jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivasheesh Varshney | Sooho Choo | L. F. Thompson | Zhifei Yang | Jay Shah | Jiaxuan Wen | Steven J. Koester | K. Andre Mkhoyan | Alexander McLeod | Bharat Jalan
Source: ACS Nano (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Hybrid Molecular Beam Epitaxy for Single-Crystalline Oxide Membranes with Binary Oxide Sacrificial Layers The advancement in thin-film exfoliation for synthesizing oxide membranes has led to possibilities for creating artificially assembled heterostructures with structurally and chemically incompatible materials. The sacrificial layer method is a promising approach to exfoliate as-grown films from a compatible material system, allowing for their integration with dissimilar materials. Nonetheless, the conventional sacrificial layers often possess an intricate stoichiometry, thereby constraining their practicality and adaptability, particularly when considering techniques such as molecular beam epitaxy (MBE). This is where easy-to-grow binary alkaline-earth-metal oxides with a rock salt crystal structure are useful. These oxides, which include (Mg, Ca, Sr, Ba)O, can be used as a sacrificial layer covering a much broader range of lattice parameters compared to conventional sacrificial layers and are easily dissolvable in deionized water. In this study, we show the epitaxial growth of the single-crystalline perovskite SrTiO<sub>3</sub> (STO) on sacrificial layers consisting of crystalline SrO, BaO, and Ba<sub>1-<i>x</i></sub>Ca<sub><i>x</i></sub>O films, employing a hybrid MBE method. Our results highlight the rapid (≤5 min) dissolution of the sacrificial layer when immersed in deionized water, facilitating the fabrication of millimeter-sized STO membranes. Using high-resolution X-ray diffraction, atomic-force microscopy, scanning transmission electron microscopy, impedance spectroscopy, and scattering-type near-field optical microscopy (SNOM), we demonstrate single-crystalline STO membranes with bulk-like intrinsic dielectric properties. The employment of alkaline earth metal oxides as sacrificial layers is likely to simplify membrane synthesis, particularly with MBE, thus expanding the research and application possibilities. Materials science ; Molecular beam epitaxy ; Membrane ; Oxide ; Thin film ; Chemical engineering ; Nanotechnology ; Heterojunction ; Focused ion beam ; Exfoliation joint ; Fabrication ; Epitaxy ; Layer (electronics) ; Optoelectronics ; Graphene ; Chemistry Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Yuqiang Han | Xiaoyang Xu | Chang‐Yu Hsieh | Keyan Ding | Hongxia Xu | Renjun Xu | Tingjun Hou | Qiang Zhang | Huajun Chen
Source: Nature Communications (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Retrosynthesis prediction with an iterative string editing model Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K. String (physics) ; Retrosynthetic analysis ; Computer science ; Computational biology ; Artificial intelligence ; Natural language processing ; Biology ; Physics ; Chemistry ; Theoretical physics Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Haoyang Mi | Shamilene Sivagnanam | Won Jin Ho | Shuming Zhang | Daniel Bergman | Atul Deshpande | Alexander S. Baras | Elizabeth M. Jaffee | Lisa M. Coussens | Elana J. Fertig | Aleksander S. Popel
Source: Briefings in Bioinformatics (review, 2024-07-25)
Field: Chemistry — Spectroscopy

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. Biomarker discovery ; Proteomics ; Biomarker ; Computational biology ; Precision oncology ; Computer science ; Data science ; Medicine ; Internal medicine ; Biology ; Cancer ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Sudarsan M. Pai | Karim Ali Shah | S. Shyam Sunder | Rodrigo Q. Albuquerque | Christian Brütting | Holger Ruckdäschel
Source: Next Materials (review, 2024-12-14)
Field: Materials Science — Materials Chemistry

Machine learning applied to the design and optimization of polymeric materials: A review Polymeric materials have long been at the forefront of materials science due to their exceptional physical and chemical properties. Designing new polymeric materials traditionally relies on a combination of physical and chemical principles along with empirical trial and error methods. A big issue in this approach is that the traditional trial and error experiments/simulations are often time consuming and sometimes inefficient in order to achieve targeted properties. Machine learning (ML) offers a promising solution to this issue. By leveraging ML algorithms, researchers can accelerate the design process, predict material properties more rapidly, and optimize formulations. ML approaches can analyze vast amounts of data, uncover hidden patterns, and generate predictive models that significantly reduce the time needed to develop materials with desired properties. This paradigm shift allows for more efficient exploration of the material space and faster innovation in polymeric material design. The current review article focuses on the application of ML methods to help design polymeric materials, more especifically, thermosets, thermoplastics and elastomers. Additionally, the review explores future possibilities and opportunities arising from the state-of-the-art advancements in ML, offering perspectives on how these technologies might evolve and influence the polymer science domain. • Tailored ML strategies for thermosets, thermoplastics, and elastomers are provided in this review. • The key role of ML in optimizing formulations and accelerating polymer design is discussed. • Opportunities and challenges are highlighted, encouraging advancements in ML for polymer science. Computer science ; Biochemical engineering ; Process engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Manufacturing Process and Optimization
Authors: Ali Nabi Duman | Almaz S. Jalilov
Source: Materials Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for carbon dot synthesis and applications One of the hottest topics in nanoparticles research right now is carbon dots (CDs). Carbon fibers ; Materials science ; Nanotechnology ; Computer science ; Composite material Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Gas Sensing Nanomaterials and Sensors
Authors: Yue Li | Shurui Wang | Zhou Lv | Zhaoji Wang | Yunbiao Zhao | Ying Xie | Yang Xu | Qian Liu | Yaodong Yang | Ziqiang Zhao | Jin Zhang
Source: Matter (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Transforming the synthesis of carbon nanotubes with machine learning models and automation Carbon nanotube ; Automation ; Nanotechnology ; Materials science ; Computer science ; Engineering ; Mechanical engineering Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Janghoon Ock | Srivathsan Badrinarayanan | Rishikesh Magar | Akshay Antony | Amir Barati Farimani
Source: Nature Machine Intelligence (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Multimodal language and graph learning of adsorption configuration in catalysis Adsorption ; Graph ; Computer science ; Catalysis ; Chemistry ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Quan Zhou | Hongwei Shou | Sicong Qiao | Yuyang Cao | Pengjun Zhang | Shiqiang Wei | Shuangming Chen | Xiaojun Wu | Li Song
Source: Journal of the American Chemical Society (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of Δ<i>G</i><sub>H</sub>, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts. Chemistry ; Catalysis ; Alloy ; Distribution (mathematics) ; Hydrogen ; Series (stratigraphy) ; Metal ; Active site ; Density functional theory ; Statistical physics ; Computational chemistry ; Organic chemistry ; Mathematical analysis ; Mathematics ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Chalcogenide Semiconductor Thin Films
Authors: Xia Zong | Xinran Xu | Dai‐Wen Pang | Xinglu Huang | An‐An Liu
Source: Advanced Healthcare Materials (review, 2024-07-17)
Field: Materials Science — Materials Chemistry

Fine‐Tuning Electron Transfer for Nanozyme Design Nanozymes, with their versatile composition and structural adaptability, present distinct advantages over natural enzymes including heightened stability, customizable catalytic activity, cost-effectiveness, and simplified synthesis process, making them as promising alternatives in various applications. Recent advancements in nanozyme research have shifted focus from serendipitous discovery toward a more systematic approach, leveraging machine learning, theoretical calculations, and mechanistic explorations to engineer nanomaterial structures with tailored catalytic functions. Despite its pivotal role, electron transfer, a fundamental process in catalysis, has often been overlooked in previous reviews. This review comprehensively summarizes recent strategies for modulating electron transfer processes to fine-tune the catalytic activity and specificity of nanozymes, including electron-hole separation and carrier transfer. Furthermore, the bioapplications of these engineered nanozymes, including antimicrobial treatments, cancer therapy, and biosensing are also introduced. Ultimately, this review aims to offer invaluable insights for the design and synthesis of nanozymes with enhanced performance, thereby advancing the field of nanozyme research. Electron transfer ; Electron ; Computer science ; Materials science ; Chemistry ; Physics ; Photochemistry ; Nuclear physics Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Eray Abakay | Mustafa Armağan | Yasemin Yıldıran Avcu | Mert Güney | B.F. Yousif | Egemen Avcu
Source: Frontiers in Materials (review, 2024-09-06)
Field: Materials Science — Materials Chemistry

Advances in improving tribological performance of titanium alloys and titanium matrix composites for biomedical applications: a critical review Titanium (Ti) alloys have been widely used in biomedical applications due to their superior mechanical, physical, and surface properties, while improving their tribological properties is critical to widening their biomedical applications in the current era. The present review examines the recent progress made in enhancing the tribological performance of titanium alloys and titanium matrix composites for biomedical purposes. It specifically focuses on the progress made in biomedical coatings, mechanical surface treatment, and developing titanium matrix composites in terms of their processing, tribological testing conditions, and characterization. Despite thorough investigations, the specific testing procedures for evaluating the friction and wear properties of the alloy and/or biomedical component are still uncertain. The majority of researchers have selected test methods and parameters based on previous studies or their own knowledge, but there is a scarcity of studies that incorporate limb-specific tribological tests that consider the distinct kinematic and biological structure of human limbs. Since advanced microscopy has great potential in this field, a variety of advanced characterization techniques have been used to reveal the relationship between microstructural and tribological properties. Many coating-based strategies have been developed using anodizing, PEO, VD, PVD, nitriding, thermal spray, sol-gel, and laser cladding, however; composition and processing parameters are crucial to improving tribological behaviour. Reinforcing component type, amount, and distribution has dominated Ti matrix composite research. Ti grade 2 and Ti6Al4V alloy has been the most widely used matrix, while various reinforcements, including TiC, Al 2 O 3 , TiB, hydroxyapatite, Si 3 N 4 , NbC, ZrO 2 have been incorporated to enhance tribological performance of Ti matrix. Mechanical surface treatments improve biomedical Ti alloys’ tribological performance, which is advantageous due to their ease of application. The implementation of machine learning methods, such as artificial neural networks, regression, and fuzzy logic, is anticipated to make a substantial contribution to the field due to their ability to provide cost-effective and accurate results. The microstructural and surface features of biomedical Ti alloys directly affect their tribological properties, so image processing strategies using deep learning can help researchers optimize these properties for optimal performance. Tribology ; Materials science ; Titanium ; Composite material ; Titanium alloy ; Matrix (chemical analysis) ; Metallurgy ; Alloy Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Metal and Thin Film Mechanics
Authors: Benjamin M. Comer | Neha Bothra | Jaclyn R. Lunger | Frank Abild‐Pedersen | Michal Bajdich | Kirsten T. Winther
Source: ACS Catalysis (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Prediction of O and OH Adsorption on Transition Metal Oxide Surfaces from Bulk Descriptors In the search for stable and active catalysts, density functional theory and machine learning-based models can accelerate the screening of materials. While stability is conveniently addressed on the bulk level of computation, the modeling of catalytic activity requires expensive surface simulations. In this work, we develop models for the surface adsorption energy of O and OH intermediates across a consistent and extensive data set of pure transition metal oxide surfaces. We show that adsorption energies across metal oxidation states of +2 to +6 are well captured from the metal–oxygen bond strength extracted from the bulk level calculation. Specifically, we calculate the integrated crystal orbital Hamiltonian population (ICOHP) of the metal–oxygen bond in the bulk oxide and employ a simple normalization scheme to obtain a strong correlation with the adsorption energetics. By combining our ICOHP descriptor with non-DFT features in a Gaussian Process regression (GPR) model, we achieve a high model accuracy with mean absolute errors of 0.166 and 0.219 eV for OH and O adsorption, respectively. By targeting the adsorption energy difference of the OH–OH adsorption with our GPR model, we predict the oxygen evolution reaction activity from bulk descriptors only. Furthermore, we utilize the strong correlation between the COHP and metal–oxygen bond lengths to rapidly predict the adsorption energetics and catalytic activity from the optimized bulk geometry. Our approach can enable an efficient search for active catalysts by eliminating the need for surface calculations in the initial screening phase. Adsorption ; Density functional theory ; Oxide ; Catalysis ; Bond length ; Transition metal ; Chemistry ; Chemical physics ; Bond strength ; Physical chemistry ; Metal ; Computational chemistry ; Materials science ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Debjyoti Bhattacharya | Harrison J. Cassady | Michael A. Hickner | Wesley F. Reinhart
Source: Journal of Chemical Information and Modeling (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Large Language Models as Molecular Design Engines The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although generative machine learning methods have recently shown potential for computational molecular design, their use is hindered by complex training procedures, and they often fail to generate valid and unique molecules. In this context, pretrained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts. In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with impressive 97% valid and unique molecules. By quantifying these modifications in a low-dimensional latent space, we systematically evaluate the model's behavior under different prompting conditions. Notably, the model is able to perform guided molecular generation when asked to manipulate the electronic structure of molecules using simple, natural-language prompts. Our findings highlight the potential of LLMs as powerful and versatile molecular design engines. Chemical space ; Computer science ; Context (archaeology) ; Generative grammar ; Simple (philosophy) ; Generative model ; Natural language ; Space (punctuation) ; Human–computer interaction ; Data science ; Artificial intelligence ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xudong Sun | Bowen Chen | Zhenshengnan Li | Yongjie Shan | Minghong Jian | Xianying Meng | Zhenxin Wang
Source: Chemical Engineering Journal (article, 2024-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate diagnosis of thyroid cancer using a combination of surface-enhanced Raman spectroscopy of exosome on MXene-coated gold@silver core@shell nanoparticle substrate and deep learning Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Detection limit ; Materials science ; Nanoparticle ; Nanotechnology ; Silver nanoparticle ; Substrate (aquarium) ; Biomedical engineering ; Analytical Chemistry (journal) ; Raman scattering ; Chemistry ; Chromatography ; Medicine ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Extracellular vesicles in disease
Authors: Duong Q. Nguyen | Kim Q. Tran | Thinh D. Le | Magd Abdel Wahab | H. Nguyen‐Xuan
Source: Computer Methods in Applied Mechanics and Engineering (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

A data-driven uncertainty quantification framework in probabilistic bio-inspired porous materials (Material-UQ): An investigation for RotTMPS plates Uncertainty quantification ; Probabilistic logic ; Porosity ; Porous medium ; Computer science ; Materials science ; Mathematics ; Composite material ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Manufacturing Process and Optimization
Authors: Jia Si | Panpan Zhang | Chenyi Zhao | Dongyi Lin | Lin Xu | Haitao Xu | Lijun Liu | Jianhua Jiang | Lian‐Mao Peng | Zhiyong Zhang
Source: Nature Electronics (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

A carbon-nanotube-based tensor processing unit Carbon nanotube ; Unit (ring theory) ; Nanotube ; Materials science ; Nanotechnology ; Psychology ; Mathematics education Carbon Nanotubes in Composites ; Parallel Computing and Optimization Techniques ; Interconnection Networks and Systems