jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Björn Abeln | Helen Bartsch | Pilar Francisca Figueroa Sánchez | Amir Ehsan Kianfar | Thorben Geers | Markus Feldmann | Elisabeth Clausen
Source: Journal of Nondestructive Evaluation (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Prediction of Micro-Cracks in Steel Structures Subjected to Fatigue by Means of Acoustic Emission Abstract This paper presents the development of a monitoring system using acoustic emission (AE) analysis for the prediction of micro- and initial cracks in fatigue-stressed steel structures such as bridges, cranes, offshore, or industrial constructions. Initial experimentation suggests a relationship between microscopically observed crack length and AE intensity, further data is required to establish a definitive correlation. As part of an ongoing research project, AE measurement techniques and evaluation are to be further developed to create a monitoring concept for micro-crack prediction in more complex fatigue-stressed steel components. The focus of this research is not on the localization and detection of crack growth or structural changes but on micro-crack detection using AE. Existing acoustic emission analysis systems can thus be extended to measure and detect micro-cracks for the earliest possible identification of damage events. This paper describes the first results of the innovative research idea. Acoustic emission ; Solid mechanics ; Materials science ; Structural engineering ; Composite material ; Forensic engineering ; Acoustics ; Engineering ; Physics Material Properties and Failure Mechanisms ; Ultrasonics and Acoustic Wave Propagation ; Fatigue and fracture mechanics
Authors: SABİNA GADİROVA | MALAHAT ALİYAROVA
Source: MƏHKƏMƏ EKSPERTİZASI KRİMİNALİSTİKA VƏ KRİMİNOLOGİYANIN AKTUAL MƏSƏLƏLƏRİ (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

ON THE ORIGIN, CHEMICAL COMPOSITION, LEGAL STATUS, AND LIABILITY-RELATED ASPECTS OF PEGANUM HARMALA Mhkm ekspertizas, kriminalistika v kriminologiyann aktual msllri" adl elmi srlr mcmusi bard "Azrbaycan Respublikas dliyy Nazirliyinin Mhkm Ekspertizas Mrkzininsasnamsi"nd Mrkzin mhkm ekspertizalarnn tyin edilmsi, aparlmas v ekspert rylrinin qiymtlndirilmsi il bal mhkm v hquq-mhafiz orqanlarna elmi-metodiki kmkliyin gstrilmsi, metodik vsaitlrin hazrlanmas, mhkm, hquq-mhafiz orqanlar v ekspertlr n hazrlanm elmi-metodik materiallarn nrinin tkil edilmsi, qanunvericilikl myyn edilmi qaydada mtbu nrlr tsis etmkl xsusi blletenlr, elmi-tdqiqat ilrinin nticlrin dair materiallar v digr nrlrin buraxlmas, nriyyat-poliqrafiya faliyytinin hyata keirilmsi zr hquq v vziflri tsbit olunub.Eyni zamanda sasnamd elmi-tdqiqat ilrinin aparlmas, ekspert tdqiqatnn yeni metod v metodikalarnn hazrlanmas, elc d tkmilldirilmsi onun faliyyt istiqamtlrin aid edilib.Mhkm ekspertizas, kriminalistika, kriminologiya v hququn digr aidiyyti sahlrind elmi-tdqiqat ilrinin yerin yetirilmsi, ld ediln nticlrin ekspert tcrbsind ttbiq edilmsi zr elmi-nzri, tcrbi v metodiki xarakterli problemlrin akar edilmsi v mzakirsi Peganum harmala ; Immunopharmacology ; Product (mathematics) ; Medicinal plants History and advancements in chemistry ; Heavy Metals in Plants ; Plant-based Medicinal Research
Authors: Hector Pasache | Cristian Tuesta | Carlos H. Inga Espinoza
Source: AgriEngineering (article, 2025-08-27)
Field: Chemistry — Analytical Chemistry

Design of an Automated System for Classifying Maturation Stages of Erythrina edulis Beans Using Computer Vision and Convolutional Neural Networks Erythrina edulis, commonly known as pajuro, is a large leguminous plant native to the Amazon region of Peru. Its seeds are valued for their high protein content and their potential to enhance food security in rural communities. However, the current methods of harvesting and sorting are entirely manual, making the process labor-intensive, time-consuming, and subject to high variability, particularly in industrial contexts. A custom lightweight convolutional neural network (CNN) was developed from scratch and optimized specifically for real-time execution on embedded hardware. The model employs ReLU activation, Adam optimization, and a SoftMax output layer to enable efficient and accurate classification. The system employs a fixed-region segmentation strategy to prevent overcounting and utilizes GPIO-based control on a Raspberry Pi 5 to synchronize seed classification with physical sorting in real time. Seeds identified as defective are automatically removed via a servo-controlled ejection mechanism. The integrated system combines object detection, image processing, and real-time actuation, achieving a classification accuracy exceeding 99.6% and an average processing time of 12.4 milliseconds per seed. The proposed solution contributes to the industrial automation of pajuro sorting and provides a scalable framework for color-based grain classification applicable to a wide range of agricultural products. Convolutional neural network ; Erythrina ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Phytochemicals and Antioxidant Activities
Authors: N. Kitamura
Source: ChemRxiv (preprint, 2025-05-02)
Field: Materials Science — Materials Chemistry

Lightweight Crystal Graph Neural Networks for Activation Energy Prediction in Organic Molecular Reactions Fast and accurate estimation of reaction activation energies is crucial for understanding chemical reactivity and accelerating catalyst discovery. While traditional methods based on quantum chemical calculations are reliable, they are computationally expensive, motivating the development of machine learning approaches. In this study, we propose lightweight frameworks based on Crystal Graph Convolutional Neural Networks (CGCNN) with about a tenth of the number of parameters of state-of-the-art graph neural network models to predict activation energies for both organic molecular reactions and heterogeneous surface reactions. Two model architectures̶a Kronecker product-based model and a diﬀerence-based model - were investigated using the Transition1x and Open Catalyst Project (OCP) datasets. The Kronecker product model consistently outperformed the diﬀerence-based model, achieving a coeﬃcient of determination (𝑅2) of 0.45 on the Transition1x dataset which is generally better than those of methods (𝑅2 ∼ 0.3) based on a molecular descriptor and a decision tree model. We also investigated the Open Catalyst Project (OCP) dataset to identify challenges for estimating activation energies in heterogeneous catalytic systems. Artificial neural network ; Graph ; Crystal (programming language) ; Computer science ; Activation energy ; Chemistry ; Artificial intelligence ; Materials science ; Biological system ; Theoretical computer science ; Organic chemistry ; Biology ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2025-03-18)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Synthesis of Prospective Organic Molecular Materials: An Algorithm with Latent Variables for Understanding and Predicting Experimentally Unobservable Reactions Chemists have traditionally relied on heuristic approaches to qualitatively assess chemical structure-property relationships and interpret experimental outcomes. However, these methods are inherently limited in handling large volumes of data and integrating them effectively into experimental planning. Understanding the interrelationships among different substitution patterns of organic molecular materials is 1 crucial for optimizing synthetic conditions and expanding their applicability. In this study, we developed a machine learning (ML) algorithm incorporating latent variables to predict unobservable reactions and synthetic conditions for organic materials: perfluoro-iodinated naphthalene derivatives. The algorithm accurately estimated substitution pattern relationships and reaction yields, which were experimentally validated with high-yield outcomes. Our findings reveal that latent variables effectively capture underlying physicochemical relationships, achieving an R2 value >0.99. This approach establishes an ML-guided framework that complements heuristic decision-making in chemistry and optimizes synthetic processes for the target molecule in an extrapolative manner. Further applications of this algorithm will focus on synthetic design and physicochemical property prediction, particularly for catalyst discovery and organic semiconductor optimization. Unobservable ; Machine learning ; Computer science ; Artificial intelligence ; Algorithm ; Latent variable ; Econometrics ; Mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Sahba Tabarraei | Omid Karimi Sadaghiani | Ahmet Ekrem Mermer
Source:  (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Recent Applications of Machine Learning and Deep Learning in Identifying Alloys with Specific Conductivity Nowadays, machine learning and deep learning are transforming the way researchers discover and optimize alloys with specific electrical conductivity. These advanced computational techniques can move forward the search for the mentioned alloys by analyzing large datasets, identifying hidden patterns, and predicting properties more efficiently than traditional trial-and-error methods. Having analyzed the numerous recent works, the applications of the machine learning and deep learning in identifying alloys with specific conductivity are determined. Accordingly, predicting electrical conductivity of alloys, accelerated discovery of new alloys, optimization of alloy composition and processing, understanding the structure-property relationship and identifying the new metals, data-driven experimental design, high-quality analyzing experimental and simulation data, and developing industrial applications are the main areas developed by the machine learning and deep learning techniques. Besides, the regression algorithms are utilized in predicting electrical conductivity and estimating conductivity. On the other hands, the classifying algorithms are applied in identifying suitable alloys and classifying alloys. Furthermore, clustering alloys and finding new material categories can be done mostly by K-Means Clustering, and DBSCAN algorithms. It also was conducted, PCA and t-SNE are broadly used in simplifying complex alloy composition data and visualizing conductivity trends. Eventually, deep learning can be applied in predicting conductivity changes, modeling thermal effects on alloy, suggesting new high-conductivity alloy, simulating alloy conductivity, and optimizing alloys. Computer science ; Artificial intelligence ; Deep learning ; Conductivity ; Machine learning ; Materials science ; Physics Machine Learning in Materials Science
Authors: Parisa Shafiee | Mitra Jafari | Julia Schowarte | Bogdan Dorneanu | Harvey Arellano‐García
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Streamlining Catalyst Development through Machine Learning: Insights from Heterogeneous Catalysis and Photocatalysis Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization. Photocatalysis ; Catalysis ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yunlai Cai
Source:  (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Prediction and Analysis of Chemical Molecular Reaction Paths Driven by Deep Learning Algorithms The primary objective of this study is to address the challenges associated with traditional chemical reaction path prediction methods, including the high computational cost, the over-reliance on expert experience, and the inability to effectively explore complex reaction systems. To address these challenges, this paper proposes an efficient prediction and analysis model of chemical molecular reaction paths based on deep learning. Firstly, a graph neural network is used to deeply characterize the topological structure and electronic environment of the reactant molecules, extracting node and edge features that contain chemical information. Second, in this study, a sequence decoder based on the Transformer architecture is constructed to generate a series of intermediates and transition state structures in the reaction path. Additionally, the attention mechanism is introduced in the research to dynamically weight the contributions of different atomic characteristics to the reaction energy barrier during the decoding process, thereby enhancing the model's attention to key reaction sites and the interpretability of the prediction. Finally, it is verified on the open quantum chemistry calculation data set. The research findings demonstrate that the proposed model outperforms mainstream benchmark methods in terms of both prediction accuracy and computational efficiency, exhibiting superior comprehensive performance. The study confirms that the model provides a new theoretical basis and an effective technical scheme for conducting large-scale, high-throughput research into previously unknown chemical reaction spaces and the automatic discovery of new reaction mechanisms. Deep learning ; Artificial neural network ; Pattern recognition (psychology) ; Feature (linguistics) ; Automatic target recognition Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Carlota Bozal‐Ginesta | Sergio Pablo‐García | Changhyeok Choi | Albert Tarancón | Alán Aspuru-Guzik
Source: ChemRxiv (preprint, 2025-03-05)
Field: Materials Science — Materials Chemistry

Machine learning for the experimental and computational development of heterogeneous catalysis Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets. These techniques could be particularly useful in heterogeneous catalysis research for enabling the correlation of the catalyst performance to its physicochemical properties. So far in the chemistry and material science communities, machine learning models have mostly been built on high-throughput quantum chemistry calculations, and only selected case studies have led to the experimental discovery of improved catalyst materials. The slow pace and limited number of scientific breakthroughs may be attributed to simplistic assumptions about catalyst structure in quantum chemistry calculations and the incomplete experimental data available. Therefore, we believe that the development of high-throughput approaches closely coupled with machine-learning-based approaches could help accelerate experimental catalysis research. To aid the community, we bring together the available body of work applying high-throughput approaches and machine learning to the development of solid heterogeneous catalysis. We offer an objective view of the trends in the field by performing a detailed and systematic comparison of papers based on the (1) the ML method, the features used as model input and output, (3) the material, device or reaction investigated, (4) the dataset size, and (5) the overall achievement. Furthermore, for models reporting unitless R2 values, we quantitatively analyze the model performance as a function of the features used, the reaction type and the dataset size. Development (topology) ; Computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Quantum Entanglement Signatures in Chemical Reaction Dynamics: A Machine Learning Approach Quantum entanglement, a fundamental phenomenon of quantum mechanics, is increasingly recognized for its potential role in governing the behavior of chemical systems and reactions. While theoretical predictions suggest entanglement can influence reaction pathways, rates, and product distributions, its direct detection and characterization in complex molecular environments remain significant challenges due to the inherent complexity and decoherence effects. This paper proposes a novel framework utilizing machine learning techniques to identify and interpret quantum entanglement signatures embedded within the high-dimensional data generated by quantum chemical reaction dynamics simulations. We outline a methodology that involves generating comprehensive datasets of molecular trajectories and quantum observables for various chemical reactions, followed by the application of advanced machine learning models, including deep neural networks, to discern subtle patterns indicative of entanglement. The approach aims to extract chemically intuitive features correlating with entanglement measures, thereby providing a powerful tool for understanding the role of quantum mechanics beyond classical approximations in chemical transformations. This work not only seeks to advance the fundamental understanding of quantum entanglement in chemistry but also paves the way for the design of quantum-informed chemical reactions and materials with tailored properties. Quantum entanglement ; Quantum ; Quantum sensor ; Quantum decoherence ; Quantum machine learning ; Quantum chemical ; Observable ; Quantum computer ; Quantum metrology Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Haoyue Guo | Chuntian Cao | Matthew R. Carbone | Shinjae Yoo | Feng Wang | Deyu Lu | Alexander Urban | Nongnuch Artrith
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning to Advance Amorphous Battery Materials Many materials with applications in energy, e.g., in batteries, are non-crystalline, exhibiting amorphous structures, chemical disorder, and complex compositions. This complexity makes direct modeling with first principles methods challenging. To address this challenge, we developed accelerated sampling strategies based on machine learning interatomic potentials, genetic algorithms, and molecular-dynamics simulations [1]. Here, I will discuss the methodology and its applications to amorphous battery materials. We mapped the composition and structure space of amorphous lithium thiophosphate (LPS) solid electrolytes [2]. The thermodynamic stability and ionic conductivity of the non-crystalline phases were correlated with local structural motifs, leading to the identification of structure-composition-conductivity relationships that can be used for materials optimization and design. X-ray absorption spectroscopy (XAS) reveals details of the absorber atom’s local chemistry and is, therefore, well suited to validate amorphous structures predicted by simulations. For this purpose, we created a computational sulfur/phosphorus K-edge XAS spectra database for LPS phases using structures from our previous study [3]. We then showed how experimental XAS measurements of degraded LPS can be interpreted by matching with the simulation database using machine learning [4], providing initial atomic-scale insights into the oxidative degradation of LPS electrolytes, guiding macroscopic reactions via microstructural engineering, and enhancing sulfide electrolyte design. [1] - N. Artrith and A. Urban, Comput. Mater. Sci. 114 , 135-150 (2016). [2] - H. Guo, Q. Wang, A. Urban, and N. Artrith, Chemistry of Materials 34 , 6702-6712 (2022). [3] - H. Guo, M.R. Carbone, C. Cao, N. Artrith, A. Urban, D. Lu, et al, Scientific Data 10 , 349 (2023). [4] - C. Cao, M.R. Carbone, C. Komurcuoglu, S. Jagriti, S. Yoo, N. Artrith, A. Urban, D. Lu, F. Wang et al, Cell Reports Physical Science, 5 , 101909 (2024). Figure 1 Amorphous solid ; X-ray absorption spectroscopy ; Absorption spectroscopy ; Stability (learning theory) ; Fast ion conductor ; Battery (electricity) ; Ionic conductivity ; Absorption (acoustics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Subhan Azeem | Ayesha Bibi | N.S. Hassan | Мuhammad Abid
Source: Kashf Journal of Multidisciplinary Research (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

TOWARDS SMART CATALYSIS: MACHINE LEARNING TECHNIQUES FOR ENHANCED PERFORMANCE IN DRY REFORMING OF METHANE Catalysis supports today's chemical technology to create clean power while reducing harmful gases in our atmosphere. Dry Reforming of Methane (DRM) has gained attention as a valuable process to use methane and carbon dioxide for making synthesis gas which supports both clean energy production and chemical manufacturing. Traditional lab methods for improving DRM catalysts don't work well because they waste energy, make catalysts stop working faster, and fail to understand how multiple reaction elements relate to one another. The challenges of traditional methods drive researchers to use machine learning to find efficient solutions. Through our research, we present a system that combines machine learning models including regression analysis, decision trees, and neural networks to improve DRM catalyst development. Our neural network tests beat older methods by providing highly precise predictions with only a 3% deviation and an R² score of 0.98. As our analysis shows temperature stands out as the most important variable then time and catalyst makeup show secondary importance in helping guide catalyst development. Our method of using machine learning shortens the time needed to test and refine catalysts by 50%. Although this research offers useful outcomes the small data sample makes it hard to apply conclusions to other scenarios. Neural network operations remain unclear so scientists need SHAP analysis tools for better understanding. The next phase of the study should collect additional data and merge machine learning techniques with live experimental testing to discover superior catalysts and build practical heat-neutral DRM systems. Carbon dioxide reforming ; Methane ; Catalysis ; Environmental science ; Computer science ; Chemistry ; Syngas ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Yiding Wang | Fengpei Zhang | Tianqing Li | Xiangdong Ding | Graeme J. Ackland | Hongxiang Zong | Turab Lookman | Jun Sun
Source: Acta Materialia (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Nested crystal graph neural networks for modeling chemically complex materials Scalability ; Artificial neural network ; Complex system ; Uniqueness ; Representation (politics) ; Ranging ; Graph ; Lattice (music) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electrocatalysts for Energy Conversion