jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyu Li | He Han | Nikolaos Evangelou | Noah J. Wichrowski | Peng Lü | Wenqian Xu | Son‐Jong Hwang | Wenyang Zhao | Chunshan Song | Xinwen Guo | Aditya Bhan | Ioannis G. Kevrekidis | Michael Tsapatsis
Source: Nature Communications (article, 2023-05-31)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted crystal engineering of a zeolite It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na<sub>2</sub>O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8). Zeolite ; Catalysis ; Aluminosilicate ; Materials science ; Crystallization ; Dehydrogenation ; Faujasite ; Fluid catalytic cracking ; Chemical engineering ; Chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Atul Mishra | Snehal Rajput | Meera Karamta | Indrajit Mukhopadhyay
Source: ACS Omega (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes Unlike conventional liquid electrolytes, solid-state electrolytes (SSEs) have gained increased attention in the domain of all-solid-state lithium-ion batteries (ASSBs) due to their safety features, higher energy/power density, better electrochemical stability, and a broader electrochemical window. SSEs, however, face several difficulties, such as poorer ionic conductivity, complicated interfaces, and unstable physical characteristics. Vast research is still needed to find compatible and appropriate SSEs with improved properties for ASSBs. Traditional trial-and-error procedures to find novel and sophisticated SSEs require vast resources and time. Machine learning (ML), which has emerged as an effective and trustworthy tool for screening new functional materials, was recently used to forecast new SSEs for ASSBs. In this study, we developed an ML-based architecture to predict ionic conductivity by utilizing the characteristics of activation energy, operating temperature, lattice parameters, and unit cell volume of various SSEs. Additionally, the feature set can identify distinct patterns in the data set that can be verified using a correlation map. Because they are more reliable, the ensemble-based predictor models can more precisely forecast ionic conductivity. The prediction can be strengthened even further, and the overfitting issue can be resolved by stacking numerous ensemble models. The data set was split into 70:30 ratios to train and test with eight predictor models. The maximum mean-squared error and mean absolute error in training and testing for the random forest regressor (RFR) model were obtained as 0.001 and 0.003, respectively. Overfitting ; Ionic conductivity ; Computer science ; Mean squared error ; Random forest ; Ensemble learning ; Stacking ; Electrolyte ; Materials science ; Machine learning ; Artificial intelligence ; Data mining ; Statistics ; Artificial neural network ; Mathematics ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Nanna Ahlmann Ahm
Source: Journal of Computational Intelligence in Materials Science (article, 2023-06-10)
Field: Materials Science — Materials Chemistry

Machine Learning Approches for Evaluating the Properties of Materials Machine Learning for Materials Science is a primer on the subject that also delves into the specifics of where ML might be applied to materials science research. With a focus on where to collect data and some of the issues when choosing a strategy, this article includes example approaches for ML applied to experiments and modeling, such as the first steps in the procedure for constructing an ML solution for a materials science problem. The lengthy cycles of development, inefficiencies, and higher costs of conventional techniques of material discovery, such as the density functional theory- based and empirical trials and errors approach, make it impossible for materials research to keep up with modern advances. Hence, machine learning is extensively employed in material detection, material design, and material analysis because of its cheap computing cost and fast development cycle, paired with strong data processing and good prediction performance. This article summarizes recent applications of ML algorithms within different material science fields, discussing the advancements that are needed for widespread application, and details the critical operational procedures involved in evaluating the features of materials using ML. Computer science ; Focus (optics) ; Artificial intelligence ; Machine learning ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Naomi Omori | Antonia D. Bobitan | Antonis Vamvakeros | Andrew M. Beale | Simon D. M. Jacques
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (review, 2023-09-11)
Field: Materials Science — Materials Chemistry

Recent developments in X-ray diffraction/scattering computed tomography for materials science X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'. Tomography ; Diffraction tomography ; Scattering ; Diffraction ; Focus (optics) ; Computed tomography ; Tomographic reconstruction ; Iterative reconstruction ; Materials science ; Medical physics ; Computer science ; Physics ; Optics ; Radiology ; Medicine ; Artificial intelligence X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Daniel Wines | Tian Xie | Kamal Choudhary
Source: The Journal of Physical Chemistry Letters (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models Finding new superconductors with a high critical temperature (<i>T</i><sub>c</sub>) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique structures and chemical compositions. Specifically, we used a crystal diffusion variational autoencoder (CDVAE) along with atomistic line graph neural network (ALIGNN) pretrained models and the Joint Automated Repository for Various Integrated Simulations (JARVIS) superconducting database of density functional theory (DFT) calculations to generate new superconductors with a high success rate. We started with a DFT data set of ∼1000 superconducting materials to train the diffusion model. We used the model to generate 3000 new structures, which along with pretrained ALIGNN screening results in 61 candidates. For the top candidates, we performed DFT calculations for validation. Such approaches go beyond funnel-like materials screening approaches and allow for the inverse design of next-generation materials. Superconductivity ; Autoencoder ; Computer science ; Generative model ; Artificial neural network ; Density functional theory ; Set (abstract data type) ; Inverse ; Statistical physics ; Generative grammar ; Artificial intelligence ; Theoretical computer science ; Physics ; Condensed matter physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Adnan Hosen | Sumaiya Yeasmin | K.M. Sayem Bin Rahmotullah | Md. Ferdous Rahman | Sheikh Rashel Al Ahmed
Source: Optics & Laser Technology (article, 2023-09-16)
Field: Materials Science — Materials Chemistry

Design and simulation of a highly efficient CuBi2O4 thin-film solar cell with hole transport layer Heterojunction ; Materials science ; Optoelectronics ; Photovoltaic system ; Solar cell ; Fabrication ; Semiconductor ; Capacitance ; Thin film ; Computer science ; Nanotechnology ; Electrode ; Electrical engineering Copper-based nanomaterials and applications ; ZnO doping and properties ; Chalcogenide Semiconductor Thin Films
Authors: José Ilton de Oliveira Filho | Murilo Calil Faleiros | Daísy Camargo Ferreira | Veerappan Mani | K. Saláma
Source: Advanced Intelligent Systems (article, 2023-08-18)
Field: Chemistry — Electrochemistry

Empowering Electrochemical Biosensors with AI: Overcoming Interference for Precise Dopamine Detection in Complex Samples Two significant issues in biosensors that can't be solved by conventional analytical methods are selectivity among likely biological interfering molecules and background noise in clinical samples. The application of embedded machine learning in the removal of background interference, which is extremely common in complex matrix solutions such as cerebrospinal fluid, is still unexplored. The implementation of machine learning into devices and sensors can enhance their reliability to discriminate responses. In addition, implementing these models into portable devices can further improve the usage of point‐of‐care devices, continuous monitoring, and viral mutation assessments. Herein, the requirements to implement an embedded AI model (TinyML) into low‐power portable systems and its use in the electrochemical field are presented. The application of TinyML to discriminate between interference of uric acid and ascorbic acid, both well‐known abundant electrochemically active species, in neurotransmitter detection, is explored, reaching overall accuracy of 98.1% for 32‐bit float point unit and 96.01% after 8‐bit quantization, with the usage of 4.55% of the custom‐made potentiostat memory. The model can be improved simply by having the trade‐off between memory and accuracy. The research suggests that TinyML can be a key component in future medical devices, allowing data processing in real time and with increasing reliability. Computer science ; Potentiostat ; Reliability (semiconductor) ; Ascorbic acid ; Biosensor ; Interference (communication) ; Computer hardware ; Embedded system ; Nanotechnology ; Materials science ; Power (physics) ; Chemistry ; Telecommunications Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Deyang Yu | Shasha Cheng | Yu Li | Wentao Su | Mingqian Tan
Source: Critical Reviews in Food Science and Nutrition (review, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Recent advances on natural colorants-based intelligent colorimetric food freshness indicators: fabrication, multifunctional applications and optimization strategies With the increasing concerns of food safety and public health, tremendous efforts have been concentrated on the development of effective, reliable, nondestructive methods to evaluate the freshness level of different kinds of food. Natural colorants-based intelligent colorimetric indicators which are typically constructed with natural colorants and polymer matrices has been regarded as an innovative approach to notify the customers and retailers of the food quality during the storage and transportation procedure in real-time. This review briefly elucidates the mechanism of natural colorants used for intelligent colorimetric indicators and fabrication methodologies of natural colorants-based food freshness indicators. Subsequently, their multifunctional applications in intelligent food packaging systems like antioxidant packaging, antimicrobial packaging, biodegradable packaging, UV-blocking packaging and inkless packaging are well introduced. This paper also summarizes several optimizing strategies for the practical application of this advanced technology from different perspectives. Strategies like adopting a hydrophobic matrix, constructing double-layer film and encapsulation have been developed to improve the stability of the indicators. Co-pigmentation, metal ion complexation, pigment-mixing and using substrates with high surface area are proved to be effective to enhance the sensitivity of the indicators. Approaches include multi-index evaluation, machine learning and smartphone-assisted evaluation have been proven to improve the accuracy of the intelligent food freshness indicators. Finally, future research opportunities and challenges are proposed. Based on the fundamental understanding of natural colorants-based intelligent colorimetric food freshness indicators, and the latest research and findings from literature, this review article will help to develop better, lower cost and more reliable food freshness evaluation technique for modern food industry. Active packaging ; Food packaging ; Computer science ; Biochemical engineering ; Food industry ; Natural food ; Process engineering ; Engineering ; Chemistry ; Food science Dye analysis and toxicity ; Nanocomposite Films for Food Packaging ; Advanced Nanomaterials in Catalysis
Authors: Madhusudan G. Lanjewar | Pranay P. Morajkar | Jivan S. Parab
Source: Food Control (article, 2023-09-11)
Field: Chemistry — Analytical Chemistry

Portable system to detect starch adulteration in turmeric using NIR spectroscopy Near-infrared spectroscopy ; Mean squared error ; Mathematics ; Starch ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Biological system ; Statistics ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Umar Noor | Muhammad Furqan Mughal | Toheed Ahmed | Muhammad Fayyaz Farid | Muhammad Ammar | Umme Kulsum | Amna Saleem | Mahnoor Naeem | Aqsa Khan | Ammara Sharif | Kashif Waqar
Source: Nanotechnology (review, 2023-03-27)
Field: Materials Science — Materials Chemistry

Synthesis and applications of MXene-based composites: a review Recently, there has been considerable interest in a new family of transition metal carbides, carbonitrides, and nitrides referred to as MXenes (Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i>) due to the variety of their elemental compositions and surface terminations that exhibit many fascinating physical and chemical properties. As a result of their easy formability, MXenes may be combined with other materials, such as polymers, oxides, and carbon nanotubes, which can be used to tune their properties for various applications. As is widely known, MXenes and MXene-based composites have gained considerable prominence as electrode materials in the energy storage field. In addition to their high conductivity, reducibility, and biocompatibility, they have also demonstrated outstanding potential for applications related to the environment, including electro/photocatalytic water splitting, photocatalytic carbon dioxide reduction, water purification, and sensors. This review discusses MXene-based composite used in anode materials, while the electrochemical performance of MXene-based anodes for Li-based batteries (LiBs) is discussed in addition to key findings, operating processes, and factors influencing electrochemical performance. MXenes ; Materials science ; Anode ; Carbon nanotube ; Nanotechnology ; Nitride ; Electrochemistry ; Carbide ; Photocatalysis ; Electrode ; Composite material ; Layer (electronics) ; Catalysis MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Memory and Neural Computing
Authors: Hung Vo Thanh | Sajad Ebrahimnia Taremsari | Benyamin Ranjbar | Hossein Mashhadimoslem | Ehsan Rahimi | Mohammad Rahimi | Ali Elkamel
Source: Energies (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H2 storage using four nature-inspired algorithms applied in the random forest (RF) model. Various carbon-based adsorbents, chemical activating agents, ratios, micro-structural features, and operational parameters as input variables are applied in the ML model to predict H2 uptake (wt%). Particle swarm and gray wolf optimizations (PSO and GWO) in the RF model display accuracy in the train and test phases, with an R2 of ~0.98 and 0.91, respectively. Sensitivity analysis demonstrated the ranks for temperature, total pore volume, specific surface area, and micropore volume in first to fourth, with relevancy scores of 1 and 0.48. The feasibility of algorithms in training sizes 80 to 60% evaluated that RMSE and MAE achieved 0.6 to 1, and 0.38 to 0.52. This study contributes to the development of sustainable energy sources by providing a predictive model and insights into the design of porous carbon adsorbents for hydrogen storage. The use of nature-inspired algorithms in the model development process is also a novel approach that could be applied to other areas of materials science and engineering. Microporous material ; Adsorption ; Porosity ; Hydrogen storage ; Algorithm ; Carbon fibers ; Volume (thermodynamics) ; Materials science ; Random forest ; Process engineering ; Computer science ; Environmental science ; Hydrogen ; Chemical engineering ; Biological system ; Machine learning ; Chemistry ; Engineering ; Thermodynamics ; Composite material ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Hybrid Renewable Energy Systems
Authors: Mohammad Suhaan Dar | Tanveer A. Tabish | Nanasaheb D. Thorat | G. Swati | Niroj Kumar Sahu
Source: APL Bioengineering (review, 2023-08-21)
Field: Materials Science — Materials Chemistry

Photothermal therapy using graphene quantum dots The rapid development of powerful anti-oncology medicines have been possible because of advances in nanomedicine. Photothermal therapy (PTT) is a type of treatment wherein nanomaterials absorb the laser energy and convert it into localized heat, thereby causing apoptosis and tumor eradication. PTT is more precise, less hazardous, and easy-to-control in comparison to other interventions such as chemotherapy, photodynamic therapy, and radiation therapy. Over the past decade, various nanomaterials for PTT applications have been reviewed; however, a comprehensive study of graphene quantum dots (GQDs) has been scantly reported. GQDs have received huge attention in healthcare technologies owing to their various excellent properties, such as high water solubility, chemical stability, good biocompatibility, and low toxicity. Motivated by the fascinating scientific discoveries and promising contributions of GQDs to the field of biomedicine, we present a comprehensive overview of recent progress in GQDs for PTT. This review summarizes the properties and synthesis strategies of GQDs including top-down and bottom-up approaches followed by their applications in PTT (alone and in combination with other treatment modalities such as chemotherapy, photodynamic therapy, immunotherapy, and radiotherapy). Furthermore, we also focus on the systematic study of <i>in vitro</i> and <i>in vivo</i> toxicities of GQDs triggered by PTT. Moreover, an overview of PTT along with the synergetic application used with GQDs for tumor eradication are discussed in detail. Finally, directions, possibilities, and limitations are described to encourage more research, which will lead to new treatments and better health care and bring people closer to the peak of human well-being. Photothermal therapy ; Nanomedicine ; Nanotechnology ; Radiation therapy ; Quantum dot ; Cancer therapy ; Biomedicine ; Photodynamic therapy ; Modalities ; Materials science ; Medicine ; Cancer ; Nanoparticle ; Chemistry ; Bioinformatics ; Surgery ; Internal medicine Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications