jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jaesun Kim | Jisu Kim | Jaehoon Kim | Jiho Lee | Yutack Park | Youngho Kang | Seungwu Han
Source: Journal of the American Chemical Society (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from <i>ab initio</i> calculations, providing near-quantum-level accuracy with reduced computational costs. However, the high cost of assembling high-fidelity databases hampers the application of MLIPs to systems that require high chemical accuracy. Utilizing an equivariant graph neural network, we present an MLIP framework that trains on multifidelity databases simultaneously. This approach enables the accurate learning of high-fidelity PES with minimal high-fidelity data. Employing the generalized gradient approximation (GGA) and meta-GGA as low- and high-fidelity approaches, respectively, we tested this framework on the Li<sub>6</sub>PS<sub>5</sub>Cl and In<sub><i>x</i></sub>Ga<sub>1-<i>x</i></sub>N systems. The results show that using a high-fidelity training set with a size approximately 10% of the low-fidelity set, the multifidelity training framework achieves excellent accuracy, with Li-ion conductivity predictions within 10% error and In<sub><i>x</i></sub>Ga<sub>1-<i>x</i></sub>N mixing energy showing an <i>R</i><sup>2</sup> of 0.98 compared to the reference high-fidelity MLIP results. It indicates that geometric and compositional spaces not covered by the high-fidelity meta-GGA database can be effectively inferred from low-fidelity GGA data, thus enhancing accuracy and molecular dynamics stability. We also developed a general-purpose MLIP that utilizes both GGA and meta-GGA data from the Materials Project, significantly enhancing MLIP performance for high-accuracy tasks such as predicting energies above hull for crystals in general. Furthermore, we demonstrate that the present multifidelity learning is more effective than transfer learning or Δ-learning and that it can also be applied to learn higher-fidelity up to the coupled-cluster level. We believe this methodology holds promise for creating highly accurate bespoke or universal MLIPs by effectively expanding the high-fidelity data set. Chemistry ; Training (meteorology) ; Fidelity ; Training set ; Machine learning ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Domain Adaptation and Few-Shot Learning
Authors: Guo Wang | Zhiqiang Ma | Zhuo Chen | Haojun Hua | Dong Wang | Mohamed Elhousseini Hilal | Yatian Fu | Pengyi Lu | Jian Lü | Yuan‐Ting Zhang | Derek Ho | Bee Luan Khoo
Source: Chemical Engineering Journal (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Thin and soft Ti3C2Tx MXene sponge structure for highly sensitive pressure sensor assisted by deep learning Materials science ; Polydimethylsiloxane ; Pressure sensor ; Polymer ; Nanotechnology ; Inert ; Surface modification ; Nanomaterials ; Composite material ; Thermoplastic polyurethane ; Composite number ; Elastomer ; Chemical engineering ; Mechanical engineering ; Chemistry MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Anton Bochkarev | Yury Lysogorskiy | Ralf Drautz
Source: Physical Review X (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing The atomic cluster expansion provides local, complete basis functions that enable efficient parametrization of many-atom interactions. We extend the atomic cluster expansion to incorporate graph basis functions. This naturally leads to representations that enable the efficient description of semilocal interactions in physically and chemically transparent form. Simplification of the graph expansion by tensor decomposition results in an iterative procedure that comprises current message-passing machine learning interatomic potentials. We demonstrate the accuracy and efficiency of the graph atomic cluster expansion for a number of small molecules, clusters, and a general-purpose model for carbon. We further show that the graph atomic cluster expansion scales linearly with the number of neighbors and layer depth of the graph basis functions. Published by the American Physical Society 2024 Equivariant map ; Graph ; Cluster (spacecraft) ; Computer science ; Message passing ; Cluster expansion ; Mathematics ; Theoretical computer science ; Physics ; Parallel computing ; Programming language ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Qijun Tang | Tianhao Li | Wenguang Tu | Haiqiang Wang | Yong Zhou | Zhigang Zou
Source: Advanced Functional Materials (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Recent Advances in Diverse MXenes‐Based Structures for Photocatalytic CO<sub>2</sub> Reduction into Renewable Hydrocarbon Fuels Abstract Photocatalytic CO 2 reduction into renewable hydrocarbon fuels is a green solution to address CO 2 emission and energy issues simultaneously. However, the fast recombination of photogenerated charge carriers and the sluggish surface reaction kinetics restrict the efficiency of photocatalytic CO 2 reduction. The emergence of 2D MXenes has potential in improving the efficiency of photocatalytic CO 2 reduction, owing to their high electrical conductivity, flexible structural properties, and abundant active sites. Hence, this review will concisely summarize and highlight recent advances of MXenes‐based photocatalysts used in photocatalytic CO 2 reduction. First, the synthesis and properties of MXenes is briefly introduced. Second, the mechanism of CO 2 photoreduction along with the roles of MXenes in photocatalytic CO 2 reduction are summarized, including promoting the adsorption of CO 2 , enhancing the separation of photo‐induced charge carriers, acting as a robust support, and photothermal effect. Third, different kinds of MXenes‐based photocatalysts such as MXenes/metal oxides, MXenes/nitrides, MXenes/LDH, MXenes/perovskite, and MXene‐derived photocatalysts for CO 2 reduction are classified via the type of semiconductors. Finally, the challenges and perspectives are also presented, including exploring suitable MXenes via machine learning, uncovering the structure‐activity relationship by in situ, time‐ and space‐resolved characterization techniques, improving anti‐oxidization ability, and scale‐up applications. MXenes ; Photocatalysis ; Materials science ; Nanotechnology ; Charge carrier ; Chemistry ; Optoelectronics ; Catalysis ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Xinyi Dong | Ying Dong | Jinming Liu | Chunqi Wang | Changhao Bao | Na Wang | Xiaoyu Zhao | Zhengguang Chen
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-08-06)
Field: Chemistry — Analytical Chemistry

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics Chemometrics ; Partial least squares regression ; Chemistry ; Calibration ; Support vector machine ; Feature selection ; Residual ; Pattern recognition (psychology) ; Wheat flour ; Biological system ; Artificial intelligence ; Chromatography ; Statistics ; Computer science ; Algorithm ; Mathematics ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kai Liu | Shibo Jiao | Guangbo Nie | Hui Ma | Bo Gao | Chuanming Sun | Dongli Xin | Tapan Kumar Saha | Guangning Wu
Source: High Voltage (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains Abstract Partial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods. Train ; Partial discharge ; Transformation (genetics) ; Identification (biology) ; Computer science ; Image (mathematics) ; Acoustics ; Engineering ; Electrical engineering ; Artificial intelligence ; Geography ; Physics ; Cartography ; Voltage High voltage insulation and dielectric phenomena ; Advanced Measurement and Detection Methods ; Embedded Systems and FPGA Design
Authors: Wei Zhao | Baohu Wu | Zhouyue Lei | Peiyi Wu
Source: Angewandte Chemie International Edition (article, 2024-03-28)
Field: Materials Science — Biomaterials

Hydrogels with Differentiated Hydrogen‐Bonding Networks for Bioinspired Stress Response Stress response, an intricate and autonomously coordinated reaction in living organisms, holds a reversible, multi-path, and multi-state nature. However, existing stimuli-responsive materials often exhibit single-step and monotonous reactions due to the limited integration of structural components. Inspired by the cooperative interplay of extensor and flexor cells within Mimosa's pulvini, we present a hydrogel with differentiated hydrogen-bonding (H-bonding) networks designed to enable the biological stress response. Weak H-bonding domains resemble flexor cells, confined within a hydrophobic network stabilized by strong H-bonding clusters (acting like extensor cells). Under external force, strong H-bonding clusters are disrupted, facilitating water diffusion from the bottom layer and enabling transient expansion pressure gradient along the thickness direction. Subsequently, water diffuses upward, gradually equalizing the pressure, while weak H-bonding domains undergo cooperative elastic deformation. Consequently, the hydrogel autonomously undergoes a sequence of reversible and pluralistic motion responses, similar to Mimosa's touch-triggered stress response. Intriguingly, it exhibits stress-dependent color shifts under polarized light, highlighting its potential for applications in time-sensitive "double-lock" information encryption systems. This work achieves the coordinated stress response inspired by natural tissues using a simple hydrogel, paving the way for substantial advancements in the development of intelligent soft robots. Self-healing hydrogels ; Hydrogen bond ; Stress (linguistics) ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Polymer chemistry ; Molecule ; Organic chemistry ; Engineering Silk-based biomaterials and applications ; Hydrogels: synthesis, properties, applications ; biodegradable polymer synthesis and properties
Authors: Abdelkarim Chaouiki | Maryam Chafiq | Young Gun Ko
Source: Environmental Research (article, 2024-01-04)
Field: Chemistry — Inorganic Chemistry

Unveiling the mechanisms behind high CO2 adsorption by the selection of suitable ionic liquids incorporated into a ZIF-8 metal organic framework: A computational approach Adsorption ; Metal-organic framework ; Ionic liquid ; Selection (genetic algorithm) ; Environmental science ; Chemical engineering ; Metal ; Environmental chemistry ; Chemistry ; Materials science ; Biochemical engineering ; Computer science ; Organic chemistry ; Engineering ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Kang Xia | Takafumi Yatabe | Kentaro Yonesato | Soichi Kikkawa | Seiji Yamazoe | Ayako Nakata | Ryo Ishikawa | Naoya Shibata | Yuichi Ikuhara | Kazuya Yamaguchi | Kosuke Suzuki
Source: Nature Communications (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Ultra-stable and highly reactive colloidal gold nanoparticle catalysts protected using multi-dentate metal oxide nanoclusters Owing to their remarkable properties, gold nanoparticles are applied in diverse fields, including catalysis, electronics, energy conversion and sensors. However, for catalytic applications of colloidal gold nanoparticles, the trade-off between their reactivity and stability is a significant concern. Here we report a universal approach for preparing stable and reactive colloidal small (~3 nm) gold nanoparticles by using multi-dentate polyoxometalates as protecting agents in non-polar solvents. These nanoparticles exhibit exceptional stability even under conditions of high concentration, long-term storage, heating and addition of bases. Moreover, they display excellent catalytic performance in various oxidation reactions of organic substrates using molecular oxygen as the sole oxidant. Our findings highlight the ability of inorganic multi-dentate ligands with structural stability and robust steric and electronic effects to confer stability and reactivity upon gold nanoparticles. This approach can be extended to prepare metal nanoparticles other than gold, enabling the design of novel nanomaterials with promising applications. Nanoclusters ; Catalysis ; Colloidal gold ; Nanoparticle ; Reactivity (psychology) ; Nanomaterials ; Materials science ; Nanotechnology ; Colloid ; Oxide ; Metal ; Chemical engineering ; Chemistry ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Catalytic Processes in Materials Science
Authors: Simpy Sanyal | SeonJu Park | Ramachandran Chelliah | Su‐Jung Yeon | Kaliyan Barathikannan | Selvakumar Vijayalakshmi | Ye-Jin Jeong | Momna Rubab | Deog H. Oh
Source: Coatings (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Emerging Trends in Smart Self-Healing Coatings: A Focus on Micro/Nanocontainer Technologies for Enhanced Corrosion Protection Smart self-healing coatings offer a revolutionary approach to mitigating metal corrosion, a problem with significant economic and environmental impacts. Divided into intrinsic and extrinsic types, these coatings autonomously rectify the damage. Intrinsic variants utilize reversible bonds to achieve ongoing repair, while extrinsic ones incorporate micro/nanocontainers that activate upon environmental triggers to mend micro-cracks, their efficacy dictated by the encapsulated healing agents’ volume. This review dissects the rapidly evolving sector of stimuli-responsive self-healing coatings, emphasizing the progress in micro/nano container technology. It discusses the synthesis and encapsulation processes of different micro/nanocontainers and charts the transition from single to multistimulus-responsive systems, which enhances the coatings’ sensitivity and functionality. The addition of multifunctional traits such as self-reporting and anti-microbial actions further broadens their industrial applicability. The review provides a succinct overview of the field’s current state and future potential, envisioning a paradigm shift in corrosion protection through advanced smart coatings. Corrosion ; Self-healing ; Nanotechnology ; Focus (optics) ; Corrosion prevention ; Materials science ; Engineering ; Metallurgy ; Medicine ; Physics Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Surface Modification and Superhydrophobicity
Authors: Guozhen Zhang | Fuhai Chen | Yi‐Ming Di | S. C. Yuan | Yang Zhang | Xin Quan | Yong Chen | Hongming Chen | Mei‐Jin Lin
Source: Advanced Functional Materials (article, 2024-04-21)
Field: Materials Science — Materials Chemistry

Guest‐Induced Thermally Activated Delayed Fluorescence Organic Supramolcular Macrocycle Scintillators for High‐Resolution X‐Ray Imaging Abstract Organic scintillators, pivotal in security and medical applications, face challenges due to limited X‐ray absorption and exciton utilization. Herein, a novel class of organic scintillators is introduced, named guest‐induced thermally activated delayed fluorescence (TADF) within supramolecular macrocycles via host‐guest through‐space charge transfer (TSCT). Four co‐crystals are obtained through orthogonal crystallizations involving calix[3]acridan (C[3]A) and calix[3]phenothiazine (C[3]P) macrocycles as hosts, along with 1,2‐dicyanobenzene (DCB) and 4‐bromo‐1,2‐benzenedicarbonitrile (BrDCB) as guests. Interestingly, DCB@C[3]A and BrDCB@C[3]A co‐crystals exhibit strong host‐guest TSCT with reduced single‐triplet energy gap for efficient TADF emission, which leads to enhanced exciton utilization and X‐ray absorption, yielding radioluminescence intensities over 29 and 25 times higher than C[3]A. Similarly, substituting C[3]A with C[3]P, the obtained TADF co‐crystals also outperform C[3]P in scintillation performance. Additionally, the scintillation color of co‐crystals can be adjusted by varying the electron‐donating abilities of macrocycles and the electron‐accepting abilities of guests, offering a simpler color‐tuning mechanism than covalent‐bonded scintillators. Furthermore, the flexible film based on DCB@C[3]A exhibits promising application in X‐ray radiography, showcasing a high spatial resolution of 20 lp mm −1 @MTF = 0.77. The innovative strategy of fabricating organic scintillators via reversible non‐covalent interactions presents a novel solution for designing color‐tunable and high‐performance scintillators. Scintillator ; Materials science ; Fluorescence ; Absorption (acoustics) ; Scintillation ; Exciton ; Supramolecular chemistry ; Covalent bond ; Radioluminescence ; Optoelectronics ; Photochemistry ; Crystallography ; Crystal structure ; Optics ; Chemistry ; Detector ; Physics ; Organic chemistry Luminescence and Fluorescent Materials ; Perovskite Materials and Applications ; Atomic and Subatomic Physics Research
Authors: Yuanjun Chen | Ruixue Zhang | Zhiwen Chen | Jiangwen Liao | Xuedong Song | Xiao Liang | Yu Wang | Juncai Dong | Chandra Veer Singh | Dingsheng Wang | Yadong Li | F. Dean Toste | Jie Zhao
Source: Journal of the American Chemical Society (article, 2024-04-07)
Field: Chemistry — Organic Chemistry

Heterogeneous Rhodium Single-Atom-Site Catalyst Enables Chemoselective Carbene N–H Bond Insertion Transition-metal-catalyzed carbene insertion reactions of a nitrogen-hydrogen bond have emerged as robust and versatile methods for the construction of C-N bonds. While significant progress of homogeneous catalytic metal carbene N-H insertions has been achieved, the control of chemoselectivity in the field remains challenging due to the high electrophilicity of the metal carbene intermediates. Herein, we present an efficient strategy for the synthesis of a rhodium single-atom-site catalyst (Rh-SA) that incorporates a Rh atom surrounded by three nitrogen atoms and one phosphorus atom doped in a carbon support. This Rh-SA catalyst, with a catalyst loading of only 0.15 mol %, exhibited exceptional catalytic performance for heterogeneous carbene insertion with various anilines and heteroaryl amines in combination with diazo esters. Importantly, the heterogeneous catalyst selectively transformed aniline derivatives bearing multiple nucleophilic moieties into single N-H insertion isomers, while the popular homogeneous Rh<sub>2</sub>(OAc)<sub>4</sub> catalyst produced a mixture of overfunctionalized side products. Additionally, similar selectivities for N-H bond insertion with a set of stereoelectronically diverse diazo esters were obtained, highlighting the general applicability of this heterogeneous catalysis approach. On the basis of density functional theory calculations, the observed selectivity of the Rh-SA catalyst was attributed to the insertion barriers and the accelerated proton transfer assisted by the phosphorus atom in the support. Overall, this investigation of heterogeneous metal-catalyzed carbene insertion underscores the potential of single-atom-site catalysis as a powerful and complementary tool in organic synthesis. Carbene ; Chemistry ; Catalysis ; Diazo ; Migratory insertion ; Rhodium ; Insertion reaction ; Nucleophile ; Homogeneous catalysis ; Chemoselectivity ; Electrophile ; Combinatorial chemistry ; Photochemistry ; Medicinal chemistry ; Organic chemistry Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Markus J. Buehler
Source: ACS Engineering Au (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis. Computer science ; Generative grammar ; Artificial intelligence ; Domain knowledge ; Data science ; Generative model ; Machine learning Machine Learning in Materials Science ; Topic Modeling ; Ferroelectric and Negative Capacitance Devices