jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peng Zhang | Yuxi Song | Lijuan Hu | Wen Ma | Shitong Xu | Meiyi Yao
Source: Computational Materials Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Tuning thermal conductivity of thermal barrier ceramic SrZrO3 via La doping: Computational study using machine learning potential Thermal conductivity ; Molecular dynamics ; Phonon ; Workflow ; Anisotropy ; Thermal barrier coating ; Interatomic potential ; Thermal Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Zan Tang | Zongquan Zhang | Lirong Qiu
Source:  (article, 2025-10-28)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-driven rapid hydrophobicity prediction for high-efficiency fog collection on laser-textured brass surfaces Water scarcity remains a critical global challenge, and fog collection has emerged as a promising solution to harness alternative freshwater sources. However, conventional fabrication of fog collection devices, typically relying on chemical methods, suffers from complex procedures and environmental risks. This study introduces a deep-learning strategy to predict the hydrophobic contact angle of laser-processed square-groove arrays on brass surfaces directly from microscopic images, aiming to optimize superhydrophobic/hydrophilic hybrid surfaces for efficient fog collection. By training a convolutional neural network (CNN) on 1000+ microstructure images with corresponding contact angle data, the method achieves 92.3% prediction accuracy and reduces surface functionalization time by 80% compared to conventional approaches. This enables real-time optimization of laser parameters (e.g., spacing, power) to tailor wettability for fog harvesting. Experiments show that brass surfaces fabricated via this method exhibit stable superhydrophobicity (water contact angle &gt;150°) after heat treatment, eliminating the need for time-consuming posttreatment drying processes. The integration of deep learning with laser processing facilitates the construction of highperformance hybrid wettability surfaces, with potential to enhance fog collection efficiency—supporting sustainable freshwater generation in arid and water-scarce regions. Contact angle ; Brass ; Wetting ; Convolutional neural network ; Fabrication ; Microstructure ; Surface (topology) Surface Modification and Superhydrophobicity ; Solar-Powered Water Purification Methods ; Advanced Sensor and Energy Harvesting Materials
Authors: A.R. Bestugin | E.A. Gushchina | E.A. Frolova
Source: Radioengineering (article, 2025-05-05)
Field: Materials Science — General Materials Science

Methodology of diagnostics of polymer composite materials composition Modern methods of determining the optimal composition of polymer composite materials are based on multi-stage experimental studies that require significant time, financial and labor resources. The traditional approach involves a large number of laboratory tests to select components and their ratios, which leads to high costs and increases the environmental load due to energy consumption, chemical reagents and waste generation. In this regard, the actual task is the development of alternative methods that allow to reduce the volume of experimental studies without losing the accuracy of predicting the properties of materials. The aim of the work is to develop a methodology for automated selection of polymer composite compositions, which allows to significantly reduce the number of tests and minimize costs. In the course of the research, a model using machine learning and statistical analysis methods was developed to determine the composition of polymer composites based on a set of experimental data. The proposed approach demonstrates high efficiency, reducing the number of required tests by 19 times compared to traditional methods. The application of the technique provides significant economic benefits, reducing the cost of research from 17-73 million rubles to 0.9-4 million rubles depending on the complexity of the composition of composite material. An important achievement of the work is a significant reduction of environmental load - CO₂ emissions during the development process are reduced from 605 kg to 32 kg per research cycle. For practical implementation of the method a specialized software interface was created, which provides convenient input of initial data, automated selection of optimal compositions and clear visualization of the obtained results. The developed methodology makes it possible to significantly accelerate the creation of new polymer composites with specified properties, which is especially in demand in the aircraft, automotive and medical industries. Reduction of R&amp;D costs makes the technology affordable for small and medium-sized enterprises, and minimization of environmental impact corresponds to the principles of sustainable development. The developed software product can be easily integrated into existing computer-aided design systems, ensuring quick implementation into production processes. Work (physics) ; Process (computing) ; Composite number ; Selection (genetic algorithm) ; Material selection ; Interface (matter) ; Reagent ; Software Material Properties and Applications ; Polymer Science and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Jiale Zhao | Cong Liu | Yuxuan Zhang | Chengyue Gong | Zhenyi Zhang | Shifeng Jin | Zhenyu Liu
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases. Diffraction ; Benchmark (surveying) ; Electron diffraction ; Crystal structure ; Phase (matter) ; Crystal (programming language) ; Generative model ; X-ray crystallography Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: U.S. Rai | Projesh Saha | Bibhas Pandit | Manisha Dahal | Soumya Banerjee | A. K. Rakshit
Source: International Journal of Pharmacy and Pharmaceutical Research (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review on The Aspect of Green Analytical Chemistry in Pharmaceutical Analysis The aim of this review is to elaborate the concept of Green Analytical Chemistry and explore its utility in pharmaceutical analysis sector.Several article and literature reviews were studied to understand the concept of Green Analytical Chemistry and the impact it has in safeguarding the environment and ensuring sustainability of resources.The 12 principles of Green Analytical Chemistry have contributed significantly in implementing Green Analytical Chemistry in the agricultural, industrial as well as petrochemical sector.Metrices like Analytical Eco-Scale, Analytical Greenness, Atomic Efficiency Score and Green Analytical Procedure Index have proved to be a significant contributor in analysing the greenness of the analytical procedures and techniques.It is essential to understand the strategies that are required for implementing Green Analytical Chemistry in pharmaceutical analysis like miniaturization, green solvent usage, energy efficient technologies, automation & online analysis.The challenges & limitations of Green Analytical Chemistry like economic and logistical barriers to adopting green methods, balancing green practices with regulatory requirements and analytical performance, etc need to be overcome efficiently in order to ensure their optimum application and implementation in the coming future.The future perspectives of Green Analytical Chemistry including role of artificial intelligence and machine learning in optimizing green methods & potential for integrating Green Analytical Chemistry with other sustainable practices in the pharmaceutical industry need to be given special emphasis to maximise the benefits of Green Analytical Chemistry in the coming future. Biochemical engineering ; Management science ; Nanotechnology ; Chemistry ; Computer science ; Engineering ; Materials science Dye analysis and toxicity ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Hyunsoo Hong | Samuel Kim | Jee‐Eun Lee | Seong Su Kim
Source: International Journal of AI for Materials and Design (review, 2025-08-04)
Field: Materials Science — Materials Chemistry

A comprehensive review of artificial intelligence applications in composite materials: Predictive, generative, and automation approaches The rapid advancement of artificial intelligence (AI) has led to its widespread adoption across various engineering fields, including composite materials research. Composite materials, known for their superior mechanical properties and lightweight characteristics, play a crucial role in industries such as aerospace, automotive, and robotics. However, their inherent complexity&amp;ndash;such as anisotropic behavior, nonlinear characteristics, and intricate microstructures&amp;ndash;poses significant challenges for traditional design and analysis methods. To address these challenges, AI-driven approaches have emerged as powerful tools, offering solutions in prediction, generation, and automation. This review systematically explores applications of machine learning and deep learning in composite materials research, categorized into three major approaches: predictive, generative, and automation models. Predictive models enhance the accuracy of property prediction and microstructure analysis. Generative models facilitate novel material discovery and microstructure design. Automatic models improve quality control and can be used to optimize manufacturing processes through real-time data analysis. By leveraging diverse large-scale datasets, AI provides innovative solutions to the key challenges associated with composite materials and enhances research and design efficiency. This review highlights the transformative potential of AI in composite materials research, providing insights into future research directions and challenges. Artificial intelligence ; Automation ; Generative grammar ; Computer science ; Robotics ; Flexibility (engineering) ; Machine learning ; Automotive industry ; Transformative learning ; Data science ; Engineering ; Robot ; Mechanical engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Material Selection and Properties
Authors: Filippozzi, Davide | Mayer, Alexandre | Roy, Nicolas | Fang, Wei | Rahimi-Iman, Arash
Source: Figshare (preprint, 2025-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches Chiral photonic metasurfaces provide unique capabilities for tailoring light-matter interactions, which are essential for next-generation photonic devices. Here, we report an advanced optimization framework that combines deep learning and evolutionary algorithms to significantly improve both the design and performance of chiral photonic nanostructures. Building on previous work utilizing a three-layer perceptron reinforced learning and stochastic evolutionary algorithm with decaying changes and mass extinction for chiral photonic optimization, our study introduces a refined pipeline featuring a two-output neural network architecture to reduce the trade-off between high chiral dichroism (CD) and reflectivity. Additionally, we use an improved fitness function, and efficient data augmentation techniques. A comparative analysis between a neural network (NN)-based approach and a genetic algorithm (GA) is presented for structures of different interface pattern depth, material combinations, and geometric complexity. We demonstrate a twice higher CD and the impact of both the corner number and the refractive index contrast at the example of a GaP/air and PMMA/air metasurface as a result of superior optimization performance. Additionally, a substantial increase in the number of structures explored within limited computational resources is highlighted, with tailored spectral reflectivity suggested by our electromagnetic simulations, paving the way for chiral mirrors applicable to polarization-selective light-matter interaction studies. Artificial neural network ; Photonics ; Genetic algorithm ; Evolutionary algorithm ; Perceptron ; Pipeline (software) ; Genetic programming ; Multilayer perceptron Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Neural Networks and Reservoir Computing
Authors: Beatriz Peñín | Nil Sanosa | Cecilia Merino‐Robledillo | Aimara García‐Camacho | Diego Sampedro | Julian A. Hueffel | Ignacio Funes‐Ardoiz
Source: Angewandte Chemie (article, 2025-10-11)
Field: Chemistry — Organic Chemistry

Mapping Boryl Radical Properties and Reactivity Using Machine Learning: The B‐Rad and React‐B‐Rad Maps Abstract Boryl radicals have become indispensable in organic synthesis, yet, translating their complex steric and electronic properties into actionable reactivity insights remains challenging. Herein, we present a comprehensive classification of boryl radicals, including a publicly accessible database of 141 neutral 7e‐4c boryl radicals, each parametrized by a set of electronic and steric features derived from DFT calculations. Unsupervised machine learning ( k‐ means clustering) and dimensionality reduction (PCA/UMAP) condense this high dimensional descriptor space into the “B‐rad map”, capturing trends in sterics and electronics among the resulting five clusters. Global electrophilicity ( ω ) and nucleophilicity (N) indices are overlaid to create a polarity‑annotated guide, while DFT‑computed activation free energies for six benchmark reactions (HAT, radical addition, and XAT for two different substrates) yield the React‑B‑rad maps that directly link intrinsic properties to specific reaction performance. To demonstrate predictive power, supervised machine learning models (random forest) are trained on the descriptors and successfully predict radical reactivity regimes across all reaction types. Overall, this integrated, machine‐learning‐driven platform can serve as both a practical guide for experimental decision‐making and a foundation for data‐driven discovery, paving the way towards rational design and virtual screening of boryl‐radical reagents for diverse synthetic applications. Steric effects ; Reactivity (psychology) ; Nucleophile ; Electrophile ; Benchmark (surveying) ; Radical ; Electronic effect ; Dimensionality reduction Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Machine Learning in Materials Science
Authors: Ian Chesser | Mashroor S. Nitol | Esther C. Hessong | Himanshu Joshi | Nikhil Chandra Admal | Brandon Runnels | Daniel N. Blaschke | Khanh Dang | Abigail Hunter | Saryu Fensin
Source: Acta Materialia (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

The structure and migration of twin boundaries in tetragonal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si48.svg" display="inline" id="d1e1440"><mml:mi>β</mml:mi></mml:math>-Sn: An application of machine learning based interatomic potentials Tetragonal crystal system ; Scalable Vector Graphics ; Materials science ; Computer graphics (images) ; Crystallography ; Computer science ; World Wide Web ; Crystal structure ; Chemistry Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Semiconductor materials and interfaces
Authors: Kushal Chakraborty | V. Chandrasekar | Dhananjay Kumar | Nabin Baran Manik
Source: Solid-State Electronics (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Machine learning impassioned assessment of trap states driven band bending analysis in organic Schottky formation Trap (plumbing) ; Bending ; Optoelectronics ; Materials science ; Schottky diode ; Mechanical engineering ; Electrical engineering ; Chemistry ; Engineering ; Composite material ; Environmental engineering Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Memory and Neural Computing
Authors: Nathan J. Meigs | Logan D. Gibb | Nicholas Kurtyka | Aaron M. Chalifoux | Tara Mastren | Luther W. McDonald
Source: ACS Omega (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Comparative Morphological Signatures of Strike Ordered Uranium Oxides for Nuclear Forensics Nuclear forensics remains critical in addressing threats associated with the illicit and malicious use of radioactive materials, necessitating continuous advancements in the characterization of uranium ore concentrates (UOCs). In this study, we investigate the influence of precipitation strike order on the morphological features of U<sub>3</sub>O<sub>8</sub> derived from four synthetic routes: ammonium uranyl carbonate (AUC), ammonium diuranate (ADU), uranyl peroxide (UO<sub>4</sub>), and magnesium diuranate (MDU). Machine learning classification was performed to differentiate between the synthetic route and strike order, capturing subtle variations in product morphology. Morphological signatures of scanning electron microscopy (SEM) images were analyzed using a convolutional neural network (CNN) architecture based on a ResNet34 backbone for transfer learning, with images processed into "spot" locations that combine multiple magnifications and detector modes to enhance feature extraction. While most synthetic pathways displayed marked differences in morphology between strike orders (<i>p</i> < 0.05), the magnesium diuranate (MDU) route exhibited only minor morphological differences, 42-72% discrimination accuracy, and no statistical significance, as determined by the CNN analysis. These findings indicate that the dependence of the final morphology of UOCs on strike order is dependent on the synthetic route, resulting in distinct signatures for AUC, ADU, and UO<sub>4</sub> but not MDU for the synthesis processes used. A distinct strike order fingerprint may have wider implications for forensic attribution and process monitoring. Convolutional neural network ; Ammonium carbonate ; Uranium ; Uranyl ; Feature (linguistics) ; Pattern recognition (psychology) ; Characterization (materials science) ; Process (computing) Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Jian Zhou | Weiren Wang | Xue Jiang | Bing Bai | Yanjing Su
Source: Journal of Nuclear Materials (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

LLMs and NLP integration enables ion irradiation-induced swelling prediction in stainless steels Swelling ; Pipeline (software) ; Interpretability ; Consistency (knowledge bases) ; Ion ; Irradiation ; Sample (material) ; Scalability Machine Learning in Materials Science ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Bhaskarrao Yakkala | Raja Mohan | Vijayaragavan Elumalai | Muthuraj Balakrishnan | L. Umasankar
Source: Journal of Computational Electronics (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

A hybrid artificial intelligence framework for predicting electrical and thermal properties of graphene nanoplatelet-enhanced nanoelectronic materials Graphene ; Artificial neural network ; Nanoelectronics ; Mean squared error ; Thermal conductivity ; Reliability (semiconductor) ; Convolutional neural network ; Thermal Graphene research and applications ; Semiconductor materials and devices ; Advanced Memory and Neural Computing
Authors: Pandit, Prakul | Kanagasenthinathan, Sugan | Rege, Ameya Govind
Source: elib (German Aerospace Center) (article, 2025-01-01)
Field: Chemistry — Spectroscopy

3D Microstructure Reconstruction of Aerogels via Conditional GANs Aerogels are low-density and highly porous materials (90–99% porosity) with exceptional thermal and mechanical properties, governed by their intricate nanoporous microstructure. Understanding their structure-property relationships is essential for optimizing their performance across industrial applications. A sig- nificant challenge appears in precisely identifying the complete pore space and thus mapping their microstructural morphology of aerogels. This work presents a deep learning-driven digital twin framework for aerogels, leveraging Conditional Generative Adversarial Networks (cGANs) and Convolutional Neural Networks (CNNs) for 3D microstructure reconstruction and predictive modeling. Our ap- proach reconstructs 3D aerogel microstructures from synthetic 2D scanning elec- tron microscopy (SEM) images that mimic real samples by incorporating depth effects. A CNN predicts key microstructural parameters, including pore radius, relative density, and pore size distribution, with minimal error. A 3D cGAN then generates aerogel microstructures by capturing global spatial features and conditioning on the extracted parameters. We demonstrate that conditioning improves the fidelity of reconstruction by en- forcing physically meaningful constraints. This method provides a scalable, data- driven approach for microstructure modeling, enabling efficient structure-property predictions, and guiding aerogel design for targeted applications. Microstructure ; Aerogel ; Nanoporous ; Porosity ; Convolutional neural network ; Porous medium ; Microscopy Aerogels and thermal insulation ; Nanoporous metals and alloys ; Transition Metal Oxide Nanomaterials