jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timothy B. Gaines | Camden Boyle | James M. Keller | Matthew R. Maschmann | Stanton R. Price | Grant J. Scott
Source:  (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Scanning Electron Microscope Image Segmentation with Foundation AI Vision Model for Nanoparticles in Autonomous Materials Explorations The role of scientists and engineers is being significantly reshaped by the increased role of artificial intelligence (AI) across various science domains. Materials science is a field that is well-positioned to benefit from the integration of AI in the design of automated experimental systems. This is especially true for systems that are collecting and analyzing massive quantities of data from scanning electron microscopes (SEM). The associated SEM imagery is on the nanoscale, offering unprecedented fidelity to analyze materials in their most basic and simple structures. However, the richness and potential volume of this data necessitate human-machine teaming, specifically AI tools to accelerate scientific discovery. This paper details how the foundation AI vision Segment Anything Model (SAM) is leveraged within an experimental workflow. Specifically, SAM’s basic use requires significant human-in-the-loop effort to mark relevant areas. Herein, we detail how SAM creates excessive segmentation masks and describe the necessary post-processing to refine and filter outputs to be suitable for an autonomous materials experimentation pipeline. Our vision system design that leverages SAM coupled with classical computer vision as a post-processing stage achieves 98–100% precision isolating Aluminum nanoparticles within SEM images. Foundation (evidence) ; Image segmentation ; Artificial intelligence ; Scanning electron microscope ; Computer vision ; Segmentation ; Computer science ; Electron microscope ; Image (mathematics) ; Microscope ; Materials science ; Nanotechnology ; Optics ; Physics ; Composite material ; Geography Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Qingbin Tian | Lansen Bi | Shuyan Lin | Jiangshan Gao | Yan He
Source: Clean Energy Science and Technology (review, 2024-03-29)
Field: Materials Science — Materials Chemistry

A review of cold plasma for catalyst synthesis and modification Cold plasma has been extensively studied and developed in the field of energy storage and conversion, with a focus on its ability to assist in catalyst synthesis, surface modification, the introduction of heteroatoms, the generation of defects and vacancies, the improvement of catalyst dispersion, and the reduction of particle size. In contrast to conventional calcination and chemical methods, the energy from cold plasma can be transferred directly to the catalyst and carrier during the treatment process, which can improve the interaction between the loaded catalyst and carrier by changing the internal structure and surface morphology of the catalyst. Therefore, these properties make cold plasma quite green, safe, and efficient for catalyst synthesis and modification. In this paper, the characteristics and applications of various cold plasma technologies, as well as the synergistic treatment of cold plasma technology with thermodynamic principles on catalysts, are analyzed. Based on current research progress, this paper provides a summary and outlook on the synthesis and modification of catalysts using cold plasma. Plasma ; Catalysis ; Chemistry ; Physics ; Organic chemistry ; Nuclear physics Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; High-Temperature Coating Behaviors
Authors: Sanghoon Lee | Kevin Cruse | Samuel P. Gleason | A. Paul Alivisatos | Gerbrand Ceder | Anubhav Jain
Source: Digital Discovery (article, 2024-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven analysis of text-mined seed-mediated syntheses of gold nanoparticles Gold nanoparticle synthesis recipes were extracted from the literature to obtain data-driven hypotheses for synthesis outcome morphology and size. Used images from https://Flaticon.com. Colloidal gold ; Nanoparticle ; Outcome (game theory) ; Nanotechnology ; Computer science ; Materials science ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Liao Chang | Yemin Yu | Yu Mei | Ying Wei
Source: arXiv (Cornell University) (preprint, 2024-02-02)
Field: Materials Science — Materials Chemistry

From Words to Molecules: A Survey of Large Language Models in Chemistry In recent years, Large Language Models (LLMs) have achieved significant success in natural language processing (NLP) and various interdisciplinary areas. However, applying LLMs to chemistry is a complex task that requires specialized domain knowledge. This paper provides a thorough exploration of the nuanced methodologies employed in integrating LLMs into the field of chemistry, delving into the complexities and innovations at this interdisciplinary juncture. Specifically, our analysis begins with examining how molecular information is fed into LLMs through various representation and tokenization methods. We then categorize chemical LLMs into three distinct groups based on the domain and modality of their input data, and discuss approaches for integrating these inputs for LLMs. Furthermore, this paper delves into the pretraining objectives with adaptations to chemical LLMs. After that, we explore the diverse applications of LLMs in chemistry, including novel paradigms for their application in chemistry tasks. Finally, we identify promising research directions, including further integration with chemical knowledge, advancements in continual learning, and improvements in model interpretability, paving the way for groundbreaking developments in the field. Chemistry ; Computer science ; Linguistics ; Philosophy Machine Learning in Materials Science ; History and advancements in chemistry ; Topic Modeling
Authors: Santiago Morandi | Oliver Loveday | Tim Renningholtz | Sergio Pablo‐García | Rodrigo A. Vargas–Hernández | Ranga Rohit Seemakurthi | Pol Sanz Berman | Rodrigo Garcı́a-Muelas | Alán Aspuru-Guzik | Núria López
Source: ChemRxiv (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

A Foundational Model for Reaction Networks on Metal Surfaces Process optimization in heterogeneous catalysis relies on the control of competing reactions. The reaction mechanisms based on chemical knowledge can be evaluated via density functional theory unveiling experimental catalytic trends. However, this approach finds its limits when applied to complex reaction networks or large molecules, disregarding alternative paths and rare events. Here we present CARE, a foundational model for catalysis on metal surfaces with a rule-based reaction network generator for CxHyOz species built with GAME-Net-UQ, a graph neural network with uncertainty quantification targeting thermodynamic and kinetic parameters, coupled to microkinetic modeling. CARE reproduces experimental activity trends in methanol decomposition, selectivity to C3 products in electrochemical reduction processes, and models the Fischer-Tropsch synthesis to C6 products, including 370k reactions, breaking the current limits of network exploration. This comprehensive model opens the path towards the exploration of thermal and electrocatalytic surface processes previously not amenable to atomistic simulations. Catalysis ; Chemistry ; Computer science ; Biochemical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Zhenru Chen | Yuchao Wu | Yunchao Xie | Kianoosh Sattari | Jian Lin
Source: Materials Horizons (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning enabled virtual experimentation for 3D printed thermoplastic The performance of 3D printed thermoplastics largely depends on the ink formulation, which is composed of tremendous chemical space as an increased number of monomers, making it very difficult to identify an optimum one with desired properties. To tackle this challenge, we demonstrate a virtual experimentation platform that is enabled by a physics-informed machine learning algorithm. As a case study, the algorithm was trained based on a multilayer perceptron (MLP) model to predict the experimental stress-strain curves of the 3D printed thermoplastics given the ink compositions made of six monomers. To solve the issue of experimental data scarcity, we first reduced the dimensions of the curves to eight principal components (PCs), which serve as the outputs of the model. In addition, we incorporated the physics-informed descriptors into the input dataset. These two strategies afford the model with a prediction accuracy of <i>R</i><sup>2</sup> of 0.97 and an RMSE value of 1.01 for fracture strength, and an <i>R</i><sup>2</sup> of 0.95 and a RMSE of 0.40 for toughness. To perform virtual experimentation, the well-trained model was then utilized to predict 100 000 sets of the PCs from the randomly given 100 000 ink formulations. The PC sets were then converted back to the corresponding stress-strain curves. To validate the prediction results, some of the virtual experiments were performed. The results showed a good match between the predicted and experimental curves. This methodology offers a general and efficient pathway to virtual experimentation for establishing the correlation between the complex input variables and the output performance metrics of new materials. 3d printed ; Thermoplastic ; Identification (biology) ; Inkwell ; Virtual screening ; Nanotechnology ; Mechanical engineering ; Artificial intelligence ; Materials science ; Computer science ; Composite material ; Engineering ; Biology ; Biomedical engineering ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Microfluidic and Capillary Electrophoresis Applications
Authors: Zhiqiang Zhong | Kuangyu Zhou | Davide Mottin
Source: arXiv (Cornell University) (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

Benchmarking Large Language Models for Molecule Prediction Tasks Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. Despite the widespread adoption of LLMs in NLP, much of their potential in broader fields remains largely unexplored, and significant limitations persist in their design and implementation. Notably, LLMs struggle with structured data, such as graphs, and often falter when tasked with answering domain-specific questions requiring deep expertise, such as those in biology and chemistry. In this paper, we explore a fundamental question: Can LLMs effectively handle molecule prediction tasks? Rather than pursuing top-tier performance, our goal is to assess how LLMs can contribute to diverse molecule tasks. We identify several classification and regression prediction tasks across six standard molecule datasets. Subsequently, we carefully design a set of prompts to query LLMs on these tasks and compare their performance with existing Machine Learning (ML) models, which include text-based models and those specifically designed for analysing the geometric structure of molecules. Our investigation reveals several key insights: Firstly, LLMs generally lag behind ML models in achieving competitive performance on molecule tasks, particularly when compared to models adept at capturing the geometric structure of molecules, highlighting the constrained ability of LLMs to comprehend graph data. Secondly, LLMs show promise in enhancing the performance of ML models when used collaboratively. Lastly, we engage in a discourse regarding the challenges and promising avenues to harness LLMs for molecule prediction tasks. The code and models are available at https://github.com/zhiqiangzhongddu/LLMaMol. Benchmarking ; Computer science ; Natural language processing ; Artificial intelligence ; Business Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Nourelhouda Bounedjar | Mohammed Fouad Ferhat | Chun Ouyang | M. Bououdina | Ihab Shawish | Rasha A. Abumousa | Muhammad Humayun
Source: Heliyon (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Facile fabrication of ZnO nanoparticles via non-thermal plasma technique and their anti-corrosive effects on X60 API 5L steel in 1M HCl solution This work aims to explore the efficiency of ZnO nanoparticles synthesized via the non-thermal gliding arc discharge-assisted plasma (NT-GAD) technique for inhibiting the corrosion of X60 API 5L steel in a 1M HCl environment. The XRD pattern revealed that the ZnO nanoparticles exhibit hexagonal wurtzite structure with average particle size of ∼24 nm. UV-visible spectroscopy analysis revealed an absorption peak centering at 365 nm, corresponding to an energy band gap of 3.29 eV. SEM and TEM analysis revealed that the nanoparticles exhibit an agglomerated and irregular morphology. The corrosion inhibition of ZnO NPs was investigated via the electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization tests (PDP), while varying both concentration and temperature. The results revealed that the increase in inhibitor concentration resulted in a higher activity at ambient temperature, with an optimal efficiency of 93 % at a concentration of 100 mg/L. However, the increase in temperature remarkably reduced the inhibition efficiency, suggesting a physisorption behavior of ZnO NPs onto the steel surface. AFM and FE-SEM analysis confirmed the formation of a protective layer on the X60 API 5L steel surface. This study emphasizes the significant potential of ZnO NPs synthesized via the NT-GAD assisted plasma technique as corrosion inhibitor for X60 API 5L carbon steel in 1M HCl corrosive media. Fabrication ; Nanoparticle ; Materials science ; Plasma ; Chemical engineering ; Nanotechnology ; Thermal ; Corrosion ; Metallurgy ; Nuclear chemistry ; Chemistry ; Engineering ZnO doping and properties ; Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Aleksei G. Krasnov | Shadrack J. Barnabas | Timo Boehme | Stephen Boyer | Lutz Weber
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Comparing software tools for optical chemical structure recognition The extraction of chemical information from images, also known as Optical Chemical Structure Recognition (OCSR) has recently gained new attention. Computer science ; Software ; Artificial intelligence ; Programming language Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Yiding Wang | Tianqing Li | Hongxiang Zong | Xiangdong Ding | Songhua Xu | Jun Sun | Turab Lookman
Source: npj Computational Materials (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Self-supervised probabilistic models for exploring shape memory alloys Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified. Probabilistic logic ; Shape-memory alloy ; Artificial intelligence ; Computer science ; Machine learning ; Psychology Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Calcium Carbonate Crystallization and Inhibition
Authors: Xiaonan Li | Qin Tong | Wenxu Zhang | Hong Wang | Yanhong Chen | Kangle Li | Qing Wang | Yibo Wang
Source: Nanomaterials (article, 2024-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and Performance Study of Composite Aramid Paper for High-Frequency Working Conditions When the power converter connects to the high-frequency transformer breaks through the bottleneck and reaches a frequency of 100 kHz or even higher, the high-frequency transformer's inter-turn insulation faces more serious high-frequency discharge and high-temperature problems. In order to improve the service performance of oil-immersed high-frequency transformer insulation paper, composite K-BNNS particles are prepared by ultrasonic stripping, heat treatment, and thermomagnetic stirring. Then, K-BNNS particles are mixed with PMIA (polymeric m-phenylenediamine solution) slurry to produce composite aramid paper. And the effects of K-BNNS particles with different contents on the thermal conductivity, dielectric properties, partial discharge properties, and mechanical properties of aramid paper are explored. It can be found that, when the addition of composite particles (K-BNNS) is 10%, the comprehensive performance of composite aramid paper is the best. Compared with Nomex paper, the in-plane and through-plane thermal conductivity of composite insulating paper F-10 increased by 668.33% and 760.66%, respectively. Moreover, the high-frequency breakdown voltage increased by 48.73% and the tensile strength increased by 2.49%. The main reason is that the composite particles form a complete thermal conductive network in the aramid paper matrix and a large number of hydrogen bonds with the matrix, which enhances the internal interface bonding force of the material and changes the charge transport mechanism. Aramid ; Composite number ; Materials science ; Composite material Electromagnetic wave absorption materials ; Fiber-reinforced polymer composites ; Advanced Battery Technologies Research
Authors: Zeinab Ramezani | Sakhrat Khizroev
Source: Journal of Electronic Materials (article, 2024-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Equivalent Circuit Model of Magnetoelectric Composite Nanoparticles Abstract This study presents an analysis of magnetoelectric nanoparticles (MENPs) through the development of equivalent circuits to predict the frequency-dependent magnetoelectric coefficient, with a focus on the widely utilized CoFe 2 O 4 @BaTiO 3 core–shell configuration. This approach involves –derivation of phenomenological expressions that capture the dynamic behavior of MENPs under varying magnetic and electric fields. By integrating piezoelectric and magnetostrictive constitutive equations, along with consideration of dynamic effects and bio-load conjugation, a magneto-elasto-electric effect equivalent circuit has been constructed. This circuit model not only facilitates the investigation of longitudinal data in cube-shaped MENPs but also offers insights into fundamental biological processes. The versatility of this model is shown through translation to other core–shell nanoparticles, composite structures, and multiferroic nanostructures. This analysis provides quantitative predictions of the magnetoelectric coefficients, enhancing general understanding of MENP characteristics across a broad frequency range. Furthermore, the study highlights the framework for future refinement to incorporate intrinsic composition-specific resonances, such as ferromagnetic and ferroelectric resonances, to further significantly improve the nanoparticles’ performance. Overall, this work lays the groundwork for future technology to intelligently and wirelessly control biological processes using MENPs, thus paving a way for innovative biomedical applications. This quantitative approach may facilitate further interdisciplinary research and contribute to advancement of magnetoelectric materials and their applications. Magnetostriction ; Materials science ; Piezoelectricity ; Equivalent circuit ; Multiferroics ; Nanoparticle ; Magnetoelectric effect ; Composite number ; Ferroelectricity ; Computer science ; Nanotechnology ; Magnetic field ; Voltage ; Physics ; Electrical engineering ; Engineering ; Optoelectronics ; Composite material Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics
Authors: Mai M. El-Masry | Rania Ramadan
Source: Adsorption (article, 2024-09-21)
Field: Materials Science — Materials Chemistry

Flash combustion prepared Sm and Co doped Sr hexaferrite for environmental applications Abstract Nanotechnology is offering solutions to water contamination issues, as new techniques are needed to improve the removal of harmful compounds from water bodies. Despite previous reviews on this topic, nanotechnology is paving the way for more effective water treatment methods. Understanding the substitute influence of divalent Co 2+ and rare earth elements Sm 3+ on the structure, magnetic, and removal efficiency of hexagonal ferrites requires an understanding of a sequence of SrFe 12 O 19 , SrFe 11.5 Co 0.5 O 19 , Sr 0.95 Sm 0.05 Fe 12 O 19 , and Sr 0.95 Sm 0.05 Fe 11.5 Co 0.5 O 19 M-type hexagonal ferrites were prepared using the flash technique. The XRD examination revealed that the crystallized material formed a single M-type hexagonal phase. The characteristics of M-type hexagonal ferrites include absorption bands with low wavenumbers in the FTIR curves between 400 to 1000 cm −1 . There was a variation in magnetic characteristics with the replacement of Sm 3+ and Co 2+ doping, possibly due to the spin canting impact created by rare earth Sm 3+ and Co 2+ ions. The goal of the research is to explore the potential of doping magnetic hexaferrites and its influence in wastewater treatment. Various parameters, such as pH and contact duration, that influence the adsorption of lead ions from aqueous solutions were also examined. At pH 7 and 25 °C after 70min, the maximal removal efficiency of the Sr 0.95 Sm 0.05 Fe 11.5 Co 0.5 O 19 was found to be 99%. Magnetic separation was carried out by applying an external magnetic field using a permanent magnet. The strong magnetization of the ferrites (51–58 emu/g) enabled the rapid separation of the magnetic particles from the solution, with over 95% of the ferrite particles being recovered within 10 to 70 min. The Freundlich isotherm model fitted all the isotherm data. Adsorption kinetics were explained by the pseudo-first-order, pseudo-second order, and intraparticle diffusion models. The investigated samples’ adsorption capacity remained efficient till 5 cycles. Flash (photography) ; Combustion ; Doping ; Materials science ; Environmental science ; Nuclear engineering ; Chemistry ; Optoelectronics ; Engineering ; Physics ; Physical chemistry Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Pigment Synthesis and Properties