jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maharram Jabrayilov | Jeremy B. Essner | Abhijit Bera | V. Paris | Matthew G. Panthani
Source: Dalton Transactions (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Data-driven prediction of HSQ polymer structure and silicon nanocrystal photoluminescence The synthesis of silicon nanocrystals through high-temperature pyrolysis of hydrogen silsesquioxane has emerged as a valuable approach for obtaining quantum-sized crystallites with controllable sizes and distinct photoluminescent maxima. Nevertheless, the use of commercial hydrogen silsesquioxane has notable disadvantages, such as poor shelf life, high cost, and limited supply, that motivate the exploration of alternative precursors. Recent studies have demonstrated silsesquioxane-like polymer precursors derived from molecular polysilanes (<i>e.g.</i>, trichlorosilane, triethoxysilane) that offer a cost-effective and tunable precursor for synthesizing silicon nanocrystal. Here, we elucidate the relationship between the silsesquioxane precursor chemistry, its structure, and the photoluminescence of alkyl-passivated silicon nanocrystals derived from this precursor using a statistical design of experiments technique called response surface methodology. Using this technique, we quantitatively model the relationship between precursor molar ratios, polymer structure (cage <i>vs.</i> network content), and photoluminescence quantum yield of alkyl-passivated silicon nanocrystals. We find that synthesis approaches to silsesquioxane polymers that use higher proportions of methanol and water in a trichlorosilane : water : methanol mixture result in larger amounts of network-type polymer structures, and that the network-type polymeric precursors yield silicon nanocrystals with higher photoluminescence quantum yields. While polymer structure strongly correlates with precursor composition, it is only weakly coorelated to the photoluminescence quantum yield of the resultingsilicon nanocrystals. These finding suggest factors other than precursor structure play significant roles in the photoluminescence of silicon nanocrystals. Nanocrystal ; Photoluminescence ; Materials science ; Polymer ; Silicon ; Nanotechnology ; Optoelectronics ; Composite material Conducting polymers and applications
Authors: Muhammad Khan | Mohamed Hassan Eisa | Xiaohui Sun | Muffarih Shah | Ahmed M. Alkaoud | Katabathini Narasimharao | Zainuriah Hassan
Source: Coordination Chemistry Reviews (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

MXene aerogels: A review on pioneering design strategies, cross-disciplinary applications, and emerging frontier MXenes ; Aerogel ; Flexibility (engineering) ; Characterization (materials science) ; Desalination ; Scalability ; Efficient energy use MXene and MAX Phase Materials ; Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials
Authors: K. Deva Arun Kumar | Kulmani Mehar | Satish Choudhury | D. Dhorajiya Amitkumar | Arvind Kumar Pandey | N. Rajendran | Bishakh Paul | Aseel Samrat
Source: Materials & Design (article, 2025-11-26)
Field: Chemistry — Inorganic Chemistry

Waste-derived nanomaterials: defect engineering, supramolecular assembly, and design rules for sustainable hybrids • Waste streams serve as intrinsic sources of dopants, defects, and supramolecular sites. • Integrated framework links precursor chemistry, defect engineering, and hybrid design. • Thermochemical, biological, and mechanochemical syntheses enable defect modulation. • Structure–defect–function relationships govern multifunctional material performance. • LCA, TEA, AI-driven defect prediction, and digital twins enable scalable deployment. The conversion of waste feedstocks into functional nanomaterials facilitates sustainable methodologies by transforming discarded biomass, polymers, industrial by-products, and electronic waste into valuable hybrid systems. This review rigorously analyzes the manner in which chemical signatures derived from waste influence defect formation, supramolecular assembly, and hierarchical structuring across nano-, meso-, and macro-scales. A comparative evaluation of thermochemical, biological, and mechanochemical methodologies is presented regarding their effectiveness in modulating defects, dopants, and interfaces that are critical to material performance. Quantitative correlations that connect defect metrics to electrochemical, catalytic, and biomedical functionalities are emphasized, substantiated by case studies and performance matrices. Additionally, the review incorporates life cycle and techno-economic assessments, delineating design principles that reconcile high functionality with environmental sustainability. Innovative tools, such as Artificial Intelligence (AI)-based defect prediction and digital twin modeling, are described as facilitators for scalable production. By reframing waste as a resource rich in design potential, this work outlines a strategic framework for the development of next-generation, sustainable hybrid nanomaterials. Structuring ; Scalability ; Supramolecular chemistry ; Cognitive reframing ; Resource efficiency ; Resource (disambiguation) ; Material efficiency ; Sustainability Metal-Organic Frameworks: Synthesis and Applications ; biodegradable polymer synthesis and properties ; Supercapacitor Materials and Fabrication
Source: Journal of Physics Conference Series (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Preface The International Conference on Artificial Intelligence and Materials (ICAIM 2025), sponsored by China University of Petroleum and co-sponsored by Sustainable Solid Energy, took place in Qingdao, China, from April 18 to 20, 2025, uniting global experts at the forefront of AI-driven materials innovation. This premier event highlighted transformative advancements where artificial intelligence intersects with materials science, offering a dynamic platform for researchers to present peer-reviewed breakthroughs, debate emerging methodologies, and forge cross-disciplinary collaborations poised to redefine both fields. The event featured an inaugural ceremony, technical sessions showcasing cutting-edge research, and a closing plenary synthesizing key insights, with keynote lectures anchoring each day’s agenda. Academician Tong-Yi Zhang from Shanghai University’s Materials Genome Institute opened the discourse with “Materials Science and Engineering in the AI Era: Paradigm Shifts and Challenges,” followed by Prof. Shiyu Du of China University of Petroleum elucidating “AI-Driven Autonomous Networks for Next-Generation Communications.” Prof. Bing-Yi Jing from Southern University of Science and Technology then explored “Distributed Computing Architectures for Large-Scale AI Modeling,” while Prof. Jian Sun of Beijing Institute of Technology concluded the keynote series with groundbreaking insights into “Ionic Liquid-Mediated Synthesis of Advanced Functional Materials.” Beyond fostering intellectual exchange, the conference’s success was driven by three pillars: the committee’s meticulous coordination, rigorously implemented peer review processes, and the steadfast dedication of the guest editor, Prof. Huiqiu Deng from Hunan University—efforts that not only ensured seamless operations but also propelled ICAIM’s mission to accelerate AI-powered materials discovery. List of COMMITTEE is available in this PDF. Computer science Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis ; Polyoxometalates: Synthesis and Applications
Authors: Nolan
M. Green (22572962) | Rachel I. Hammond (10682037) | James Planey (22572965) | Nicholas H. Angello (12213512) | Jamie L.B. Putnam (22572968) | Matthew Berry (11196675) | Wenqi He (460919) | Emily Chen (26350) | Santiago Nuñez-Corrales (22572971) | Danielle C. Loving (22572974) | Wesley Wang (6760079) | Tina Huang (249031) | Bhagya Gunasekera (22572977) | Kelly Marville (22572980) | Rachel Switzky (9928765) | Serenity Desmond (22572983) | Martin D. Burke (1280013)
Source: Figshare (article, 2025-11-07)
Field: Chemistry — Physical and Theoretical Chemistry

Illuminatingthe Interface of Blocc Chemistry andData Science: Maximizing Function with ML-Guided Discovery and a DigitalMolecule Maker The intersection of automatable blocc chemistry for iterative carbon–carbon bond formation with artificial intelligence is amplifying molecular innovation in new and exciting ways. In this lab, students are introduced to concepts and tools that help them gain familiarity and confidence with this emerging area of chemistry. Students specifically learn about four automated synthesis platforms, each of which stitches together a bounded set of molecular building blocks using just one type of robust bond-forming reaction. Students then analyze a variety of small molecules and biopolymers to identify the most redundant types of bonds in each molecule that are compatible with iterative formation from bifunctional building blocks. Based on their analysis, students then select an appropriate iterative automated synthesis platform and determine which molecular building blocks would be required to assemble the desired targets. Students next investigate two case studies where blocc chemistry was used used in concert with artificial intelligence to discover new molecular functions. In the first case, students identify high-performing blocks from selected data sets to optimize a single objective function related to organic laser properties. In the second case, students engage with a new online platform inspired by Scratch, dubbed the Digital Molecule Maker (DMM), to perform a multi-objective optimization of organic photovoltaic candidates (OPV). To conclude the lab, students manually perform the blocc chemistry that is foundational for the DMM. This activity series is the second installment of a sequence of undergraduate laboratories designed to illuminate the functional discovery-enabling interface of AI and automated blocc chemistry. Intersection (aeronautics) ; Interface (matter) ; Bifunctional ; Set (abstract data type) ; Function (biology) ; Sequence (biology) ; Representation (politics) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: Nolan M. Green | Rachel I. Hammond | James Planey | Nicholas H. Angello | Jamie L. B. Putnam | Matthew Berry | Wenqi He | Emily Chen | Santiago Núñez-Corrales | Danielle C. Loving | Wesley Wang | Tina Huang | Bhagya Gunasekera | Kelly Marville | Rachel Switzky | Serenity Desmond | Martin D. Burke
Source: Journal of Chemical Education (article, 2025-11-06)
Field: Chemistry — Physical and Theoretical Chemistry

Illuminating the Interface of Blocc Chemistry and Data Science: Maximizing Function with ML-Guided Discovery and a Digital Molecule Maker The intersection of automatable blocc chemistry for iterative carbon-carbon bond formation with artificial intelligence is amplifying molecular innovation in new and exciting ways. In this lab, students are introduced to concepts and tools that help them gain familiarity and confidence with this emerging area of chemistry. Students specifically learn about four automated synthesis platforms, each of which stitches together a bounded set of molecular building blocks using just one type of robust bond-forming reaction. Students then analyze a variety of small molecules and biopolymers to identify the most redundant types of bonds in each molecule that are compatible with iterative formation from bifunctional building blocks. Based on their analysis, students then select an appropriate iterative automated synthesis platform and determine which molecular building blocks would be required to assemble the desired targets. Students next investigate two case studies where blocc chemistry was used used in concert with artificial intelligence to discover new molecular functions. In the first case, students identify high-performing blocks from selected data sets to optimize a single objective function related to organic laser properties. In the second case, students engage with a new online platform inspired by Scratch, dubbed the Digital Molecule Maker (DMM), to perform a multi-objective optimization of organic photovoltaic candidates (OPV). To conclude the lab, students manually perform the blocc chemistry that is foundational for the DMM. This activity series is the second installment of a sequence of undergraduate laboratories designed to illuminate the functional discovery-enabling interface of AI and automated blocc chemistry. Intersection (aeronautics) ; Bifunctional ; Interface (matter) ; Function (biology) ; Set (abstract data type) ; Sequence (biology) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Science Education and Pedagogy
Authors: Federica Bortolussi | James Brean | Shawon Barua | Avinash Kumar | Alexandra Karppinen | Siddharth Iyer | Hilda Sandström | Zongbo Shi | Roy M. Harrison | Patrick Rinke | Matti Rissanen
Source:  (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

Exploring the Role of Highly Oxygenated Organic Molecules in New Particle Formation Events with Explainable Artificial Intelligence New particle formation (NPF) is a significant source of atmospheric aerosols. The formation rate (J) quantifies the rate at which new particles form, and can be empirically determined from the particle number size distribution (PNSD). However, identifying the specific causes of NPF in urban areas remains challenging.It is believed that in urban environments, most NPF occurs through the clustering of sulfuric acid (H2SO4) with bases. However, in a chamber study, it was observed that also the clustering of H2SO4 with highly oxygenated organic molecules (HOMs) may greatly promote NPF (Riccobono, 2014, Science, 344(6185), 717-721).This research project employs AI data-driven approaches to predict J and examine the role of HOMs in NPF events.The data were collected in August 2022 in two close sites in Leipzig, Germany: an urban background (Leibniz Institute for Tropospheric Research) and a roadside (Eisenbahnstra&amp;#223;e). The data include HOMs, H2SO4, and bases from the nitrate CIMS, PNSD measurements, pollutants such as BC, and meteorological variables. Previous research indicated a higher concentration of OOMs at the roadside (Brean, 2024, Environ. Sci. Technol. 58, 10664&amp;#8722;10674), suggesting its potential impact on J.Preliminary results show that our data-driven model successfully predicted J values on a logarithmic scale with a mean absolute error of 0.33 at the urban background site and 0.63 at the roadside. Further analysis reveals the most significant contributors to predicting J, indicating that alongside H2SO4, various HOMs play a crucial role. Organic molecules ; Particle (ecology) ; Artificial intelligence ; Molecule ; Chemistry ; Nanotechnology ; Computer science ; Organic chemistry ; Materials science ; Biology ; Ecology Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Hydrocarbon exploration and reservoir analysis
Authors: Carsten Wickmann | Malte Bruhn | Robin Kratochwill | Bianca Guedert | Berit Zeller-Plumhoff
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Schattenwelten: Mit Schatten Strukturen enthüllen Data-driven Analysis and Design of Materials Set (abstract data type) ; Identification (biology) ; Perspective (graphical) ; Focus (optics) ; Frame (networking) Machine Learning in Materials Science ; Material Selection and Properties ; Physics and Engineering Research Articles
Authors: Abraham Castro Garcia (21417907) | Ken Sakaushi (1299657)
Source: Figshare (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Pitfallsin Artificial Intelligence Powered DiscoveryDue to Electrocatalyst Evaluation Methodologies The explosive increase in popularity and increased accessibility to artificial intelligence and data science tools have opened the door for exploring previously deemed “too large” experimental spaces efficiently in experimental chemistry. This is of special interest for the field of electrocatalysis, where the development of new materials has relied largely on trial-and-error approaches that are not time- and resource-efficient. By leveraging these approaches, we can more effectively find promising electrocatalyst compositions and synthesis conditions that result in lower overpotentials and higher electrocatalyst durability. In this Technical Note, we use oxygen evolution electrocatalysts as a model to highlight potential pitfalls in the use of data-driven strategies for electrocatalyst development, focusing on the important choice of optimization metrics, normalization of data, and common errors that may appear during these kinds of approaches. Electrocatalyst ; Field (mathematics) ; Normalization (sociology) ; Oxygen reduction ; Applications of artificial intelligence ; Popularity Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jian Liu | Tuo Yao | Muyang Li | Sohrab Rohani | Jingkang Wang | Zhenguo Gao | Junbo Gong
Source: Engineering (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Computer Vision-Assisted High-Throughput Screening of Crystallization Additives for Crystal Size, Shape, and Agglomeration Regulation Additives are widely employed to regulate the morphology, size, and agglomeration degree of crystalline materials during crystallization to enhance their functional, physical, and powder properties. However, the existing methods for screening and validating target additives require a large quantity of materials and involve tedious molecular simulation/crystallization experiments, making them time-consuming, resource-intensive, and reliant on the operator’s experience level. To overcome these challenges, we proposed a computer vision-assisted high-throughput additive screening system (CV-HTPASS) which comprises a high-throughput additive screening device, in situ imaging equipment, and an artificial intelligence (AI)-assisted image-analysis algorithm. Using the CV-HTPASS, we performed high-throughput screening experiments on additives to regulate the succinic acid crystal properties, generating thousands of crystal images with diverse crystal morphologies. To extract valuable crystal information from the massive data and improve the analysis accuracy and efficiency, the AI-based image-analysis algorithm was implemented innovatively for the segmentation, classification, and data mining of crystals with four morphologies to further screen the target additive. Subsequently, scale-up crystallization experiments conducted under optimized conditions demonstrated that succinic acid products exhibited a preferred cubic morphology, reduced agglomeration degree, narrowed crystal size distribution, and improved powder properties. The proposed CV-HTPASS offers a highly efficient approach for scale-up experiments. Further, it provides a platform for the screening of additives and the optimization of the powder properties of crystal products in industrial-scale crystallization processes. Economies of agglomeration ; Crystallization ; Throughput ; Crystal (programming language) ; Nanotechnology ; Materials science ; Process engineering ; Computer science ; Chemical engineering ; Engineering ; Telecommunications Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Shreya Gupta | Ethan F. Bull-Vulpe | Henry Agnew | Shishir Iyer | Xuanyu Zhu | Ruihan Zhou | Chris Knight | Francesco Paesani
Source: ChemRxiv (preprint, 2025-02-09)
Field: Materials Science — Materials Chemistry

MBX v1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations Molecular dynamics ; Dynamics (music) ; Statistical physics ; Nanotechnology ; Physics ; Chemistry ; Materials science ; Computational chemistry Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Yutong Lu | Dan Huang | Pin Chen
Source: The International Journal of High Performance Computing Applications (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

HPC-AI coupling methodology for scientific applications Artificial intelligence (AI) technologies have fundamentally transformed numerical-based high-performance computing (HPC) applications with data-driven approaches and endeavored to address existing challenges, e.g., high computational intensity, in various scientific domains. In this study, we explore the scenarios of coupling HPC and AI (HPC-AI) in the context of emerging scientific applications, presenting a novel methodology that incorporates three coupling patterns: surrogate, directive, and coordinate. Each pattern exemplifies a distinct coupling strategy, an AI-driven prerequisite, and typical HPC-AI ensembles. Through case studies in materials science, we demonstrate the application and effectiveness of these patterns. The study highlights technical challenges, performance improvements, and implementation details, providing insight into promising perspectives of HPC-AI coupling. The proposed coupling patterns are applicable not only to materials science but also to other scientific domains, offering valuable guidance for future HPC-AI ensembles in scientific discovery. Coupling (piping) ; Context (archaeology) ; Loose coupling ; Scientific discovery ; Bibliographic coupling ; Close coupling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Xinyu Wu | Ting Liang | Weitao Wang | See PDF
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Flipping Engineering for High Thermal Anisotropy in Janus Two-Dimensional Materials Anisotropy ; Stacking ; Janus ; Phonon ; Thermal conductivity ; van der Waals force ; Thermal ; Thermal fluctuations Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Yutong Lu | Dan Huang | Pin Chen
Source: ArXiv.org (preprint, 2025-06-17)
Field: Materials Science — Materials Chemistry

HPC-AI Coupling Methodology for Scientific Applications Artificial intelligence (AI) technologies have fundamentally transformed numerical-based high-performance computing (HPC) applications with data-driven approaches and endeavored to address existing challenges, e.g. high computational intensity, in various scientific domains. In this study, we explore the scenarios of coupling HPC and AI (HPC-AI) in the context of emerging scientific applications, presenting a novel methodology that incorporates three patterns of coupling: surrogate, directive, and coordinate. Each pattern exemplifies a distinct coupling strategy, AI-driven prerequisite, and typical HPC-AI ensembles. Through case studies in materials science, we demonstrate the application and effectiveness of these patterns. The study highlights technical challenges, performance improvements, and implementation details, providing insight into promising perspectives of HPC-AI coupling. The proposed coupling patterns are applicable not only to materials science but also to other scientific domains, offering valuable guidance for future HPC-AI ensembles in scientific discovery. Coupling (piping) ; Context (archaeology) ; Scientific discovery ; Loose coupling ; Emerging technologies ; Applications of artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications