jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yue Fan
Source: Applied Sciences (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Atomistic Modeling of Microstructural Defect Evolution in Alloys Under Irradiation: A Comprehensive Review Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. Materials science ; Metallurgy Fusion materials and technologies ; Nuclear Materials and Properties ; Ion-surface interactions and analysis
Authors: Marc Botifoll | Ivan Pinto‐Huguet | Enzo Rotunno | Thomas Galvani | Catalina Coll | Payam Habibzadeh Kavkani | María Chiara Spadaro | Yann‐Michel Niquet | Martin Eriksen | Sara Martí‐Sánchez | Georgios Katsaros | Giordano Scappucci | Peter Krogstrup | Giovanni Isella | Andreu Cabot | G. Merino | Pablo Ordejón | Stephan Roche | Vincenzo Grillo | Jordi Arbiol
Source: Advanced Materials (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of devices due to its time-intensive nature. To address this, an analytical workflow is introduced for the holistic characterization, modelling, and simulation of device heterostructures. This workflow automates the experimental (S)TEM data analysis, providing an in-depth characterization of crystallographic information, 3D orientation, elemental composition, and strain distribution. It reduces a process that typically takes days for a trained human into an automatic routine solved in minutes. Utilizing a physics-guided artificial intelligence model, it generates representative descriptions of materials and samples. The workflow culminates in creating digital twins of systems limited with at least one axis of translational invariance -3D finite element and atomic models of millions of atoms-enabling simulations that provide crucial insights into device behavior in practical applications. Demonstrated with SiGe planar heterostructures for scalable spin qubits, the workflow links digital twins to theoretical properties, revealing how atomic structure impacts materials and functional properties such as spatially-resolved phononic or electronic characteristics, or (inverse) spin orbit lengths. The versatility of the workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies. Workflow ; Characterization (materials science) ; Automation ; Scalability ; Transmission (telecommunications) ; Heterojunction ; Process (computing) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Sailajah Gukathasan | Chibuzor Olelewe | Libby Ratliff | Jong H. Kim | Alyson M. Ackerman | J. Robert McCorkle | Sean Parkin | Gunnar F. Kwakye | Jill Kolesar | Samuel G. Awuah
Source: Journal of Medicinal Chemistry (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Chemoproteomic Profiling of a Carbon-Stabilized Gold(III) Macrocycle Reveals Cellular Engagement with HMOX2 In this work, we discovered a novel organometallic gold(III) macrocycle, <b>Au-Mac1,</b> that demonstrates anticancer potency in a panel of triple-negative breast cancer cells (TNBC), and based on this complex, a biotinylated-Au-Mac1 probe was designed for target identification via chemoproteomics, which uncovered the engagement of HMOX2 of the heme-energy metabolism pathway. Using orthogonal chemical biology and molecular biology approaches, including immunoblotting, flow cytometry, and cellular thermal shift assays, it was confirmed that <b>Au-Mac1</b> engages HMOX2 in cells. Downstream effects of <b>Au-Mac1</b> on the depletion of mitochondrial membrane proteins and bioenergetics point to the potential role of HMOX2 in cancer. Importantly, <b>Au-Mac1</b> inhibits in vivo tumor growth of metastatic breast tumor-bearing mice. We believe that this approach is clinically relevant in network-oriented drug discovery. To the best of our knowledge, <b>Au-Mac1</b> is the first gold complex that targets HMOX2 to elicit an anticancer effect. Chemistry ; Profiling (computer programming) ; Stereochemistry ; Combinatorial chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Ferroptosis and cancer prognosis
Authors: Zhuofan Shi | Xin Chen | Teh‐Ia Huo | Yuntao Jiang | Bowen Wu | Xingyue Chen | Wei Qin | Xinjian Ma | Gang Huang | Zhenyu Wang | Jing Xiang
Source: Scientific Reports (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

A fine-tuned large language model based molecular dynamics agent for code generation to obtain material thermodynamic parameters In the field of materials science, addressing the complex relationship between the material structure and properties has increasingly involved leveraging the text generation capabilities of AI-generated content (AIGC) models for tasks that include literature mining and data analysis. However, theoretical calculations and code development remain labor-intensive challenges. This paper proposes a novel approach based on text-to-code generation, utilizing large language models to automate the implementation of simulation programs in materials science. The effectiveness of automated code generation and review is validated with thermodynamics simulations based on the LAMMPS software as a foundation. This study introduces Molecular Dynamics Agent (MDAgent), a framework designed to guide large models in automatically generating, executing, and refining simulation code. In addition, a thermodynamic simulation code dataset for LAMMPS was constructed to fine-tune the language model. Expert evaluation scores demonstrate that MDAgent significantly improves the code generation and review capabilities. The proposed approach reduces the average task time by 42.22%, as compared to traditional models, thus highlighting its potential applications in the field of materials science. Computer science ; Code (set theory) ; Molecular dynamics ; Dynamics (music) ; Code generation ; Biological system ; Statistical physics ; Programming language ; Chemistry ; Physics ; Biology ; Computational chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Fuel Cells and Related Materials
Authors: Hongliang Xin | John R. Kitchin | Núria López | Neil M. Schweitzer | Nongnuch Artrith | Fanglin Che | Lars C. Grabow | G. T. Kasun Kalhara Gunasooriya | Heather J. Kulik | Teodoro Laino | Suljo Linic | Andrew J. Medford | Randall J. Meyer | Jiayu Peng | C. B. Phillips | Jin Qian | Long Qi | Wendy J. Shaw | Zachary W. Ulissi | Siwen Wang | Xiaonan Wang
Source: ChemRxiv (preprint, 2025-09-12)
Field: Materials Science — Materials Chemistry

Roadmap for Transforming Heterogeneous Catalysis with Artificial Intelligence Artificial intelligence (AI) is poised to transform heterogeneous catalysis, ushering in a new paradigm for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental, and chemical sectors. This promise, however, hinges on overcoming fundamental barriers including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. In this Perspective, we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately agentic future labs to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration. Interpretability ; Heterogeneous catalysis ; Generalizability theory ; Applications of artificial intelligence Machine Learning in Materials Science
Authors: Hongchen Wang | Kangming Li | Scott Ramsay | Yao Fehlis | Edward Kim | Jason Hattrick‐Simpers
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Evaluating the performance and robustness of LLMs in materials science Q&amp;A and property predictions We evaluate LLM performance and robustness for materials science Q&amp;A and property prediction. Prompt sensitivity and mode collapse reveal reliability concerns, providing informed skepticism and guiding more cautious adoption in scientific research. Robustness (evolution) ; Property (philosophy) ; Economics ; Biology ; Genetics ; Epistemology Machine Learning in Materials Science ; Software Engineering Research ; Advanced Materials Characterization Techniques
Authors: N.I. Abu-Elsaad | Rabab A. Metwally | A.S. Nawara
Source: Brazilian Journal of Physics (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Erbium Substituted Mn-Cu Ferrite Nanoparticles: Synthesis, Structural, Magnetic, and Antimicrobial Activity Properties Abstract In this study, the citrate–nitrate auto combustion process was used to create spinel ferrite nanoparticles Mn 0.5 Cu 0.5 Er x Fe 2−x O 4 , where 0.0 ≤ x ≤ 0.075. An analysis was conducted to evaluate the influence of erbium doping on the antimicrobial, microstructural, morphological, and magnetic possessions. A pure single-phase cubic structure is revealed by the structural study of the produced samples with Er doping, but the second phase emerges at x = 0.075. Results show that the lattice parameter grows with incorporating Er 3+ concentration up to x = 0.06, after which it starts to drop at x = 0.075, which lies between 8.432 and 8.446 Å. The TEM pictures revealed nanoscale dimensions and a cubic spinel structure. A rise in the Er 3+ content in Mn-Cu nanoferrites caused a dramatic variation in the magnetic properties. The coercivity varied from 93.60 to 114.89 G, whereas the saturation magnetization (ranging from 54.83 to 40.42 emu/g) and remanence (from 11.57 to 7.61 emu/g) showed a general decreasing trend. The results of the microwave frequency measurements show that ferrites can be used in wireless communication technology. The evaluation of the antimicrobial efficacy of MCEF nanoparticles against pathogenic microorganisms, including Bacillus cereus , Staphylococcus aureus , Pseudomonas aeruginosa , Fusarium oxysporum , Aspergillus flavus , A. terreus , A. fumigatus , A. niger , and Rhizopus sp., demonstrated that MCF nanoparticles doped with erbium at concentrations of 0.03 and 0.075 exhibit enhanced inhibitory effects on the growth of all aforementioned microorganisms. The highest inhibition was observed at an erbium concentration of 0.075, with a mean crystallite size of 39.16 nm, surpassing the inhibition at 0.03 concentration, which had an average size of 49.36 nm, in comparison to the undoped nanoparticles. The microbiological studies demonstrate that these nanoparticles may serve as a prospective antimicrobial agent with reduced effects on P. aeruginosa . Physics ; Ferrite (magnet) ; Antimicrobial ; Nanoparticle ; Erbium ; Nanotechnology ; Condensed matter physics ; Microbiology ; Doping ; Quantum mechanics ; Biology Magnetic Properties and Synthesis of Ferrites ; Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications
Authors: Tuan Anh Tran | Chinh Hoang Tran | Nhuan P. Nghiem | L. V. Truong-Son | Gunel Imanova | S. H. Jabarov
Source: Journal of Materials Science Materials in Engineering (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Effect of doping Mn ion on the crystal structure and cation distribution in Co1-xMnxFe2O4 compounds Abstract In this study, we investigated the effect of Mn doping on the crystal structure and the distribution of cations at tetrahedral and octahedral sites in Co 1- x Mn x Fe 2 O 4 compounds (0 ≤ x ≤ 0.4), synthesized utilizing a solid phase reaction method, utilizing X-ray diffraction (XRD) and Raman spectroscopy measurements. The surface morphology and chemical composition of the compounds were studied using scanning electron microscope and X-ray energy dispersive spectroscopy. The Rietveld refinement of XRD data using the Fullprof package demonstrate that all studied samples are single phase spinels with no other detectable phases and have a cubic structure with the space group of Fd-3 m. The lattice constant and unit-cell volume increased with increased Mn doping concentration, which are attributed to the larger ionic radius of Mn in comparison with Co. Both the X-ray diffraction and Raman spectroscopy results demonstrated that doping Mn at the Co-site leads to the movement of Fe cations from the tetrahedral sites to the octahedral ones. Doping ; Ion ; Materials science ; Crystal structure ; Manganese ; Crystallography ; Chemistry ; Optoelectronics ; Metallurgy ; Organic chemistry Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Vahid Attari | Raymundo Arróyave
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Decoding non-linearity and complexity: deep tabular learning approaches for materials science Materials datasets often contain skewed distributions, broad feature ranges, and multimodal behavior, posing challenges for machine learning. These complexities call for models that capture non-linearity and structure in tabular data. Linearity ; Decoding methods ; Computer science ; Artificial intelligence ; Algorithm ; Engineering ; Electronic engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mineral Processing and Grinding
Authors: Kepeng Chen | X ZHANG | Jike Wang | Dan Li | Tingjun Hou | Wenbo Yang | Yu Kang
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Effective generation of heavy-atom-free triplet photosensitizers containing multiple intersystem crossing mechanisms based on deep learning Photodynamic therapy (PDT) is a clinically approved therapeutic modality that has demonstrated significant potential for cancer treatment, and triplet photosensitizers (PSs) play a key role in its efficacy. Despite deep learning having emerged as a next-generation tool for material discovery, existing methods mainly target a limited subset of triplet PSs, such as thermally activated delayed fluorescence (TADF) materials, neglecting the critical intersystem crossing (ISC) between the high-lying singlet and triplet states (Δ<i>E</i> <sub>S <sub><i>n</i></sub> T <sub><i>n</i></sub> </sub> ). To overcome this limitation, we compiled a comprehensive dataset (∼1.90 × 10<sup>9</sup>) of triplet PSs encompassing various ISC mechanisms. Then, we proposed a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning. <i>In silico</i> experiments revealed that the Frag-MD model outperforms the MD model in generating larger conjugated motifs with higher average ring numbers and atom counts; while the MD model generates twice as many unique motifs and excels in novelty and diversity, as evaluated by conditional and MOSES metrics. Therefore, our approach is highly effective for modifying conjugated motifs and designing novel triplet PSs. Notably, the recently reported high-efficiency triplet PSs have been re-identified through ablation experiments using our proposed models, which target Δ<i>E</i> <sub>S <sub><i>n</i></sub> T <sub><i>n</i></sub> </sub> and significantly outperform traditional baselines, achieving a prediction accuracy of 73% <i>versus</i> 4%. Our approach holds the potential to establish a new paradigm for discovering novel PSs applicable in PDT. Intersystem crossing ; Computer science ; Deep learning ; Photodynamic therapy ; Singlet state ; Artificial intelligence ; Chemistry ; Excited state ; Physics ; Atomic physics Luminescence and Fluorescent Materials ; Advanced Photocatalysis Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Xiaoyu Wu | Jianwen Jiang
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Can large language models predict the hydrophobicity of metal–organic frameworks? Fine-tuning a large language model to predict the hydrophobicity of metal–organic frameworks. Metal-organic framework ; Computer science ; Environmental science ; Chemistry ; Organic chemistry ; Adsorption Machine Learning in Materials Science
Authors: Agnieszka Polowczyk | Alicja Polowczyk | Marcin Woźniak
Source: Scientific Reports (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing Graph neural networks are becoming increasingly popular in deep learning due to their ability to process data in irregular structures and graphs thus preserving additional spatial dependencies due to the arrangement of nodes. This network allows for better accuracy results in classification problems, where information from neighbors is also crucial. However, training such a model is complex and time-consuming and challenging. To date, some metaheuristic algorithms have been used primarily to optimize convolutional networks and find suitable hyperparameters, these are: genetic algorithm, particle swarm optimization, differential evolution or covariance matrix adaptation evolution strategy. In this paper, we propose a metaheuristic algorithm of Simulated Annealing with Uniform distribution for optimization of weights in GCNs, as a hybrid in combination with gradient optimizers. The performance of our technique was tested on the QM7 Dataset, where it was split into two datasets: imbalanced and balanced. Experimental results confirm that our proposed optimization method outperformed other standalone SOTA optimization models, including gradient and heuristics methods, demonstrating in each case to lower loss function values, higher accuracy values for balanced dataset and higher AUC (macro) values for imbalanced dataset. Computer science ; Differential evolution ; Simulated annealing ; Hyperparameter ; Metaheuristic ; CMA-ES ; Particle swarm optimization ; Artificial intelligence ; Algorithm ; Heuristics ; Mathematical optimization ; Machine learning ; Evolution strategy ; Evolutionary algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and ELM
Authors: Mathieu Calvat | C. Bean | Dhruv Anjaria | H. L. Park | Haoren Wang | Kenneth S. Vecchio | Jean‐Charles Stinville
Source: npj Computational Materials (article, 2025-09-01)
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 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) ; Materials science ; Diffraction ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses