jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. Chandana | Pritom J. Bora | Praveen C. Ramamurthy | Nagarajan Raghavan
Source: Applied Materials Today (article, 2024-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring robust microwave absorption properties of functional polyvinyl butyral (PVB) via facile cross-linking and data-driven material discovery Polyvinyl butyral ; Materials science ; Microwave ; Absorption (acoustics) ; Composite material ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Dielectric materials and actuators
Authors: Wenhao Yu | Jing Li | Hanyun Li | Fengling Shi | Guoqing Wu
Source: Materials Today Communications (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Data-driven insights into the dynamic operational performance of large-scaled complex titanium alloy castings Materials science ; Alloy ; Titanium alloy ; Metallurgy Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; High Temperature Alloys and Creep
Authors: Clémence Courrégelongue | Damien Baigl
Source: Chemistry - A European Journal (review, 2024-11-14)
Field: Materials Science — Materials Chemistry

Functionalization of Emulsion Interfaces: Surface Chemistry Made Liquid Disperse systems, and emulsions in particular, are currently massively used in fields as varied as food industry, cosmetics, health care and environmentally-friendly materials. To meet increasingly precise needs or targeted applications, these systems need to be endowed with new functionalities at their interfaces, in addition to their composition and structural properties. However, due to the fragility of drops and the low reactivity of their surface, conventional solid surface chemistry cannot be used for such a purpose. Several specific emulsion interface functionalization techniques have thus been developed for targeted systems and applications, but a general framework has yet to be drawn. In this review, we attempt to present these methods in a unified way through the prism of what we may call "liquid surface chemistry". We propose to categorize existing methods into drop-coating strategies, including layer-by-layer techniques and polymer coating, with a particular focus on polydopamine, and emulsifier-carrier approaches involving particles and/or amphiphilic molecules. They are discussed in a transversal way, highlighting the underlying physico-chemical principles and providing a comparative analysis of their advantages, current limitations and potential for improvement. We also propose future directions and opportunities, involving for instance DNA-based programmability or artificial intelligence, which could make liquid surface chemistry more versatile and controlled. Surface modification ; Coating ; Nanotechnology ; Emulsion ; Amphiphile ; Materials science ; Computer science ; Polymer ; Chemistry ; Chemical engineering ; Organic chemistry ; Engineering ; Composite material Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Polymer Surface Interaction Studies
Authors: Sina Hossein Zadeh | Cem Cakirhan | Danial Khatamsaz | John Broucek | Timothy D. Brown | Xiaoning Qian | İbrahim Karaman | Raymundo Arróyave
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Study of Composition-Dependent Phase Compatibility in Niti Shape Memory Alloys Nickel titanium ; Compatibility (geochemistry) ; Shape-memory alloy ; Materials science ; Metallurgy ; Composite material Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Nathan Marchack | Eric Joseph
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2024-08-06)
Field: Materials Science — Polymers and Plastics

Centering Sustainability in Process Development Through Improved Characterization of HFC-PFC Byproducts Recent legislation concerning the potential regulation of per- and polyfluorinated chemistries as a class (PFAS) has enormous implications for the field of semiconductor manufacturing. This is so because the material foundation of silicon and its dielectrics which prevails across all advanced device technologies dictates the usage of fluorocarbon gases, particularly for subtractive patterning and chamber cleaning processes. Simultaneously, recent progresses in artificial intelligence have spurred large investments in fabrication plants to produce the critical hardware driving that field. As these trends stand in clear opposition to each other, it is increasingly important for synergy between research fields to center sustainability as a key parameter of technological development. The first critical step for such research efforts involves improved characterization of the fluorinated byproducts created in such manufacturing processes. The use of low-temperature plasma discharges tremendously increases the complexity of the available reaction pathways, making this task significantly more challenging. In this paper we present a novel technical analysis method applied to the fluorocarbon polymerization remaining on through-silicon via sidewalls patterned using the Bosch Process. Fluorinated carbon fragments with longer chain lengths than the starting gas precursor molecule were detected, which represents to the best of our knowledge the first time this has been reported in literature. This baseline will be invaluable in future research efforts to assess novel chemistries and abatement treatments. Sustainability ; Process (computing) ; Characterization (materials science) ; Process development ; Engineering ; Sustainable development ; Manufacturing engineering ; Process engineering ; Systems engineering ; Computer science ; Materials science ; Nanotechnology Polymer Foaming and Composites ; Injection Molding Process and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Giulia Bellini | Gregor Koch | Frank Girgsdies | Jinhu Dong | Spencer J. Carey | Olaf Timpe | Gudrun Auffermann | Matthias Scheffler | Robert Schlögl | Lucas Foppa | Annette Trunschke
Source: Angewandte Chemie International Edition (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence The identification of key materials' parameters correlated with the performance can accelerate the development of heterogeneous catalysts and unveil the relevant underlying physical processes. However, the analysis of correlations is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant. Here, we tackle these challenges for the CO oxidation catalyzed by perovskites using a combination of rigorous experiments and artificial intelligence. A series of 13 ABO<sub>3</sub> (A=La, Pr, Nd, Sm; B=Cr, Mn, Fe, Co) perovskites was synthesized, characterized, and tested in catalysis. To the resulting dataset, we applied the symbolic-regression SISSO approach. We identified an analytical expression correlated with the activity that contains the normalized unit-cell volume, the Pauling electronegativity of the elements A and B, and the ionization energy of the element B. Therefore, the activity is described by crystallographic distortions and by the chemical nature of A and B elements. The generalizability of the identified descriptor is confirmed by the good quality of the predictions for 3 additional ABO<sub>3</sub> and 16 chemically more complex AMn<sub>(1-x)</sub>B'<sub>x</sub>O<sub>3</sub> (A=La, Pr, Nd; B'=Fe, Co, Ni, Cu, Zn) perovskites. Electronegativity ; Catalysis ; Generalizability theory ; Materials science ; Binding energy ; Crystallography ; Chemistry ; Physics ; Mathematics ; Atomic physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Simon‐Johannes Burgdorf | Thomas Roddelkopf | Kerstin Thurow
Source: Chemie Ingenieur Technik (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks Abstract Crystallization is critical in producing various substances in the food and pharmaceutical industries. The process begins with nucleation from a supersaturated solution, followed by the directed movement of solute molecules toward the newly formed nuclei. This movement is influenced by different factors such as molecular attraction and thermodynamic properties, which influence the resulting crystal size and shape. This paper presents an automated monitoring approach for the growth process of crystallization from saturated solutions. To monitor such a process, a combination of artificial intelligence and computer vision is used. The approach was developed for application in material research to automate the processing of numerous samples, which would otherwise require laboratory personnel. Crystallization ; Computer science ; Computer vision ; Artificial intelligence ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Chaoqun Li | Weijie Yang | Hao Liu | Xin‐Yuan Liu | Xiujing Xing | Zhengyang Gao | Shuai Dong | Hao Li
Source: Angewandte Chemie (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Picturing the Gap Between the Performance and US‐DOE's Hydrogen Storage Target: A Data‐Driven Model for MgH<sub>2</sub> Dehydrogenation Abstract Developing solid‐state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid‐state hydride. Transition state search and kinetics calculations are essential to understanding and designing high‐performance solid‐state hydrogen storage materials by filling in the knowledge gap that current experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally expensive and time‐consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges in this field. Herein, we develop a data‐driven model to describe and predict the dehydrogenation barriers of a typical solid‐state hydrogen storage material, magnesium hydride (MgH 2 ), based on the combination of the crystal Hamilton population orbital of Mg−H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH 2 closer to the target set by the United States Department of Energy (US‐DOE). Dehydrogenation ; Hydrogen storage ; Hydrogen ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Catalysis ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Superconductivity in MgB2 and Alloys
Authors: Glenn K. K. Clothier | Thiago R. Guimarães | Steven Thompson | Shaun C. Howard | Benjamin W. Muir | Graeme Moad | Per B. Zetterlund
Source: Angewandte Chemie (article, 2024-02-24)
Field: Chemistry — Organic Chemistry

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion Abstract Synthetic polymers are of paramount importance in modern life ‐ an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high‐throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically&gt;100,000 g mol −1 ) using reversible addition‐fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures. Copolymer ; Monomer ; Polymer ; Chain transfer ; Materials science ; Raft ; Polymerization ; Polymer science ; Polymer chemistry ; Nanotechnology ; Computer science ; Radical polymerization Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Block Copolymer Self-Assembly
Authors: Abigail K. Mann | Samuel J. Tonkin | Pankaj Sharma | Christopher T. Gibson | Justin M. Chalker
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Polymers and Plastics

Probe-Based Mechanical Data Storage on Polymers Made by Inverse Vulcanization Fueled by big data and artificial intelligence, data-centric innovations are expected to rise exponentially in the near future. This change demands rapid advancement in alternative data storage solutions and material platforms. Probe-based data storage, such as mechanical storage using an atomic force microscope tip, offers a way forward and achieves high-density storage exceeding 4 Tb/in², surpassing hard disks. However, the storage medium must be modifiable and erasable on the nanoscale. While polymers are highly promising, they face challenges with stability, synthesis, erasing temperatures, surface roughness, and elasticity. In this study, we report a low-cost and robust polymer system that allows repeated writing, reading and erasing. The polymer was made by inverse vulcanization, a process that provides polysulfide networks with S-S bonds that can be broken and re-formed repeatedly. This property was leveraged in mechanical indentation to encode information, and thermal S-S metathesis and polymer re-flow to erase the information. Exquisite control of depth was possible for making indents over a range of 1-30 nm. This control of depth allowed encoding information using a ternary method, where the data encoded is not just a function of the presence or absence of an indent, but also the depth of the indent. This ternary coding method—the first reported for mechanical information storage on polymers—increases the data density in comparison to binary coding four-fold. Furthermore, the encoding could be done at room temperature which is rare for mechanical information storage. The polymers could also be cast as films, which facilitated rapid erasing of indents in seconds at a relatively low temperature of 140 ºC. The low cost, ease of synthesis, and dynamic S-S bonds in these polymers constitute a promising, new option for polymer storage media in probe-based data storage. Materials science ; Polymer ; Computer data storage ; Ternary operation ; Indentation ; Shape-memory polymer ; Nanotechnology ; Nanoscopic scale ; Vulcanization ; Composite material ; Computer science ; Computer hardware ; Natural rubber Synthesis and properties of polymers ; Advanced Sensor and Energy Harvesting Materials ; Nanofabrication and Lithography Techniques
Authors: Ping Sheng | Shengyi Li | Li Xu | Bo Wang | Huitao Bai | Hui Li | Qing Xue
Source: Journal of Computational Methods in Sciences and Engineering (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Prospects for the database development in electrical engineering materials With the proposal and development of the Material Genome Engineering program, artificial intelligence has played a significant role in accelerating the research and development of new materials. In the field of electrical engineering materials, high-throughput experimental and computational methods provide a huge amount of data. It also poses new challenges to how to manage material data scientifically and efficiently. Database technology has become a hot topic for material scientists and engineers. This paper makes a comprehensive overview of the development, demand analysis and application of database technology in the electrical engineering materials, and discusses the existing problems and the future development trend of the database. Compared with many materials, such as energy materials, catalytic materials, biomedical materials, etc., the electrical material database still has a long way to go in the process of database platform construction, management and operation, and practical application. However, driven by governmental support and market demand, the construction of electrical material database will gradually improve and play an important role in the data-driven new materials researches. Computer science ; Database ; Field (mathematics) ; Process (computing) ; Systems engineering ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Stephen T. Kelly | Yulia Trenikhina | W. C. Harris
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Surfaces, Coatings and Films

AI-Powered 3D Image Reconstruction for Analyzing Lithium Ion Battery Microstructures at the Nanoscale A clean energy future will rely heavily on lithium-ion batteries to shift away from carbon-based energy sources. These devices are closed off from the outside world and operate under highly non-ambient conditions (e.g., liquid electrolytes, air-free environments). Their performance is largely driven by the 3D microstructures of the internal components and their evolutions. For example, breached layers in batteries due to lithium dendrite growth can lead to catastrophic failures and safety concerns. As such, imaging the internal microstructures and processes is important for observing and measuring device construction details and understanding how defects are distributed and change performance during operation. In particular, 3D imaging allows for analysis of features and parameters that can only be fully understood in 3D, like porosity and tortuosity, and can also provide realistic microstructural input for 3D modelling and simulation approaches. Within this scope, X-ray microscopy provides a unique method to image electrochemical devices because of the non-destructive nature of the technique. Modern x-ray microscopes enable 3D imaging even at the nanoscale, bringing capabilities once reserved for the synchrotron into the laboratory. Advances in tomographic image reconstruction have allowed researchers to increasingly maximize the impact of x-ray microscopy data through higher image quality and enabling faster data acquisition schemes. Recently, artificial intelligence (AI) has been incorporated into these algorithms, dramatically increasing the performance and capability of the technique. Here, we apply multiple novel reconstruction technologies that leverage AI to dramatically improve the performance of laboratory-based nanoscale x-ray microscopes, showing examples of improvements in imaging lithium-ion battery electrode materials. We show how AI-powered tomographic reconstruction can reveal new levels of detail in lithium-ion battery materials and enable up to 10 times faster data collection across the micrometer and nanometer length-scales. These capabilities shift the paradigm in volume analysis for electrochemical devices and give researchers unprecedented insight into the detailed microstructures that drive device performance, enabling new levels of understanding and helping to power the drive to a clean energy future. Nanoscopic scale ; Materials science ; Lithium (medication) ; Microstructure ; Battery (electricity) ; Ion ; Lithium-ion battery ; Nanotechnology ; Engineering physics ; Metallurgy ; Psychology ; Chemistry ; Engineering ; Physics ; Power (physics) Electron and X-Ray Spectroscopy Techniques
Authors: Hilary Egan | Max C. Gallant | Davi Fébba | Nick Wunder | J. V. Smith | Andriy Zakutayev
Source:  (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

HTEM AI-XRD: Large-scale deployment of AI interpretation of X-Ray Diffraction Spectra Experimentally synthesizing predicted materials in a reproducible manner remains a key bottleneck in materials science progress. Autonomous synthesis and closed loop integration of prediction and characterization can address these issues, however, this requires autonomous characterization methods for all analysis including crystallographic phase identification which currently remains a rate-limiting step. Here we demonstrate initial results in applying a collection of Artificial Intelligence (AI)-based X-Ray Diffraction (XRD) analysis methods at scale, using a workflow running across various compute platforms from High Performance Computing (HPC) to off-premise cloud. We detail the workflow methods, compare model performance on synthetic and experimental data across a wide-array of material systems, and analyze the performance of the AI methods in comparison to manually labeled experimental data. Interpretation (philosophy) ; Diffraction ; Software deployment ; X-ray crystallography ; Materials science ; Scale (ratio) ; Spectral line ; Computer science ; Crystallography ; Physics ; Optics ; Chemistry ; Astronomy ; Operating system ; Programming language Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Geoffrey A. Ozin | Todd Siler | Chenxi Qian | Wenjie Zhou
Source: Matter (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

The curiosity-creativity element in HI-AI materials discovery Curiosity ; Creativity ; Intuition ; Element (criminal law) ; Human intelligence ; Psychology ; Discovery learning ; Cognitive science ; Mathematics education ; Social psychology ; Developmental psychology ; Political science Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation