jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhi Ma | Peng Cui | Xu Wang | Lanyu Li | Haoxiang Xu | Adrian C. Fisher | Daojian Cheng
Source: Chinese Journal of Chemical Engineering (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design Throughput ; Catalysis ; Computer science ; Engineering ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Haoyi Tan | Yukun Teng | Guangcun Shan
Source: npj Computational Materials (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks The removal of leaked radioactive iodine isotopes in humid air environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and machine learning were combined to reveal the iodine capture performance of 1816 metal-organic framework (MOF) materials under humid air conditions. Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. In addition to 6 structural features, 25 molecular features (encompassing the types of metal and ligand atoms as well as bonding modes) and 8 chemical features (including heat of adsorption and Henry’s coefficient) were incorporated to enhance the prediction accuracy of the machine learning algorithms. Feature importance was assessed to determine the relative influence of various features on iodine adsorption performance, in which the Henry’s coefficient and heat of adsorption to iodine were found the two most crucial chemical factors. Furthermore, four types of molecular fingerprints were introduced for providing comprehensive and detailed structural information of MOF materials. The 20 most significant Molecular ACCess Systems (MACCS) bits were picked out, revealing that the presence of six-membered ring structures and nitrogen atoms in the MOF framework were the key structural factors that enhanced iodine adsorption, followed by the presence of oxygen atoms. This work combined high-throughput computation, machine learning, and molecular fingerprints to comprehensively and systematically elucidate the multifaceted factors governing the iodine adsorption performance of MOFs in humid environments, establishing a robust and profound guideline framework for accelerating the screening and targeted design of high-performance MOF materials. Throughput ; Computer science ; Artificial intelligence ; Machine learning ; High-throughput screening ; Metal-organic framework ; Chemistry ; Organic chemistry ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Maksym Szemer | Szymon Buchaniec | Tomasz Prokop | Grzegorz Brus
Source: Energy and AI (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Topology-informed machine learning for efficient prediction of solid oxide fuel cell electrode polarization Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming electrode microstructure into a format compatible with artificial neural networks. Input data may range from a comprehensive digital material representation of the electrode to a selected set of microstructural parameters. The chosen representation significantly influences the performance and results of the network. Here, we show a novel approach utilizing persistence representation derived from computational topology. Using 500 microstructures and current–voltage characteristics obtained with three-dimensional first-principles simulations, we have prepared an artificial neural network model that can replicate current–voltage characteristics of unseen microstructures based on their persistent image representation. The artificial neural network can accurately predict the polarization curve of solid oxide fuel cell electrodes. The presented method incorporates complex microstructural information from the digital material representation while requiring substantially less computational resources (preprocessing and prediction time ≈ 1 min ) compared to our high-fidelity simulations (simulation time ≈ 1 h ) to obtain a single current-potential characteristic for one microstructure. • A machine learning model predicts solid oxide cells’ electrode performance. • The model uses topological descriptors to represent electrode microstructure. • Topology-informed machine learning allows capturing complex microstructure. Polarization (electrochemistry) ; Electrode ; Solid oxide fuel cell ; Topology (electrical circuits) ; Fuel cells ; Oxide ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Chemical engineering ; Electrical engineering ; Engineering ; Chemistry ; Metallurgy ; Physical chemistry Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and ELM
Authors: Pranjit Borah | Saptarshi Roy | Md. Ahmaruzzaman
Source: Advances in Colloid and Interface Science (review, 2025-07-22)
Field: Chemistry — Inorganic Chemistry

The fusion of metal-organic framework (MOF) and covalent organic framework (COF): A synergistic leap toward bridging boundaries in catalytic, sensing, and biomedical frontiers Bridging (networking) ; Metal-organic framework ; Covalent organic framework ; Covalent bond ; Nanotechnology ; Catalysis ; Chemistry ; Materials science ; Organic chemistry ; Computer science ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Cong Fang | Zhuang Wang | Ruili Guo | Yuxiao Ding | Sicong Ma | Xiaoyan Sun
Source: Journal of the American Chemical Society (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Machine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions Determining hydrogen positions in metal hydride clusters remains a formidable challenge, which relies heavily on unaffordable neutron diffraction. While machine learning has shown promise, only one deep learning-based method has been proposed so far, which relies heavily on neutron diffraction data for training, limiting its general applicability. In this work, we present an innovative strategy─SSW-NN (stochastic surface walking with neural network)─a robust, non-neutron diffraction-dependent technique that accurately predicts hydrogen positions. Validated against neutron diffraction data for copper hydride clusters, SSW-NN proved effective for clusters where only X-ray diffraction data or DFT predictions are available. It offers superior accuracy, efficiency, and versatility across different metal hydrides, including silver and alloy hydride systems, currently without any neutron diffraction references. This approach not only establishes a new research paradigm for metal hydride clusters but also provides a universal solution for hydrogen localization in other research fields constrained by neutron sources. Neutron diffraction ; Hydride ; Diffraction ; Chemistry ; Neutron ; Hydrogen ; Artificial neural network ; Crystallography ; Physics ; Nuclear physics ; Computer science ; Optics ; Artificial intelligence ; Crystal structure Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Baojun Pan | Lijie Zhang | Shangui Lan | Yukun Hong | Jieyuan Liang | Li-sheng Deng | Takashi Taniguchi | Kenji Watanabe | Xu Gao | Jianlong Xu | Ya‐Nan Zhong | Jinshui Miao | Yu Ye | Lain‐Jong Li | Shun Wang | Weida Hu | Sui‐Dong Wang | Peijian Wang
Source: Advanced Functional Materials (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

General Synthesis Strategy of 2D Transition Metal Dichalcogenide Gradient Alloys for High‐Performance Optoelectronic Synapses Abstract 2D transition metal dichalcogenide (TMD) gradient alloys hold great promise for electronic and optoelectronic applications, benefiting from their structural and compositional diversity as well as potential multifunctionalities. However, critical challenges remain in the controlled synthesis and unexplored uses of 2D TMD gradient alloys compared to single‐composition 2D TMDs. Herein, a “Face‐to‐Face” general synthesis strategy, which enables large‐area, uniform, and highly reproducible preparation of multiple types of 2D TMD gradient alloys in a single configuration through precise control on precursor supply, and the spatial confinement effect is reported. The synthesized single‐layer Mo 1‐x W x S 2 gradient alloy features a graded energy band and an alloyed region with moderate intrinsic sulfur vacancies, which is beneficial to achieving the high photo‐synaptic response. Optoelectronic synaptic devices utilizing the unique properties of 2D gradient alloys demonstrate various neuromorphic behaviors, such as outstanding‐index 161% paired‐pulse facilitation, stimuli‐dependent transition from short‐term plasticity to long‐term plasticity, emulation of the brain‐like learning processes, and simulation of neuromorphic vision on artificial neural network for handwritten digit recognition with over 95.6% accuracy. The present general synthesis method of 2D gradient alloys and their great potential as high‐performance optoelectronic synapses may open new frontiers for the 2D TMD family. Materials science ; Transition metal ; Optoelectronics ; Nanotechnology ; Catalysis 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Mahmoud Makki Abadi | Mohammad Mehdi Rashidi
Source: Processes (review, 2025-03-16)
Field: Materials Science — Materials Chemistry

Machine Learning for the Optimization and Performance Prediction of Solid Oxide Electrolysis Cells: A Review Solid oxide electrolysis cells (SOECs) represent a promising technology because they have the potential to achieve greater efficiency than existing electrolysis methods, making them a strong candidate for sustainable hydrogen production. SOECs utilize a solid oxide electrolyte, which facilitates the migration of oxygen ions while maintaining gas impermeability at temperatures between 600 °C and 900 °C. This review provides an overview of the recent advancements in research and development at the intersection of machine learning and SOECs technology. It emphasizes how data-driven methods can improve performance prediction, facilitate material discovery, and enhance operational efficiency, with a particular focus on materials for cathode-supported cells. This paper also addresses the challenges associated with implementing machine learning for SOECs, such as data scarcity and the need for robust validation techniques. This paper aims to address challenges related to material degradation and the intricate electrochemical behaviors observed in SOECs. It provides a description of the reactions that may be involved in the degradation mechanisms, taking into account thermodynamic and kinetic factors. This information is utilized to construct a fault tree, which helps categorize various faults and enhances understanding of the relationship between their causes and symptoms. Electrolysis ; Computer science ; Oxide ; Materials science ; Machine learning ; Chemical engineering ; Artificial intelligence ; Chemistry ; Metallurgy ; Engineering ; Electrode ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Zijing Wang | Rahil Changotra | Mita Dasog | Gurpreet Singh Selopal | Jie Yang | Quan He
Source: Sustainable materials and technologies (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Carbon quantum dots: Synthesis via hydrothermal processing, doping strategies, integration with photocatalysts, and their application in photocatalytic hydrogen production Carbon quantum dots (CQDs), a quasi-spherical carbon-based nanomaterial, have attracted great attention in photocatalysis due to their unique optical and electrochemical properties, such as tunable fluorescence emission, up-conversion property, and the ability to accelerate charge separation. Significant efforts have been made to synthesize, dope, and surface-functionalize CQDs to tune their photo-physical/chemical properties. Hydrothermal (HT) processes provide a clean, cost-effective, and efficient synthesis method to prepare CQDs and their derivatives, through hydrolysis, polymerization, and carbonization reactions. This review summarizes the synthesis of HT-CQDs using various precursors, followed by evaluating the CQDs doping and heterostructure formation strategies based on their fundamental characteristics (e.g., morphology, crystallinity, absorption ability, and photoluminescent properties). Doped CQDs exhibit enhanced optical properties, improved charge transfer efficiency, better electron mobility, and increased photocatalytic activity through introducing new surface states and active sites. Decorating traditional photocatalysts with CQDs improves light absorption and charge separation, significantly boosting the overall photocatalytic activity. The performance of CQD-based photocatalysts in hydrogen (H 2 ) evolution is systematically evaluated as well. CQDs enhance photocatalytic H 2 generation by acting as photosensitizers and/or electron mediators, accelerating the separation of e − -h + pairs and reducing recombination rates. This review highlights the significant advancement in CQDs synthesis, and doping and decorating strategies, showcasing their pivotal role in improving the photocatalytic efficiency of H 2 production. However, limited understanding of mechanisms, precise control over doping and surface functionalization, and scalability of synthesis methods remain key challenges for CQD-based photocatalysts. In the future, integrating artificial intelligence (AI) and machine learning (ML) tools with advanced characterization techniques might help the development of CQD-based photocatalysts, enabling precise property tuning and scalable synthesis approaches. Photocatalysis ; Carbon quantum dots ; Quantum dot ; Hydrogen production ; Hydrothermal circulation ; Hydrothermal synthesis ; Materials science ; Carbon fibers ; Doping ; Hydrogen ; Nanotechnology ; Chemical engineering ; Chemistry ; Catalysis ; Optoelectronics ; Composite number ; Composite material ; Engineering ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Caching and Content Delivery
Authors: Jenna A. Bilbrey | Jesun Firoz | Michael Ka Ho Lee | Sutanay Choudhury
Source: npj Computational Materials (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Uncertainty quantification for neural network potential foundation models Abstract For neural network potentials (NNPs) to gain widespread use, researchers must be able to trust model outputs. However, the blackbox nature of neural networks and their inherent stochasticity are often deterrents, especially for foundation models trained over broad swaths of chemical space. Uncertainty information provided at the time of prediction can help reduce aversion to NNPs. In this work, we detail two uncertainty quantification (UQ) methods. Readout ensembling, by finetuning the readout layers of an ensemble of foundation models, provides information about model uncertainty, while quantile regression, by replacing point predictions with distributional predictions, provides information about uncertainty within the underlying training data. We demonstrate our approach with the MACE-MP-0 model, applying UQ to the foundation model and a series of finetuned models. The uncertainties produced by the readout ensemble and quantile methods are demonstrated to be distinct measures by which the quality of the NNP output can be judged. Foundation (evidence) ; Artificial neural network ; Computer science ; Artificial intelligence ; History ; Archaeology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: K. A. L. Lima | D. A. da Silva | Georges Daniel Amvame Nze | Fábio Lúcio Lópes de Mendonça | Marcelo Lopes Pereira | Luiz Antônio Ribeiro
Source: Journal of Energy Storage (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Structural, electronic, and Li-ion adsorption properties of PolyPyGY explored by first-principles and machine learning simulations: A new multi-ringed 2D carbon allotrope Adsorption ; Carbon fibers ; Ion ; Electronic structure ; Materials science ; Chemical physics ; Nanotechnology ; Computer science ; Physical chemistry ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Ting Liang | Wenwu Jiang | Ke Xu | H. Bu | Zheyong Fan | Wengen Ouyang | Jianbin Xu
Source: Journal of Applied Physics (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations Machine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials. However, extracting phonon-mode-resolved insights from these simulations remains a critical challenge. Here, we introduce pysed, a Python-based package built on the spectral energy density (SED) method, designed to efficiently compute kinetic-energy-weighted phonon dispersion and extract phonon lifetime from large-scale MD simulation trajectories. By integrating high-accuracy machine-learned neuroevolution potential (NEP) models, we validate and showcase the effectiveness of the implemented SED method across systems of varying dimensionalities. Specifically, the NEP-driven MD-SED accurately reveals how phonon modes are affected by strain in carbon nanotubes, as well as by interlayer coupling strengths and the twist angles in two-dimensional molybdenum disulfide. For three-dimensional systems, the SED method effectively establishes the thermal transport regime diagram for metal-organic frameworks, distinguishing between particlelike and wavelike propagation regions. Moreover, using bulk silicon as an example, we show that phonon SED can efficiently capture quantum dynamics based on path-integral trajectories. The pysed package bridges MD simulations with detailed phonon-mode insights, delivering a robust tool for investigating thermal transport properties with detailed mechanisms across various materials. Phonon ; Kinetic energy ; Molecular dynamics ; Dispersion (optics) ; Statistical physics ; Dynamics (music) ; Physics ; Materials science ; Condensed matter physics ; Classical mechanics ; Optics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Menglin Sun | Bai-Qi Jin | Xiaolong Yang | Shenzhen Xu
Source: Nature Communications (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand canonical constant potential condition is a great challenge for theoretical electrocatalysis. Here, we develop a unified computational framework to explicitly treat nuclear quantum effects (NQEs) by a sufficient grand canonical sampling, further assisted by a machine learning force field adapted for electrochemical conditions. Our work demonstrates a non-negligible impact of NQEs on PCET simulations for hydrogen evolution reaction at room temperature, and provides a physical picture that wave-like quantum characteristic of the transferring protons facilitates the particles to tunnel through classical barriers in PCET paths, leading to a remarkable activation energy reduction compared to classical simulations. Moreover, the physical insight of NQEs may reshape our fundamental understanding of other types of PCET reactions in broader scenarios of energy conversion processes. Electrocatalyst ; Quantum ; Constant (computer programming) ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xiaoye Zhou | Wenjie Lu | Xiangyang Peng | Xiaoqiang Zhuang | Mingming Wang | Xusheng Yang | Shulong Ye | Hong‐Hui Wu
Source: Acta Materialia (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Dissecting the phase transformation mechanism of Titanium hydride at atomic scale Materials science ; Hydride ; Atomic units ; Transformation (genetics) ; Phase (matter) ; Mechanism (biology) ; Scale (ratio) ; Titanium hydride ; Metallurgy ; Crystallography ; Titanium ; Metal Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Haoyang He | Fangxing Lai | Yan Zhang | Xue Zhang | Chenyi Tian | Xin Li | Yongtian Wang | Shumin Xiao | Lingling Huang
Source: Opto-Electronic Advances (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectro-polarimetric detection enabled by multidimensional metasurface with quasi-bound states in the continuum Spectral and polarization information are crucial for characterizing the composition and surface morphology of various materials. However, traditional spectral and polarization detection systems are hampered by bulky, single-function optical components and complex configurations, hindering the portable, low-cost, and multifunctional applications in compact, field-deployable devices. In this study, we introduce a miniaturized near-infrared (NIR) spectro-polarimetric detection device utilizing a group of meta-spectro-polarimeters (MSPs), which support quasi-bound states in the continuum (Q-BIC) under specific incident polarization, significantly reducing the spatial footprint and improving the design scalability. By precisely adjusting the high quality resonances and polarization sensitive radiation behavior of MSPs, we not only extend the number of spectral waveband to 20 that greatly surpassing traditional division-of-amplitude or division-of-time schemes, but also ensure an average spectral resolution power (SRP, <italic>λ</italic>/Δ<italic>λ</italic>) of 71.03. We achieve high accuracy multidimensional spectro-polarization detection, resulting in the angle of linear polarization (AoLP) with an average error of 4.67° and precise spectrum reconstruction in a machine-learning free way, avoiding the huge computational power and energy during the training process. Additionally, we demonstrate the high spectro-polarimetric imaging performance of our system in different experimental scene. Such multidimensional detection device holds great promise for adoption in fields like aerospace, chemical detection, machine vision, and so on. Polarization (electrochemistry) ; Spectral density ; Multidimensional systems ; Radiation ; Image resolution ; Spectral imaging ; Linear polarization Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies