jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dilpreet Singh Brar | B. M. Singh | Vikas Nanda
Source: Sustainable Food Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder An AI-based DenseNet-GradCAM framework rapidly detects natural adulterants in red chili powder, ensuring food safety with high accuracy and explainability. Variety (cybernetics) ; Natural (archaeology) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Computer graphics (images) ; Computer vision ; Geography ; Archaeology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: E Jixiang | Chengjun Zhai | Xinhua Jiang | Ziyang Xu | Muqiu Wudan | Danyang Li
Source: Foods (article, 2025-04-17)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Chilled Mutton Freshness Using a Dual-Branch Hierarchical Spectral Feature-Aware Network Precise detection of meat freshness levels is essential for food consumer safety and real-time quality monitoring. This study aims to achieve the high-accuracy freshness detection of chilled mutton freshness by integrating hyperspectral imaging with deep learning methods. Although hyperspectral data can effectively capture changes in mutton freshness, sparse raw spectra require optimal data processing strategies to minimize redundancy. Therefore, this study employs a multi-stage data processing approach to enhance the purity of feature spectra. Meanwhile, to address issues such as overlapping feature categories, imbalanced sample distributions, and insufficient intermediate features, we propose a Dual-Branch Hierarchical Spectral Feature-Aware Network (DBHSNet) for chilled mutton freshness detection. First, at the feature interaction stage, the PBCA module addresses the drawback that global and local branches in a conventional dual-branch framework tend to perceive spectral features independently. By enabling effective information exchange and bidirectional flow between the two branches, and injecting positional information into each spectral band, the model's awareness of sequential spectral bands is enhanced. Second, at the feature fusion stage, the task-driven MSMHA module is introduced to address the dynamics of freshness variation and the accumulation of different metabolites. By leveraging multi-head attention and cross-scale fusion, the model more effectively captures both the overall spectral variation trends and fine-grained feature details. Third, at the classification output stage, dynamic loss weighting is set according to training epochs and relative losses to balance classification performance, effectively mitigating the impact of insufficiently discriminative intermediate features. The results demonstrate that the DBHSNet enables a more precise assessment of mutton freshness, achieving up to 7.59% higher accuracy than conventional methods under the same preprocessing conditions, while maintaining superior weighted metrics. Overall, this study offers a novel approach for mutton freshness detection and provides valuable support for freshness monitoring in cold-chain meat systems. Hyperspectral imaging ; Discriminative model ; Computer science ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Backbone network ; Weighting ; Data mining Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Xuemei Liang | Haojie Jia | Hao Wang | Lijuan Zhang | Dongming Li | Zhanchen Wei | Haohai You | Xiang Wan | Ruixin Li | Wei Li | Minglai Yang
Source: Agronomy (article, 2025-04-29)
Field: Chemistry — Analytical Chemistry

ASE-YOLOv8n: A Method for Cherry Tomato Ripening Detection To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting. Ripening ; Cherry tomato ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Flaviano Della Pia | Benjamin X. Shi | Venkat Kapil | Andrea Zen | Dario Alfè | Angelos Michaelides
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modelling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation enthalpies - a key thermodynamic quantity measuring the stability of a molecular crystal. Specifically, two key stumbling blocks are: (i) the need for thousands of <i>ab initio</i> quality reference structures to generate training data; and (ii) the sometimes unreliable nature of density functional theory, the main technique for generating such data. Exploiting recent developments in foundation models for chemistry and materials science alongside accurate quantum diffusion Monte Carlo benchmarks, offers a promising path forward. Herein, we demonstrate the generation of MLIPs capable of describing molecular crystals at finite temperature and pressure with sub-chemical accuracy, using as few as ∼200 data structures; an order of magnitude improvement over the current state-of-the-art. We apply this framework to compute the sublimation enthalpies of the X23 dataset, accounting for anharmonicity and nuclear quantum effects, achieving sub-chemical accuracy with respect to experiment. Importantly, we show that our framework can be generalized to crystals of pharmaceutical relevance, including paracetamol and aspirin. Nuclear quantum effects are also accurately captured as shown for the case of squaric acid. By enabling accurate modelling at ambient conditions, this work paves the way for deeper insights into pharmaceutical and biological systems. Interatomic potential ; Quantum ; Molecular dynamics ; Statistical physics ; Materials science ; Chemical physics ; Condensed matter physics ; Computer science ; Physics ; Molecular physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Runze Zhao | Prashant Singh | Qiang Li | Jiaqi Yang | Prashant Deshlahra | Hongfu Liu | Fanglin Che
Source: Applied Catalysis B: Environmental (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Field-Enhanced catalysis: Integrating experiment, theory, and machine learning for catalytic innovation Understanding and controlling the local electric field distributions at catalyst interfaces offers a powerful strategy to modulate reaction kinetics by orders of magnitude, primarily through tuning the electrostatic interactions between polarized reactants and active sites. However, direct measurement of these localized fields with atomic-scale spatial resolution under operando conditions remains an experimental challenge. This perspective provides a critical overview of state-of-the-art techniques for probing local electric fields, including Kelvin Probe Force Microscopy and Vibrational Stark Effect spectroscopy. These approaches are complemented by advanced density functional theory (DFT) method, such as grand canonical DFT and dipole layer-slab models, which enable the simulation of electrostatic potential profiles and prediction of vibrational responses of surface-bound probe molecules under applied fields. Furthermore, we examine the growing role of machine learning (ML), particularly graph neural networks (GNNs) and generative models, in accelerating the prediction of local electric field distributions and the discovery of catalysts tailored for field-enhanced performance. These data-driven models capture complex, nonlinear relationships between catalyst structure, charge redistribution, and electrostatic properties, leveraging physically interpretable descriptors such as effective dipole moments and polarizabilities to predict field-dependent adsorption energetics. The integration of high-fidelity simulations, ML frameworks, and advanced experimental validation platforms establishes a comprehensive and scalable paradigm for rational catalyst design. Collectively, this work outlines a multidimensional approach for characterizing and exploiting local electric fields as dynamic tuning knobs in catalysis, with broad implications across electrocatalysis, photocatalysis, plasma catalysis, and microwave-assisted catalytic systems. • In situ spectroscopic techniques reveal structure-sensitive reactivity under strong fields. • DFT calculations quantify field-dependent adsorption with atomic-scale precision. • Field-dependent adsorption is predicted via dipole moments and polarizability descriptors. • GNN-based deep learning predicts field-dependent energetics with high efficiency. • Physics-informed and generative AI models enable inverse design of field-enhanced catalysis. Field (mathematics) ; Catalysis ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hamidreza Yazdani Sarvestani | Surabhi Nadigotti | Erfan Fatehi | Derek Aranguren van Egmond | Behnam Ashrafi
Source: Advanced Engineering Materials (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials Disordered structures, characterized by their lack of periodicity, present significant challenges in fields such as materials science and biology. Conventional methods often fall short of capturing the intricate properties and behaviors of these complex systems. For example, the prediction of material properties in amorphous polymers and high‐entropy alloys has historically been inaccurate due to their inherent disorder, which arises from the probabilistic nature of structural defects and nonuniform atomic arrangements. However, the rise of machine learning (ML) offers a revolutionary approach to understanding and predicting the behavior of disordered materials. This perspective article explores how ML techniques, including neural networks and generative models, provide unprecedented insights into materials with inherent disorder, driving advances in industries such as energy storage, drug discovery, and structural engineering. By leveraging powerful algorithms, researchers can now predict structural properties, identify hidden patterns, and accelerate the discovery of novel materials. Case studies illustrate the ability of ML to overcome data scarcity, enhance model reliability, and enable real‐time analysis of disordered structures. While challenges such as data quality and computational costs remain, the integration of ML with traditional methods marks a transformative leap in our ability to navigate the disordered landscape, setting the stage for ground‐breaking discoveries. Materials science ; Order (exchange) ; Nanotechnology ; Engineering physics ; Engineering ethics ; Engineering ; Business Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Anastasiia S. Kholtobina | Ivor Lončarić
Source: Materials & Design (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Exploring elastic properties of molecular crystals with universal machine learning interatomic potentials We benchmarked existing and newly trained universal machine learning interatomic potentials for modelling molecular crystals, particularly their elastic properties. We found that potentials trained on the SPICE dataset provide reasonable predictions of the elastic properties of molecular crystals that are as good as predictions made using density functional theory-based methods. Still, the uncertainty of predictions and difference to experimental values is relatively high (larger than 5 GPa for Young's modulus). We have performed a high-throughput study of the elastic properties of molecular crystals. We have found that some of the molecular crystals show negative linear compressibility and validated our results using density functional theory. • Universal machine learning interatomic potentials are tested for predictions of elastic properties of molecular crystals. • Accuracy of best machine learning interatomic potentials is similar to density functional theory. • The high-throughput search identifies molecular crystals with interesting properties such as negative linear compressibility. Materials science ; Interatomic potential ; Molecular dynamics ; Chemical physics ; Crystallography ; Nanotechnology ; Computational chemistry ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Yihan Ye | Bing Bai | Yilun Ding | Xinzhe Li | Feng Jiao | Jianping Xiao | Xiulian Pan
Source: Angewandte Chemie International Edition (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Accelerated Oxide‐Zeolite Catalyst Design for Syngas Conversion by Reaction Phase Diagram Analysis and Machine Learning Oxide-zeolite (OXZEO) catalyst design concept provides an alternative approach for the direct syngas-to-olefins (STO) with superior selectivity. Enhancing the activity of oxide components remains a critical and long-pursued target in this field. However, rational design strategies for optimizing oxides and improving the catalyst performance in such complex reaction networks are still lacking. We employed energetic descriptors such as the adsorption energies of CO* and O* (G<sub>ad</sub>CO* and G<sub>ad</sub>O*) through reaction phase diagram (RPD) analysis to predict the catalyst performance. The prediction was initially validated by the catalytic activity trends measured by experiments. Machine learning (ML) was further utilized to accelerate the screening of new catalysts. Ultimately, Bi-doped and Sb-doped ZnCrO<sub>x</sub> were theoretically predicted as optimized oxide candidates for the OXZEO reaction, which was experimentally verified to be more active than the currently best ZnCrO<sub>x</sub> counterpart. This work demonstrated enhanced OXZEO catalysts for STO as well as a research paradigm integrating theory and experiment to optimize bifunctional catalysts for complex reaction networks. Catalysis ; Bifunctional ; Zeolite ; Oxide ; Phase diagram ; Materials science ; Selectivity ; Rational design ; Work (physics) ; Syngas ; Complex oxide ; Bifunctional catalyst ; Chemical engineering ; Adsorption ; Phase (matter) ; Process engineering ; Computer science ; Nanotechnology ; Chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Engineering ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Unghwi Yoon | Keunhong Jeong | Sang Hoon Kim
Source: Journal of CO2 Utilization (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Advancing electrocatalysis through density functional theory: From reaction mechanisms to machine learning integration This review examines the pivotal role of density functional theory (DFT) in advancing electrocatalysis research, which underpins sustainable energy technologies ranging from fuel cells to water electrolyzers. We provide a comprehensive analysis of how DFT calculations elucidate reaction mechanisms at atomic and electronic levels, particularly for environmentally critical processes such as hydrogen peroxide production, oxygen reduction, hydrogen evolution, and carbon dioxide reduction. The review details how DFT investigations have revealed the influence of crystal facet engineering, atomic coordination environments, and electronic structures on catalytic performance. Despite its utility, conventional DFT faces inherent limitations when modeling semiconductor catalysts and complex electrochemical interfaces. We discuss innovative methodologies developed to overcome these challenges, including the integration of Poisson equations to model band bending, catalytic unit engineering approaches, and Grand-Canonical DFT frameworks. Additionally, we explore the emerging synergy between DFT and machine learning, which is revolutionizing catalyst discovery through high-throughput screening and accelerated optimization of novel materials. This integration enables researchers to navigate vast chemical spaces and establish structure-property relationships with unprecedented efficiency. Future advances in electrocatalysis will depend on further interdisciplinary collaboration, combining expertise in computational chemistry, materials science, and artificial intelligence to design highly efficient and stable catalytic systems that address pressing environmental and energy challenges. • Review on Density Function Theory (DFT) used in electrocatalysis. • Studies DFT on electrocatalysis involving H 2 O 2 production are discussed. • Studies using DFT on other electrocatalysis are discussed. • Strategies directly applicable to combination of DFT and Machine learning are discussed. Electrocatalyst ; Density functional theory ; Computer science ; Chemistry ; Computational chemistry ; Electrochemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Hengda Gao | Xiao‐Wei Guo | Genglin Li | Chao Li | Canqun Yang
Source: Neural Networks (article, 2025-04-12)
Field: Materials Science — Materials Chemistry

GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties Artificial neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Graph ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaofan Zheng | Jia Yang | Lianyong Zhou | L. Q. Qin | Changlong Lu | Nicolás Alonso‐Vante | Zhen Huang | Pengfei Zhang | Yin‐Ning Zhou | Zheng-Hong Luo | Li‐Tao Zhu
Source: Chemical Engineering Science (review, 2025-04-29)
Field: Materials Science — Materials Chemistry

Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review Transformation (genetics) ; Scale (ratio) ; Big data ; Catalysis ; SCALE-UP ; Computer science ; Atomic units ; Process engineering ; Artificial intelligence ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Data mining ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yang Wang | Qian Wang | Xijun Wang | Jing Yang | Jun Jiang | Chuanyi Jia
Source: The Journal of Physical Chemistry Letters (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Accelerated Design of Dual-Metal-Site Catalysts via Machine-Learning Prediction Dual-metal site catalysts (DMSCs) supported on nitrogen-doped graphene have shown great potential in heterogeneous catalysis due to their unique properties and enhanced efficiency. However, the precise control and stabilization of metal dimers, particularly in oxygen activation reactions, present significant challenges in practical applications. In this study, we integrate high-throughput density functional theory calculations with machine learning techniques to predict and optimize the catalytic properties of DMSCs. Transfer learning is employed to enhance the model's generalization capability, successfully predicting catalytic performance across new metal combinations. Additionally, the application of the SISSO method enables the derivation of interpretable symbolic regression models, revealing critical correlations between electronic structure features and catalytic efficiency. This approach not only advances the understanding of dual-metal site catalysis but also provides a novel framework for the systematic design and optimization of highly efficient catalysts, with broad applicability in catalytic science. Catalysis ; Dual (grammatical number) ; Density functional theory ; Graphene ; Computer science ; Throughput ; Metal ; Materials science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Marvin Poul | Liam Huber | Jörg Neugebauer
Source: npj Computational Materials (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

Automated generation of structure datasets for machine learning potentials and alloys Abstract We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials (MLIP) for multicomponent alloys, called Automated Small SYmmetric Structure Training or ASSYST . Based on exploring the full space of random crystal structures with space groups, it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question. The advantages of this approach are that only cells consisting of few atoms (≈ 10) are needed for the DFT training set, and the size and completeness of the data set can be systematically controlled with very few parameters. We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases, random alloys, as well as point and extended defects, that have not been part of the training set. Finally, we estimate the binary phase diagrams with good experimental agreement. We demonstrate that the overall excellent performance is not a coincidence, but a consequence of the extensive sampling in phase space of ASSYST . Overall, this means that ASSYST will enable the largely autonomous generation of high-quality DFT reference data and MLIPs. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods