jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyi Xu | Ning Huang
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multicomponent covalent organic frameworks: design strategies and synergistic functions Covalent organic frameworks (COFs) are crystalline porous polymers with modular architectures and long-range order, offering exceptional tunability in pore structure and functionality. While conventional COFs are typically constructed from one or two types of monomers, recent advances have led to the emergence of multicomponent COFs (MC-COFs), which integrate three or more distinct building blocks within a single lattice. This strategy enables the precise spatial arrangement of diverse geometries, connectivity, and functionalities, imparting synergistic complexity beyond the reach of single- or binary-component systems. In this perspective, we present a comprehensive overview of MC-COF chemistry, organised around five representative construction strategies: isostructural and heterostructural copolymerization, multicomponent topological design, multicomponent reactions, and pore partitioning strategies. We further highlight how these frameworks enable emergent functions in catalysis, alkane isomer separation, chemical sensing, and radiotherapy through rational control of both periodic backbone structure and local chemical environments. Despite these advances, challenges persist in structural characterization, expanding the scope of dynamic linkages, and achieving predictive design of emergent properties. Looking forward, the integration of dynamic covalent chemistry, topological programming, and machine learning-driven design is expected to unlock the full potential of MC-COFs as next-generation multifunctional materials. Covalent bond ; Chemistry ; Biochemical engineering ; Combinatorial chemistry ; Computer science ; Nanotechnology ; Engineering ; Organic chemistry ; Materials science Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Zhilong Wang | Wolfgang G. Zeier | Fengqi You
Source: Science Advances (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries Solid-state batteries (SSBs) are pivotal for sustainable energy storage, delivering extended life span, low-temperature resilience, and enhanced safety. However, designing stable solid electrolytes and interfaces in SSBs remains a formidable challenge. As a disruptive catalyst for paradigm shifts spanning materials discovery and energy system redesign, artificial intelligence (AI) is unleashing unprecedented possibilities-could it be the key breakthrough for SSB innovation? Here, we critically review the progress of AI applications in electrolyte and interface engineering, covering key aspects such as stability, conductivity, mechanical properties, and interface resistance. This work emphasizes the integration of cutting-edge modeling strategies, including the materials' screening pipelines, machine learning force fields, and generative models. Furthermore, we conduct an in-depth analysis of persistent challenges and propose a roadmap featuring multiscale modeling and multimodal models with physical constraints to build an intelligent ecosystem for SSB development. This review is expected to inspire interdisciplinary collaborations and drive forward energy materials design, ultimately accelerating the development of sustainable and cutting-edge battery technologies. Interface (matter) ; Key (lock) ; Battery (electricity) ; Sustainable energy ; Work (physics) ; Generative grammar ; Energy (signal processing) ; Efficient energy use Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Yannick Couzinié | Yuya Seki | Yusuke Nishiya | Hirofumi Nishi | Taichi Kosugi | Shu Tanaka | Yu‐ichiro Matsushita
Source: Journal of the Physical Society of Japan (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Machine Learning Supported Annealing for Prediction of Grand Canonical Crystal Structures Crystal structure prediction ; Annealing (glass) ; Grand canonical ensemble ; Materials science ; Computer science ; Crystal structure ; Statistical physics ; Condensed matter physics ; Physics ; Crystallography ; Mathematics ; Statistics ; Chemistry ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yujun Wang | Yahui Chen | Fang Wang | Sheng Lu | Xiaoqiang Chen
Source: Dyes and Pigments (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Photoresponsive single crystals of organic small molecules Molecule ; Organic molecules ; Chemistry ; Materials science ; Photochemistry ; Crystallography ; Organic chemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Luminescence and Fluorescent Materials
Authors: Lin Wang | Tanjin He | Bin Ouyang
Source: ACS Materials Letters (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. In this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain. Property (philosophy) ; Domain (mathematical analysis) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Philosophy ; Epistemology Machine Learning in Materials Science ; Neural Networks and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Siddarth K. Achar | Priyanka B. Shukla | Chinmay V. Mhatre | Leonardo Bernasconi | Caitlyn Y. Vinger | J. Karl Johnson
Source: Journal of Chemical Theory and Computation (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting Systems Discovering chemical reaction pathways using quantum mechanics is impractical for many systems of practical interest because of unfavorable scaling and computational cost. While machine learning interatomic potentials (MLIPs) trained on quantum mechanical data offer a promising alternative, they face challenges for reactive systems due to the need for extensive sampling of the potential energy surface in regions that are far from equilibrium geometries. Unfortunately, traditional MLIP training protocols are not designed for comprehensive reaction exploration. We present a reactive active learning (RAL) framework that is designed to efficiently train MLIPs to achieve near-quantum mechanical accuracy for reactive systems for situations where one may not have prior knowledge of the possible transition states, reaction pathways, or even the potential products. Our method combines automated reaction exploration, uncertainty-driven active learning, and transition state sampling to build accurate potentials. We demonstrate RAL's effectiveness across three different systems: uncatalyzed ammonia synthesis (gas-phase), methanimine hydrolysis (solution phase), and methane activation on titanium carbide surfaces (heterogeneous). The resulting MLIPs accurately predict reaction barriers and transition states. For catalysis, we show that RAL-trained MLIPs identify Ti<sub>2</sub>C as the most active methane activation surface (90% decomposition at 1000 K) through C-vacancy mediated mechanisms. The framework's ability to simulate large systems (∼900 atoms) over nanosecond time scales provides previously inaccessible insights into surface poisoning and reaction networks. We show that reactive exploration is essential for adequately capturing the potential energy surface, with chemical and configurational sampling working synergistically to improve model accuracy. Our results establish general guidelines for training robust reactive potentials and open new possibilities for computational discovery of catalysts and reaction mechanisms. Computer science ; Training (meteorology) ; Training set ; Active learning (machine learning) ; Machine learning ; Artificial intelligence ; Human–computer interaction ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Junyoung Choi | Byeongsun Jun | Yousung Jung
Source: Chemical Engineering Journal (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Reliable Li-ion conductivity with efficient data generation and uncertainty estimation toward large-scale screening Conductivity ; Estimation ; Scale (ratio) ; Ion ; Environmental science ; Computer science ; Materials science ; Chemistry ; Engineering ; Physics ; Systems engineering ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Yuchao Tang | Bin Xiao | Shuizhou Chen | Quan Qian | Yi Liu
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predefined attention-focused mechanism using center-environment features: a machine learning study of alloying effects on the stability of Nb <sub>5</sub> Si <sub>3</sub> alloys Machine learning prediction of substitution energies of Nb 5 Si 3 using the Center-Environment (CE) features with the environment atoms defined by the Atomic Environment Type (AET). Mechanism (biology) ; Stability (learning theory) ; Center (category theory) ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Liya Fu | Junkai Wang | Liyan Deng | K Cheng | Xiuwei Ao | Changyong Wu
Source: Environmental Science & Technology (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Bridging Dissolved Organic Matter Reactivity to Ozonation Catalysts for Cu@Al <sub>2</sub> O <sub>3</sub> from the Molecular Level by Machine Learning Catalytic ozonation is a widely used advanced oxidation process for treating refractory organic wastewater; yet, the variability in dissolved organic matter (DOM) composition complicates reaction mechanisms. A critical challenge lies in designing optimal catalysts tailored to wastewater characteristics, and this factor has seldom been systematically explored. Here, we integrated principal component analysis with correlation analysis to link wastewater properties to catalyst structural descriptors. Representative catalyst Cu@Al<sub>2</sub>O<sub>3</sub> was used to treat three refractory wastewaters via catalytic ozonation, revealing stark differences in total organic carbon removal efficiency (19.1%-58.6%). Fourier transform-ion cyclotron resonance-mass spectrometry uncovered molecular-level heterogeneity in refractory organics, while a random forest model classified removed, resistant, and produced molecules with accuracies of 67.3%-80.4%. Removed molecules were predominantly aromatic, heteroatom-rich (N, S), and high molecular weight (>400 Da). Statistical modeling identified the indicator UV absorbance at 254 nm (UV<sub>254</sub>) as a robust surrogate for wastewater characterization. Mechanistically, the oxygen vacancy concentration strongly correlated with CHOS compound removal (<i>r</i> = 0.998), while hindered the degradation of fluorescence region V components (<i>r</i> < -0.997). This study demonstrates a data-driven strategy of bridging molecular DOM profiling and catalyst descriptors, to guide the rational design of ozonation catalysts for targeted wastewater treatment. Catalysis ; Dissolved organic carbon ; Wastewater ; Absorbance ; Molecule ; Degradation (telecommunications) ; Organic matter ; Size-exclusion chromatography Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Miroslava Nedyalkova | Mahdi Vasighi | Marco Lattuada
Source: Chemosphere (article, 2025-04-14)
Field: Materials Science — Biomaterials

Integrating surface chemistry properties and machine learning to map the toxicity landscape of superparamagnetic iron oxide nanoparticles The relationship between Superparamagnetic Iron Oxide Nanoparticles (SPIONs) surface chemistry and their toxicological outcomes is crucial for biomedical applications, including drug delivery and imaging diagnostics. SPIONs' surface properties-such as size, shape, type of coating agents, and charge-are directly linked to their interactions with the biological environment, significantly affecting their toxicity. Surface chemistry plays a significant role in determining biocompatibility, cellular uptake, and the potential for adverse reactions. This study focuses on building a classification and prediction model based on the experimentally obtained properties and linked with the calculated molecular descriptors to describe the nature of the various coatings used for SPIONs in such a combined mode. The predictive model helps identify how specific surface modifications, including coating types and functional groups, influence toxicity responses. The results that were obtained, which correlate well with the existing literature, confirm the effects of surface chemistry on toxicity. For instance, the model accurately predicts that chitosan derivative coatings with a higher positive charge exhibit toxic potential, which aligns with previous findings. Incorporating these experimentally obtained surface features into a predictive framework enables the design of safer SPION formulations, enhancing therapeutic efficacy while managing surface chemistry's effects on toxicity. Superparamagnetism ; Nanoparticle ; Chemistry ; Nanotechnology ; Surface (topology) ; Iron oxide ; Iron oxide nanoparticles ; Toxicity ; Oxide ; Environmental chemistry ; Materials science ; Organic chemistry ; Physics ; Magnetization Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Olga Klimanova | Nikita Rybin | Alexander V. Shapeev
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning We present an algorithm for accelerating the search of a molecule's adsorption sites based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface and an active learning algorithm for the automatic construction of an optimal training dataset. To validate our methodology, we compare the results across various well-known catalytic systems with surfaces of different crystallographic orientations and adsorbate geometries, including CO/Pd(111), NO/Pd(100), NH<sub>3</sub>/Cu(100), C<sub>6</sub>H<sub>6</sub>/Ag(111), and CH<sub>2</sub>CO/Rh(211). In all the cases, we observed an agreement of our results with the literature. Molecule ; Interatomic potential ; Active learning (machine learning) ; Chemical physics ; Computer science ; Molecular physics ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Johnson Kehinde Abifarin | Yuerui Lu
Source: Materials Today Physics (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Statistics in enabling 2D materials: Optimization, predictive modelling, and data-driven discovery The rapid advancements in two-dimensional (2D) materials have revolutionized applications in energy storage, electronics, catalysis, and sensors. However, the conventional trial-and-error approaches in synthesis and property tuning often lead to inconsistencies, low reproducibility, and suboptimal performance. To address these challenges, statistical design of experiments (DOE) and machine learning (ML), and artificial intelligence (AI) assisted optimization have emerged as powerful tools to systematically correlate synthesis parameters with material properties, enabling predictive modelling and process control. This review explores the integration of statistical methodologies such as the Taguchi method, Response Surface Methodology (RSM), and Principal Component Analysis (PCA) in optimizing synthesis routes and engineering desirable properties in 2D materials. It provides an in-depth analysis of statistical approaches applied in hydrothermal synthesis, chemical vapor deposition (CVD), electrochemical exfoliation, and intercalation studies, linking processing conditions to crystallite size, interlayer spacing, defects, and surface area. Furthermore, the synergy between statistical modelling and AI-driven material informatics is discussed, highlighting its potential in accelerating the discovery of next-generation functional 2D materials. By bridging the gap between experimental design and computational optimization, this review underscores the transformative impact of data-driven approaches in enhancing reproducibility, efficiency, and scalability in 2D materials research. Materials science ; Data science ; Statistics ; Computer science ; Mathematics 2D Materials and Applications ; Graphene research and applications ; Advanced Photocatalysis Techniques
Authors: Jiehai Peng | Shuting Shen | Wei Zhu | Chao Wu | Lin Huang | Mingzi Li | Jiamin Zhong | Nan Zhou | Shanjiang Xue | Kui Du | Zhao Chen
Source: Journal of Chemical Information and Modeling (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Ground-State Descriptor Enables Machine Learning-Assisted Virtual Screening of AIE-Active Mechanofluorochromic Molecules with High Contrast Aggregation-induced emission mechanofluorochromic (AIE-MFC) molecules with high-contrast are in high demand for pressure-sensing devices and optoelectronic devices. However, developing AIE-MFC molecules with high-contrast beyond 100 nm still highly relies on scientific intuition through experimental and traditional trial-and-error methods. Herein, we establish a new electronic descriptor E<sub>DAHL</sub> (the orbital energy of the donor and acceptor under ground-state) to characterize mechanofluorochromic (MFC) properties (preground and postground fluorescence emission wavelengths Oλ<sub>em,max</sub> and Gλ<sub>em,max</sub>). And an MFC property prediction model with high prediction accuracy and less computational complexity is established by combining E<sub>DAHL</sub> with molecular descriptors as inputs. Subsequently, we constructed a closed-loop approach that integrates machine learning (ML), density functional theory (DFT), high-throughput virtual screening (HTVS), and experiment to accelerate the discovery of AIE-MFC molecules with high contrast in a large space. After processing, 37 new high-contrast AIE-MFC molecules beyond 100 nm are rapidly screened out of more than 10000 candidates. Notably, experiments were used to further confirm the reliability of the workflow, opening an avenue for screening of high-contrast AIE-MFC molecule. We believe the new electronic descriptor may be extended to discover other molecules constructed from donors and acceptors with different properties. Contrast (vision) ; Virtual screening ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Drug discovery Luminescence and Fluorescent Materials ; Fluorine in Organic Chemistry ; Pharmacological Receptor Mechanisms and Effects
Authors: Yu Yang | Xinlu Liu | Fei Huang | Qingyu Huang | Chuang Yang | Yongjun Zhang
Source: Chemical Engineering Journal (article, 2025-04-10)
Field: Chemistry — Organic Chemistry

Electrodeposited FeNi3 alloy on 3D-printed monoliths as a stable catalyst for ofloxacin degradation in wastewater Degradation (telecommunications) ; Wastewater ; Ofloxacin ; Alloy ; Catalysis ; Materials science ; Chemistry ; Waste management ; Chemical engineering ; Metallurgy ; Environmental science ; Environmental engineering ; Organic chemistry ; Engineering ; Electrical engineering Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Environmental remediation with nanomaterials
Authors: Malte Grunert | Max Großmann | Erich Runge
Source: Small (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Discovery of Sustainable Energy Materials Via the Machine‐Learned Material Space Does a machine learning (ML) model capture the intrinsic structure of the material space? The example of the OptiMate model, a graph attention network trained to predict the optical properties of semiconductors and insulators, provides an affirmative answer. By applying the UMAP dimensionality reduction technique to its latent embeddings, it is demonstrated that the model captures a nuanced and interpretable representation of the materials space, reflecting chemical and physical principles, without any user-induced bias. This enables clustering of almost 10,000 materials based on optical properties and chemical similarities. Furthermore, it is shown how the learned material space can be used to identify more sustainable alternatives to critical materials in energy-related technologies, such as photovoltaics. These findings demonstrate the dual utility of ML models in materials science: Accurately predicting material properties while providing insights into the underlying materials space. The approach demonstrates the broader potential of leveraging learned materials spaces for the discovery and design of materials for diverse applications, and is easily applicable to any state-of-the-art ML model. Computer science ; Dimensionality reduction ; Cluster analysis ; Curse of dimensionality ; Space (punctuation) ; Chemical space ; Photovoltaics ; Representation (politics) ; Artificial intelligence ; Machine learning ; Photovoltaic system ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Ferroelectric and Negative Capacitance Devices
Authors: Zheng Wu | Zheng Wang | Jian Zeng | Minglei Zhang | Kang Yao | Xiaoya Chen | Quanan Li | Baosheng Liu
Source: Journal of Magnesium and Alloys (article, 2025-10-16)
Field: Materials Science — Biomaterials

Insight into the hot deformation behavior and recrystallization mechanism of Mg-Y-RE alloys based on machine learning Recrystallization (geology) ; Dynamic recrystallization ; Deformation (meteorology) ; Mechanism (biology) ; Deformation mechanism Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming