jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raffaella Buonsanti | Ludovic Zaza | Bojana Rankovic | Philippe Schwaller
Source: Research Square (preprint, 2024-12-13)
Field: Materials Science — Materials Chemistry

A holistic data-driven approach to synthesis predictions of colloidal nanocrystal shapes Toolbox ; Nanotechnology ; Nanocrystal ; Biomedicine ; Computer science ; Field (mathematics) ; Scale (ratio) ; Colloid ; Materials science ; Chemistry ; Physics ; Mathematics ; Bioinformatics Machine Learning in Materials Science ; Laser-Ablation Synthesis of Nanoparticles
Authors: Neal Munyebvu | Zarina Akhmetbayeva | Steve Dunn | Philip D. Howes
Source: Nanoscale Advances (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Flow synthesis and multidimensional parameter screening enables exploration and optimization of copper oxide nanoparticle synthesis Copper-based nanoparticles (NPs) are highly valued for their wide-ranging applications, with particular significance in CO<sub>2</sub> reduction. However current synthesis methods encounter challenges in scalability, batch-to-batch variation, and high energy costs. In this work, we describe a novel continuous flow synthesis approach performed at room temperature to help address these issues, producing spherical, colloidally stable copper(ii) oxide (CuO) NPs. This approach leverages stabilizing ligands like oleic acid, oleylamine, and soy-lecithin, a novel choice for CuO NPs. The automated flow platform facilitates facile, real-time parameter screening of Cu-based nanomaterials using optical spectroscopy, achieving rapid optimization of NP properties including size, size dispersity, and colloidal stability through tuning of reaction parameters. This study highlights the potential of continuous flow synthesis for efficient parameter exploration to accelerate understanding, optimization, and eventually enable scale-up of copper-based NPs. This promises significant benefits for various sectors, including energy, healthcare, and environmental conservation, by enabling reliable production with reduced energy and cost requirements. Copper ; Nanoparticle ; Flow (mathematics) ; Materials science ; Copper oxide ; Nanotechnology ; Ranging ; Reduction (mathematics) ; Oxide ; Computer science ; Biological system ; Mathematics ; Mechanics ; Metallurgy ; Physics Copper-based nanomaterials and applications ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Sambarta Chatterjee | William M. Jacobs
Source: arXiv (Cornell University) (preprint, 2024-01-20)
Field: Materials Science — Materials Chemistry

Multi-objective optimization for targeted self-assembly among competing polymorphs Most approaches for designing self-assembled materials focus on the thermodynamic stability of a target structure or crystal polymorph. Yet in practice, the outcome of a self-assembly process is often controlled by kinetic pathways. Here we present an efficient machine learning-guided design algorithm to identify globally optimal interaction potentials that maximize both the thermodynamic yield and kinetic accessibility of a target polymorph. We show that optimal potentials exist along a Pareto front, indicating the possibility of a trade-off between the thermodynamic and kinetic objectives. Although the extent of this trade-off depends on the target polymorph and the assembly conditions, we generically find that the trade-off arises from a competition among alternative polymorphs: The most kinetically optimal potentials, which favor the target polymorph on short timescales, tend to stabilize a competing polymorph at longer times. Our work establishes a general-purpose approach for multi-objective self-assembly optimization, reveals fundamental trade-offs between crystallization speed and defect formation in the presence of competing polymorphs, and suggests guiding principles for materials design algorithms that optimize for kinetic accessibility. Isotropy ; Crystallization ; Multi-objective optimization ; Kinetic energy ; Inverse ; Lattice (music) ; Stability (learning theory) ; Materials science ; Mathematical optimization ; Statistical physics ; Computer science ; Chemical physics ; Mathematics ; Thermodynamics ; Physics ; Geometry ; Classical mechanics ; Machine learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Pickering emulsions and particle stabilization
Authors: Víctor Sabanza-Gil | Riccardo Barbano | Daniel Pacheco Gutiérrez | Jeremy S. Luterbacher | José Miguel Hernández-Lobato | Philippe Schwaller | Loı̈c M. Roch
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences. Bayesian probability ; Bayesian optimization ; Fidelity ; Computer science ; High fidelity ; Data science ; Artificial intelligence ; Engineering Machine Learning in Materials Science
Authors: Aditya Raghavan | Rohit Pant | Ichiro Takeuchi | Eugene А. Eliseev | Martí Checa | Anna N. Morozovska | Maxim Ziatdinov | Sergei V. Kalinin | Yongtao Liu
Source: arXiv (Cornell University) (preprint, 2024-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evolution of ferroelectric properties in SmxBi1-xFeO3 via automated Piezoresponse Force Microscopy across combinatorial spread libraries Combinatorial spread libraries offer a unique approach to explore evolution of materials properties over the broad concentration, temperature, and growth parameter spaces. However, the traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we demonstrate the application of automated Piezoresponse Force Microscopy (PFM) for the exploration of the physics in the SmxBi1-xFeO3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allows PFM-based gird sampling over macroscopic samples. The concentration dependence of pertinent ferroelectric parameters has been determined and used to develop the mathematical framework based on Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in the high-throughput materials discovery. We make the data sets open to the community and hope that will stimulate other efforts to interpret and understand the physics of these systems. Piezoresponse force microscopy ; Ferroelectricity ; Microscopy ; Nanotechnology ; Atomic force microscopy ; Materials science ; Computer science ; Physics ; Optoelectronics ; Optics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Characterization and Applications of Magnetic Nanoparticles
Authors: Yanjun Lai
Source: Applied Mathematics and Nonlinear Sciences (article, 2024-01-01)
Field: Materials Science — General Materials Science

Quantitative study of the effect of high-performance zinc alloy coating technology on the life of mechanical components Abstract Corrosion of mechanical components has been of wide interest in recent decades. Corrosion is caused by physicochemical action between a metal and its environment, which can result in changes in the metal’s properties and functional damage in mechanical components. The main corrosion manifestations of zinc alloy coatings in marine environments are discussed in this paper, which first explores the application of high-performance aluminum alloy coatings in industry. Subsequently, the ZAS35 alloy used for this paper was experimentally prepared, and orthogonal tests were utilized to determine the optimum matching values of process parameters for zinc alloy coatings to generate materials. The hardness and wear resistance of ZAS35 were evaluated against other zinc alloys. An iterative learning control algorithm was employed to determine the thickness of the zinc alloy coating. The optimal control of the steady-state process of coating thickness can be achieved by using the NARX dynamic neural network model as a predictive identification model for zinc alloy coating thickness. Finally, data were collected using an optical microscope to quantitatively analyze the effect of zinc alloy plating on mechanical life. When the thickness of zinc alloy is 1.7 μm , the probability of life is [947.56,978.36]×10 3 h interval t=0.999, which is improved by 409~684.11×10 3 h compared with the plating thickness of 1.10 μm . After adding aluminum elements to zinc plating, the corrosion potential of the plating decreases from −800mV to −1000mV, and the zinc-aluminum alloy prevents electrochemical corrosion of the plated layer with cathodic corrosion inhibition. Coating ; Zinc ; Alloy ; Metallurgy ; Materials science ; Composite material Material Properties and Applications ; Material Properties and Failure Mechanisms ; Metal and Thin Film Mechanics
Authors: Miguel Hernández‐del‐Valle | Jorge Ilarraza-Zuazo | Enrique Dios-Lázaro | Javier Rubio | Joris Audoux | Maciej Harańczyk
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Biomaterials

Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration <i>via</i> automated material compounding We present do-it-yourself instruments that can be both adopted and adapted to fit your self-driving lab. Pellet ; Compounding ; Distributor ; Nanocomposite ; Materials science ; Space (punctuation) ; Process engineering ; Computer graphics (images) ; Nanotechnology ; Computer science ; Composite material ; Engineering ; Aerospace engineering ; Operating system biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Graphene and Nanomaterials Applications
Authors: A N M Nafiz Abeer | Sanket Jantre | Nathan M. Urban | Byung-Jun Yoon
Source: arXiv (Cornell University) (preprint, 2024-05-31)
Field: Materials Science — Materials Chemistry

Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task is impractical. Furthermore, the black-box nature of typical downstream tasks$\unicode{x2013}$such as property prediction$\unicode{x2013}$makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose a novel approach for a model uncertainty-guided fine-tuning of a pre-trained variational autoencoder (VAE)-based GMD model through performance feedback in an active learning setting. The main idea is to quantify model uncertainty in the generative model, which is made efficient by working within a low-dimensional active subspace of the high-dimensional VAE parameters explaining most of the variability in the model's output. The inclusion of model uncertainty expands the space of viable molecules through decoder diversity. We then explore the resulting model uncertainty class via black-box optimization made tractable by low-dimensionality of the active subspace. This enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties, using multiple VAE-based generative models, demonstrate that our uncertainty-guided fine-tuning approach consistently outperforms the original pre-trained models. Generative grammar ; Fine-tuning ; Computer science ; Artificial intelligence ; Generative model ; Pattern recognition (psychology) ; Machine learning ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ganeshraja Ayyakannu Sundaram | Muniyandi Govinda raj | Jayashree Ethiraj | P. Vairavel | A. Santhana Krishna Kumar
Source: Preprints.org (preprint, 2024-01-08)
Field: Materials Science — Materials Chemistry

Introduction and Advancements in Room Temperature Ferromagnetic Metal Oxide Semiconductors for Enhanced Photocatalytic Performance Recent developments in the field of room temperature ferromagnetic metal oxide semiconductors (RTFMOS) have shown promising potential for improved photocatalytic performance. This review focuses on the combined study of photocatalytic and ferromagnetic properties at room temperature, with a particular emphasis on metal oxides, such as TiO2, which have emerged as a key area of interest in the domains of magnetism and environmental remediation. Despite extensive research over the years, the precise mechanism behind the interplay of ferromagnetism and photocatalysis in these materials remains incompletely understood. Several critical factors contributing to magnetism have been hinted at, including oxygen vacancies and various metal doping. Numerous reports have demonstrated that these factors play a primary role in room temperature ferromagnetism and photocatalysis in wide-band-gap metal oxides. However, establishing a direct correlation between magnetism, oxygen vacancies, dopant concentration, and photocatalysis has proven challenging. This review aims to provide a comprehensive overview of the recent progress in understanding the magnetism and photocatalytic behaviour of metal oxides. By examining the latest findings, this study sheds light on the potential of RTFMOS as effective photocatalysts, thereby contributing to advancements in environmental remediation and related applications. Photocatalysis ; Magnetism ; Ferromagnetism ; Materials science ; Dopant ; Oxide ; Nanotechnology ; Doping ; Environmental remediation ; Magnetic semiconductor ; Condensed matter physics ; Chemistry ; Metallurgy ; Optoelectronics ; Catalysis ; Physics ; Ecology Copper-based nanomaterials and applications ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Marc Botifoll | Ivan Pinto‐Huguet | Enzo Rotunno | Thomas Galvani | Catalina Coll | Payam Habibzadeh Kavkani | María Chiara Spadaro | Yann‐Michel Niquet | Martin Eriksen | Sara Martí‐Sánchez | Georgios Katsaros | Giordano Scappucci | Peter Krogstrup | Giovanni Isella | Andreu Cabot | G. Merino | Pablo Ordejón | Stephan Roche | Vincenzo Grillo | Jordi Arbiol
Source: arXiv (Cornell University) (preprint, 2024-11-01)
Field: Materials Science — Surfaces, Coatings and Films

Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of devices due to its time-intensive nature. To address this, we introduce an analytical workflow for the holistic characterization, modelling, and simulation of device heterostructures. This workflow automates the experimental (S)TEM data analysis, providing an in-depth characterization of crystallographic information, 3D orientation, elemental composition, and strain distribution. It reduces a process that typically takes days for a trained human into an automatic routine solved in minutes. Utilizing a physics-guided artificial intelligence model, it generates representative descriptions of materials and samples. The workflow culminates in creating digital twins, 3D finite element and atomic models of millions of atoms, enabling simulations that provide crucial insights into device behaviour in practical applications. Demonstrated with SiGe planar heterostructures for scalable spin qubits, the workflow links digital twins to theoretical properties, revealing how atomic structure impacts materials and functional properties such as spatially-resolved phononic or electronic characteristics, or (inverse) spin orbit lengths. The versatility of our workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies. Workflow ; Automation ; End-to-end principle ; Computer science ; Transmission electron microscopy ; Data science ; Nanotechnology ; Engineering ; Artificial intelligence ; Materials science ; Database ; Mechanical engineering Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Kalyani Palanichamy | Banuprabha Rangachari | Jayavel Sridhar | Aravind Dhandapani | Varagunapandiyan Natarajan
Source: Chemical Industry and Chemical Engineering Quarterly (article, 2024-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in phytomass-derived activated carbon for applications in energy storage systems Phytomass i.e. plant biomass-derived active carbon is a versatile electrode material for energy devices owing to its natural and ubiquitous abundance, variety, ecocentrism, and unique physical properties. This article intricately reviews the recent advancements in phytomass-derived activated carbon (PAC), chiefly for the supercapacitor electrodes and notably, phytomass including different parts of the plants limited to, stem, leaf, flower, seed, fruit, and root for deriving PACs bestowed with excellent electrochemical performance have been considered. Advancement in the preparation of activated from phytomass, important facts associated with synthesis, and physical and electrochemical attributes have also been elaborated, which is expected to furnish a fruitful direction towards advocating supercapacitors ? the green energy packs. The surface of PAC is usually decorated with organic functional moieties containing heteroatoms like O, and/or S/N (referred to as self-doped heteroatoms). The synergy of these heteroatoms in enhancing the pseudocapacitance of the PAC electrodes in supercapacitors has also been featured. Further, the review provokes insights on strategies, prominent challenges, prospects, imminent opportunities, and hopeful trends in support of AC from various plant parts that may power our energy-based society, and scientific industries and in establishing a sustainable energy sector as well by harnessing nature?s potential. Activated carbon ; Energy storage ; Carbon fibers ; Environmental science ; Process engineering ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Physics ; Adsorption ; Physical chemistry ; Thermodynamics ; Composite material Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Mesoporous Materials and Catalysis
Authors: Leonardo Sabattini | Annalisa Coriolano | Corneel Casert | Stiven Forti | Edward S. Barnard | Fabio Beltram | Massimiliano Pontil | Stephen Whitelam | Camilla Coletti | Antonio Rossi
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Materials Chemistry

Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable production. While there have been significant advancements, much of the scientific progress has depended on the exfoliation of materials, a method that poses severe challenges for large-scale applications. With the advent of artificial intelligence (AI) in materials science, innovative synthesis methodologies are now on the horizon. This study explores the forefront of autonomous materials synthesis using an artificial neural network (ANN) trained by evolutionary methods, focusing on the efficient production of graphene. Our approach demonstrates that a neural network can iteratively and autonomously learn a time-dependent protocol for the efficient growth of graphene, without requiring pretraining on what constitutes an effective recipe. Evaluation criteria are based on the proximity of the Raman signature to that of monolayer graphene: higher scores are granted to outcomes whose spectrum more closely resembles that of an ideal continuous monolayer structure. This feedback mechanism allows for iterative refinement of the ANN's time-dependent synthesis protocols, progressively improving sample quality. Through the advancement and application of AI methodologies, this work makes a substantial contribution to the field of materials engineering, fostering a new era of innovation and efficiency in the synthesis process. Computer science ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Mohammad Maksudur Rahman
Source: Open Access Journal of Microbiology & Biotechnology (review, 2024-04-01)
Field: Materials Science — Biomaterials

A Review of Nanofiber Coating Technology for Future Food Packaging This brief review explores the burgeoning field of antimicrobial nanofiber coating technologies, poised to redefine the standards of food packaging for enhanced safety and longevity. With the global imperative for reducing food spoilage and extending shelf life, this paper highlights the revolutionary role of nanofibers infused with antimicrobial agents. These cuttingedge coatings promise not only to actively combat microbial growth but also to maintain the sensory and nutritional quality of food products. By integrating insights from recent studies and technological advancements, the review underscores the necessity for interdisciplinary collaboration among scientists, industry experts, and policymakers. The aim is to accelerate the development, scalability, and regulatory approval of these innovative materials, thereby setting a new benchmark for futureproofing food packaging against microbial contamination. Nanofiber ; Food packaging ; Coating ; Materials science ; Business ; Nanotechnology ; Engineering ; Mechanical engineering Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites ; Conducting polymers and applications