jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying Liu | Xiongfeng Zeng | Weiwei Zhang | Donghai Mei
Source: The Journal of Physical Chemistry C (article, 2025-03-19)
Field: Materials Science — Materials Chemistry

Stability and Migration of Silicalite-1 Zeolite-Encapsulated Pt Nanoclusters Using Artificial Neural Network Potential The stability and migration behavior of metal nanoclusters are critical factors in subnanometer-scale heterogeneous catalysis, directly impacting catalytic performance and selectivity. Encapsulation of metal nanoclusters within porous frameworks, such as zeolites, presents a promising strategy to enhance the stability and control migration dynamics. A comprehensive understanding of the interplay among nanocluster size, zeolite framework properties, and their influence on stability and mobility is essential for the rational design of efficient zeolite-encapsulated catalysts. This study introduces a global neural network (G-NN) potential specifically trained for platinum (Pt) nanoclusters encapsulated in silicalite-1 zeolite, validated through density functional theory (DFT) calculations. The validation encompassed structural optimization, adsorption energy analysis for Ptn (n = 1–6) nanoclusters, and migration energy barrier assessments for Pt1 and Pt2 clusters. Using the G-NN potential, adsorption sites within silicalite-1 channels were identified, and migration pathways between straight and sinusoidal channels were explored. The results revealed size-dependent interaction strengths between Ptn nanoclusters and the silicalite-1 framework, with larger clusters exhibiting stronger confinement. Migration barrier calculations and molecular dynamics simulations further demonstrated that larger Ptn nanoclusters (n ≥ 6) are effectively immobilized within the zeolite framework over nanosecond time scales. These findings provide critical insights into the design of advanced zeolite-encapsulated metal catalysts, paving the way for improved catalytic performance in various applications. Nanoclusters ; Zeolite ; Artificial neural network ; Materials science ; Stability (learning theory) ; Chemical engineering ; Nanotechnology ; Computer science ; Chemistry ; Artificial intelligence ; Engineering ; Organic chemistry ; Catalysis ; Machine learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Zeolite Catalysis and Synthesis
Authors: Tae Hyung Lee | Serin Lee | L. Yuan | Jennifer A. Dionne | Jungwon Park
Source: ACS Nano (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Integrative Approaches to Reveal Catalyst Dynamics: Bridging <i>Operando</i> Techniques, Theory, and Artificial Intelligence Catalysts operate under complex conditions that require sophisticated approaches to understand their dynamics. This perspective outlines advances in experimental <i>operando</i> techniques, theoretical approaches, and machine learning (ML)-based data analysis to elucidate catalyst dynamics and improve the next-generation catalyst design. We first survey <i>operando</i> techniques, spanning electron microscopy, X-ray spectroscopy, and vibrational spectroscopy, that capture catalyst dynamics under operating conditions. We then discuss how <i>operando</i> observations integrate with and inform theoretical models, creating an iterative feedback loop between experiment and computation. Finally, we highlight how advanced data analysis, especially ML, enables the interpretation of high-dimensional <i>operando</i> data sets and can even inform catalyst design. Together, these synergetic approaches provide a unified framework for probing catalyst function and accelerating the rational design of efficient, durable catalytic systems for sustainable chemical manufacturing. Bridging (networking) ; Catalysis ; Function (biology) ; Perspective (graphical) ; Rational design ; Interpretation (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: J K Dutta | Jobin Thomas | Sayantan Pradhan | Ramzi Jalgham | Aparna Raj | Santi M. Mandal | Jitendra Satija | Gourisankar Roymahapatra
Source: Total Chemistry (article, 2025-06-18)
Field: Chemistry — Organic Chemistry

Exploring chalcone derivatives as promising antimicrobials targeting dihydrofolate reductase Dihydrofolate reductase ; Chalcone ; Antimicrobial ; Chemistry ; Pharmacology ; Combinatorial chemistry ; Medicine ; Biochemistry ; Enzyme ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Renquan Lv | Weiwei Han | Zixu Zeng | Yi He | Lecheng Lei | Ping Li | Xingwang Zhang
Source: Chem & Bio Engineering (article, 2025-08-14)
Field: Chemistry — Organic Chemistry

Multimodal Modeling for Polymer Property Prediction and Decoupling of Structure–Property Relationship The key challenge in the development of a target property polymer stems from the unclear structure-property relationship (SPR). A multimodal graph neural network (GNN) framework, GeoALBEF, is proposed to address this problem. GeoALBEF introduces a training process based on an "Align before Fuse" architecture that optimizes a three-stage loss, which deeply fuses polymer graph information and text information. Benchmark tests constructed based on 5,000 polymer samples showed that GeoALBEF reduced the mean relative RMSE by 8.6% compared to the suboptimal model in the prediction task of 24 key properties in 6 categories. It is especially worth pointing out that the model achieves functional group level interpretability through the attention mechanism and the functional group averaging strategy. The interpretability was mapped using visualization methods. Additionally, we used reinforcement learning to quantify this interpretability and successfully decoupled the SPR of the polymers. This multimodal system, which combines high-precision prediction capability and mechanism analysis, establishes an intelligent mapping paradigm from polymer structure to property and is expected to promote the accelerated optimization of target property polymers. Property (philosophy) ; Decoupling (probability) ; Computer science ; Biological system ; Engineering ; Control engineering ; Philosophy ; Epistemology ; Biology Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Machine Learning in Materials Science
Authors: Mashroor S. Nitol | Avanish Mishra | Shuozhi Xu | Saryu Fensin
Source: Physical Review Materials (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Moment tensor potential and its application in the Ti-Al-V multicomponent system Titanium (Ti) alloys such as Ti-6Al-4V are recognized as critical materials for aerospace and biomedical applications due to their exceptional strength-to-weight ratio and high-temperature performance. Traditional interatomic potentials are known to struggle in capturing their complex phase behavior, limiting atomistic modeling capabilities. In this work, a machine learning (ML)-based moment tensor potential (MTP) is developed using first-principles data from diverse configurations that span the unary, binary, and ternary systems of Ti, Al, and V. The optimized MTP is shown to achieve accuracy of density functional theory (DFT) level for lattice parameters (errors $&lt;1.2$%), elastic constants (errors $&lt;10$% for most components), and stack fault energies, while having computational efficiency comparable to non-ML potentials. Phase stability across composition-temperature space is predicted through hybrid Monte Carlo (MC)/molecular dynamics simulations, including $\ensuremath{\alpha}/\ensuremath{\beta}$ transitions in pure Ti (1083 K vs. experimental 1155 K), $\ensuremath{\alpha}$-to-${\mathrm{D}0}_{19}$ transitions in Ti-Al (8.5--25 at.% Al), and $\ensuremath{\beta}+\ensuremath{\omega}$ coexistence in Ti-V alloys. In particular, the evolution of the $\ensuremath{\beta}$ precipitate in Ti-6Al-4V is captured by MTP without explicit training on ternary DFT-MC data. This work establishes the MTP framework as a powerful tool for modeling complex phase transformations in multicomponent Ti alloys, enabling atomistic insights into microstructural evolution and alloy design. Materials science ; Moment tensor ; Moment (physics) ; Tensor (intrinsic definition) ; Composite material ; Classical mechanics ; Pure mathematics ; Mathematics ; Physics Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Machine Learning in Materials Science
Authors: Jiayi Guo | Chunfeng Cui | Tao Ouyang | Juexian Cao | Xiaolin Wei
Source: Scientific Reports (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons This study predicts the thermoelectric figure of merit (ZT) for defective gamma-graphyne nanoribbons (γ-GYNRs) using binary coding, convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-scale feature fusion. The approach accurately predicts ZT values with only 500 initial structures (3% of 16,512 candidates), achieving an R<sup>2</sup> above 0.91 and a mean absolute error (MAE) of 0.05 to 0.06. The use of artificial feature extraction combined with an attention mechanism reveals that the number and distribution of defects are crucial for achieving high ZT values. γ-GYNRs with moderate and evenly distributed defect count show superior thermoelectric performance. This demonstrates the effectiveness of neural networks in designing low-dimensional materials like γ-GYNRs and offers insights into exploring other materials with excellent thermoelectric properties. Enhanced Data Rates for GSM Evolution ; Graphyne ; Artificial neural network ; Computer science ; Business ; Computer network ; Artificial intelligence ; Materials science ; Nanotechnology Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Graphene research and applications
Authors: Siqi Zhan | Zhenyuan Li | Hengheng Zhao | Zhanjie Liu | Qian Li | Shilong Ji | Weifeng Zhang | Qingsong Zhao | Liqun Zhang | Jun Liu
Source: Macromolecular Rapid Communications (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Transfer Learning for Polymer Mechanics: A Fusion Approach to Bridge Molecular Dynamics Simulations and Experiments in SSBR The stress-strain curve is a key indicator of the mechanical behavior of polymeric materials and plays a vital role in optimizing the performance of solution-polymerized styrene-butadiene rubber (SSBR). Molecular dynamics (MD) simulations enable the investigation of microscale deformation mechanisms, yet their use of unrealistically high strain rates leads to stress values that diverge significantly from experimental results. To address this discrepancy, we proposed a weighted fusion framework that integrates transfer learning with a hybrid long short-term memory-multilayer perceptron (LSTM-MLP) model and the eXtreme Gradient Boosting (XGBoost) algorithm. A dataset of 100 simulated stress-strain curves was generated from 20 distinct SSBR molecular systems across five strain rates, supplemented with five experimental curves for SSBR (grade 2557TH) under varying tensile rates. The model was pretrained on the simulated data and fine-tuned using the limited experimental data, enabling stress-strain predictions consistent with experiments. Comparative analyses against alternative machine learning baselines confirmed the model's superior accuracy. Additionally, correlation analysis revealed how the four structural units of SSBR-styrene, 1,2-butadiene, cis-1,4-butadiene, and trans-1,4-butadiene-influence mechanical behavior, offering theoretical insights for targeted performance enhancement. Styrene-butadiene ; Microscale chemistry ; Materials science ; Molecular dynamics ; Polymer ; Strain rate ; Stress (linguistics) ; Harmony search ; Biological system ; Computer science ; Artificial intelligence ; Composite material ; Chemistry ; Styrene ; Mathematics ; Computational chemistry ; Copolymer Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Fuel Cells and Related Materials
Authors: Taiwo Onaopemipo Alao | Kehinde Temitope Alao | Oluwaseun Ruth Alara | Victor D. Ola
Source: High Temperature Corrosion of Materials (article, 2025-11-29)
Field: Materials Science — Metals and Alloys

A Comprehensive Review of Corrosion Failure Mechanisms in Advanced Materials: Microscopic Insights and Durability Under Extreme Conditions Durability ; Stress corrosion cracking ; Corrosion ; Hydrogen embrittlement ; Embrittlement ; Corrosion fatigue ; Environmental stress fracture Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Erosion and Abrasive Machining
Authors: Mathieu Calvat | C. Bean | Dhruv Anjaria | H. L. Park | Haoren Wang | Kenneth S. Vecchio | Jean‐Charles Stinville
Source: ArXiv.org (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together these steps offer a method offers a novel means to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties. Space (punctuation) ; Diffraction ; Materials science ; Spatial heterogeneity ; Economic geography ; Geography ; Computer science ; Optics ; Physics ; Ecology ; Biology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: Jiamin Zhou | Bo Xiao | Qi Liu | Lifeng Liu | Lei Zhang
Source: ACS Applied Materials & Interfaces (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

MatPC: Prompting Large Language Model, Crystal Structure Prediction, and First-Principles for Semantic-Driven Material Design In this study, an AI-guided framework is developed for semantic-driven material design, integrating large language models (LLMs) with first-principles methods and crystal structure prediction (MatPC) to identify novel photovoltaic materials. By utilizing prompt-engineered LLMs, semantic embeddings of material property descriptions are leveraged to identify uncommon materials candidates with strong alignment to desired functionalities. The material discovery pipeline combines LLMs, similarity scoring, dimensional reduction, formula screening, crystal structure prediction, and DFT validation into a cohesive computational workflow. The candidates undergo crystal structure prediction to generate polymorphs using a hybrid genetic algorithm-graph neural network (GA-GNN) approach, followed by validation through DFT calculations on atomic and electronic properties, optical absorption, and theoretical power conversion efficiencies. As a case study, an unconventional Bi<sub>2</sub>WO<sub>6</sub> polymorph is identified as a promising photovoltaic material, with its electronic and optical properties thoroughly analyzed via first-principles calculations. Our study presents an efficient material discovery pipeline leveraging large language models (LLMs) to accelerate the material design process. Materials science ; Material Design ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Maximilian Greif | Tobias Frömel | Thomas P. Knepper | Carolin Hühn | Stephan Wagner | Michael Pütz
Source: Journal of the American Society for Mass Spectrometry (article, 2025-04-30)
Field: Chemistry — Spectroscopy

Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratories by Soft Ionization by Chemical Reaction in Transfer–High-Resolution Mass Spectrometry The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography - mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated. Chemistry ; Mass spectrometry ; Chromatography ; Analytical Chemistry (journal) ; Chemical ionization ; Ionization ; Resolution (logic) ; Organic chemistry ; Ion Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Daming Sun | Menghan Shi | Xuan Ge | Fengming Cao | Lars R. Jensen | Junwei Ding | Jianan Zhao | Tao Du | Morten M. Smedskjær
Source: Ceramics International (article, 2025-07-18)
Field: Materials Science — Ceramics and Composites

Investigating the structure and fracture toughness of spodumene glass-ceramics using machine learning Crystals in glass-ceramics can impede crack propagation and cause crack deflection to prevent crack extension, improving the fracture toughness of the glass. However, detailed atomic-scale understanding of crack initiation and propagation in glass-ceramics remains elusive. In this work, we first experimentally investigate the fracture patterns of Li 2 O-Al 2 O 3 -SiO 2 (LAS) glass-ceramics, finding enhanced crack deflection for higher crystallinity. To investigate the influence of different crystal morphologies on the fracture mode and understand the structure-mechanics relationships, we then simulate the fracture behavior of LAS glass-ceramics using molecular dynamics simulations. We find that a high frequency of Al coordination number changes (bond switching events) occur during the fracture process, consuming the fracture energy. We then use a classification-based machine learning model to calculate 'softness' based on the static structure. Subsequent simulations show that softness can predict Al bond switching and thus capture the long-term dynamic behavior of Al atoms. Notably, although we did not use crystallinity as the training dataset, the machine learning model can learn the structure features related to bond switching probability and differentiate the fracture behavior at different stages. As such, softness can effectively predict the fracture behavior, i.e., predict the microscopic failure mode of glass-ceramics based on their structure. This work thus reveals the influence of the initial atomic-scale structure on different fracture modes in glass-ceramics, which is beneficial for guiding the design of novel glass-ceramic compositions. Materials science ; Spodumene ; Fracture toughness ; Ceramic ; Composite material ; Toughness ; Metallurgy Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Metallic Glasses and Amorphous Alloys
Authors: Hyoyoung Lee | Jisik Choi | Jin Yong Lee | Jing An | Vu Thi Huong | Van Duc Bui | Hesam Kamyab | Ho-Hyun Kim | Tejraj M. Aminabhavi | Yasser Vasseghian | Sang‐Woo Joo
Source: Sustainable materials and technologies (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Photocatalytic CO2 conversions on copper nanoparticles investigated by Raman spectral changes using convolutional neural networks Photocatalysis ; Copper ; Convolutional neural network ; Raman spectroscopy ; Nanoparticle ; Nanomaterials ; Materials science ; Chemical engineering ; Photochemistry ; Chemistry ; Nanotechnology ; Catalysis ; Computer science ; Artificial intelligence ; Optics ; Metallurgy ; Physics ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Gas Sensing Nanomaterials and Sensors
Authors: Indrajeet Mandal | Sajid Mannan | Yuanqing Lu | Nitya Nand Gosvami | Lothar Wondraczek | N. M. Anoop Krishnan
Source: Journal of Non-Crystalline Solids (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for Na-Ion conductivity and activation energy Conductivity ; Energy (signal processing) ; Ion ; Physics ; Cognitive science ; Artificial intelligence ; Computer science ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Fault Detection and Control Systems