. Here we introduce a symmetry-preserving framework based on equivariant neural networks (ENNs) that provides a general, data-driven mapping from local configurations of dynamical variables to the associated on-site forces in a lattice Hamiltonian. In contrast to ENN architectures developed for molecular systems -- where continuous Euclidean symmetries dominate -- our approach aims to embed the discrete point-group and internal symmetries intrinsic to lattice models directly into the neural-network representation of the force field. As a proof of principle, we construct an ENN-based force-field model for the adiabatic dynamics of the Holstein Hamiltonian on a square lattice, a canonical system for electron-lattice physics. The resulting ML-enabled large-scale dynamical simulations faithfully capture mesoscale evolution of the symmetry-breaking phase, illustrating the utility of lattice-equivariant architectures for linking microscopic electronic processes to emergent dynamical behavior in condensed-matter lattice systems. Dynamical systems theory ; Lattice (music) ; Adiabatic process ; Homogeneous space ; Quadratic equation ; Artificial neural network ; Equivariant map ; Lattice model (finance) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Amir Karimdoost Yasuri
Source: Scientific Reports (article, 2026-02-23)
Field: Materials Science — Surfaces, Coatings and Films

Thirty years of contact angles reveal universal design rules for wetting control Wettability, commonly quantified by the static contact angle (θ), governs critical interfacial phenomena including anti-icing, self-cleaning, adhesion control, and lubrication. Although conceptual thresholds for superhydrophilic and superhydrophobic states are widely cited, their empirical validation across material classes has remained limited by the absence of a comprehensive, rigorously verified dataset spanning diverse surfaces and liquids. Here, we compile and systematically analyze 110 curated static contact-angle measurements reported between 1995 and 2025, encompassing polymers, metals, oxides, self-assembled monolayers (SAMs), and micro-/nano-textured surfaces measured with multiple probe liquids. Our meta-analysis quantitatively confirms the existence of universal critical thresholds, with θ ≲ 20° defining superhydrophilicity and θ ≳ 150° defining superhydrophobicity. Crucially, for textured and hierarchical surfaces, these limits emerge as geometry-dominated properties, persisting across material classes and largely independent of intrinsic surface chemistry. These validated thresholds establish clear, principle-driven design rules for engineering functional wetting behavior, moving beyond trial-and-error approaches. The resulting dataset provides a reliable benchmark for the community, supporting predictive wettability design, cross-study meta-analysis, and the development of data-driven and machine-learning models without the need for repetitive experimental measurements. Superhydrophilicity ; Wetting ; Benchmark (surveying) ; Contact angle ; Surface (topology) ; Adhesion Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Mechanical stress and fatigue analysis
Authors: J Charlie Maier | Nicholas E. Jackson
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Frontier-Orbital Predictions from Coarse-Grained Geometries with Physics-Constrained Neural Hamiltonians We introduce a physics-informed machine learning framework, Orbital Electronic Coarse-Graining (Orb-ECG), for predicting frontier molecular orbital energies at coarse-grained (CG) molecular resolutions. Orb-ECG constructs a realspace overlap matrix by projecting a Gaussian orbital basis onto the CG representation and employs a neural-networkparameterized expansion of this overlap matrix to reproduce quantum-chemistry-derived eigenvalues. This model is trained end-to-end using a composite loss enforcing energy accuracy, basis localization, sparsity, and smoothness. When evaluated on bithiophene conformations, Orb-ECG demonstrates improved data efficiency and extrapolation performance relative to a physics-agnostic ML benchmark while remaining competitive in the large-data limit. In addition to orbital energies, Orb-ECG provides a CG orbital basis that enables access to 3D CG pseudo-orbitals. These CG pseudo-orbitals reliably capture average population trends, but are limited in their ability to capture conformationinduced fluctuations in the electron density, motivating future work. These method developments establish a physically grounded avenue toward efficient and generalizable electronic structure modeling at CG molecular resolutions. Extrapolation ; Basis (linear algebra) ; Representation (politics) ; Molecular orbital ; Benchmark (surveying) ; Population ; Gaussian ; Matrix (chemical analysis) ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Shunming Yang | Yi Kong | Yuanyuan Zhu | Yongqi Lv | Jinglian Fan
Source: Journal of Materials Science (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

An accurate deep learning interatomic potential for TaC and its application in tensile deformation and dislocation mobility Interatomic potential ; Molecular dynamics ; Dislocation ; Deformation (meteorology) ; Stacking fault ; Grain boundary ; Ultimate tensile strength ; Vacancy defect ; Embedded atom model ; Stacking Microstructure and mechanical properties ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Abhinay Thakur | Taiwo W. Quadri | Valentine Chikaodili Anadebe | Titus A.M. Msagati | Eno E. Ebenso
Source: Next Materials (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Surface science perspectives on eco-friendly corrosion inhibitors The science of metallic degradation and its control have developed significantly over the past decades. Beginning with traditional corrosion inhibitors such as chromates, nitrates and phosphates which are well-known for their effectiveness and environmental hazards, the field has recorded great progress with the development and implementation of safer alternatives. This recent shift is premised on global regulatory measures, increasing environmental campaigns, promotion of green chemistry ideals and industrial demands. Latest advances in the field now promote the use of eco-friendly materials like bio-based ionic liquids, biopolymers, plant extracts and biosurfactants for corrosion protection. This review captures the surface and interfacial activities of these sustainable corrosion inhibitors which are extensively explored using cutting-edge experimental and computational techniques. Electrochemical insights, together with corrosion inhibition efficiencies ranging from 80 to 99 % for typical eco-friendly inhibitors have been obtained using electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) techniques. Modern analytical techniques including X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM) and scanning electron microscopy (SEM) have resulted in structural and mechanistic insights at the electrode/electrolyte interface. Recent advancements in molecular modelling and machine learning have further enhanced the understanding of inhibitor-metal interactions and rational design of eco-friendly inhibitors. Overall, this review succinctly highlights recent progress in eco-friendly inhibition approach, outlines green chemistry metrics, identifies key challenges and proposes future research directions to accelerate industrial implementation. • The perspectives of eco-friendly corrosion inhibitors were discussed. • The interfacial mechanism of eco-friendly corrosion inhibitors was highlighted. • The key factors and green metrics of eco-friendly corrosion inhibitors were discussed. • The mini-review provides a roadmap for next-generation materials for corrosion control. Corrosion ; Dielectric spectroscopy ; Corrosion inhibitor ; Polarization (electrochemistry) ; Human health ; Atomic force microscopy ; X-ray photoelectron spectroscopy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Marine Biology and Environmental Chemistry