jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Ruiz‐Garcia | Vicente Esquivel‐Peña | Francisco A. Godínez | R. Montoya
Source: ChemElectroChem (review, 2024-02-28)
Field: Materials Science — Materials Chemistry

Corrosion Modeling of Aluminum Alloys: A Brief Review Abstract Corrosion remains a critical concern in various industries, particularly in applications involving aluminum alloys due to their widespread usage as light structural materials. This work provides a comprehensive overview of the recent advances in modeling techniques for better understanding the corrosion behavior of aluminum alloys. The present study encompasses the state‐of‐the‐art of computational approaches, highlighting their strengths and limitations. Special attention is given to transport models, galvanic couples, localized corrosion, and the incorporation of different factors, such as pH variations, electrolyte thicknesses and dynamic electrolytes into corrosion models. Furthermore, the review delves into both bulk and atmospheric corrosion models. On the other hand, this work also emphasizes a clear differentiation between models designed for bare aluminum alloys and those tailored for coated ones. Additionally, it addresses certain challenges associated with the experimental validation of such models. This review concludes with perspectives on both sophisticated transport models to bridge the gap between industrial and academic investigations, as well as the integration of machine learning and artificial intelligence techniques in corrosion modeling, offering promising directions for future research in this field. Corrosion ; Galvanic cell ; Work (physics) ; Materials science ; Galvanic corrosion ; Bridge (graph theory) ; Aluminium ; Computer science ; Nanotechnology ; Mechanical engineering ; Systems engineering ; Engineering ; Metallurgy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yang Jeong Park | Sungroh Yoon | Sung Eun Jerng
Source: Journal of CO2 Utilization (article, 2024-10-21)
Field: Chemistry — Inorganic Chemistry

Machine learning of metal-organic framework design for carbon dioxide capture and utilization Metal-organic frameworks (MOFs) are attractive materials with easily tunable porous structures. Their selective carbon dioxide (CO 2 ) capture ability can be varied by altering the functionality of the organic ligands. However, rule-based approaches to tuning and developing MOFs with high CO 2 capture and conversion abilities are hindered by the numerous possible combinations of metal ions and organic linkers. Recently, machine learning (ML) has been applied to unravel key descriptors in predicting the performance of MOFs. This review summarizes recent advancements in ML models for MOFs in CO 2 capture and utilization, including high-throughput screening, neural network interatomic potential, and generative models. The development of sophisticated ML models for designing high-performance MOFs will play a critical role in addressing climate change in the future. Finally, the main challenges and limitations of current approaches in designing high-performance MOFs are discussed. • This review addresses the machine learning applications for the discovery of metal-organic frameworks for CO 2 capture. • Available data sources for machine learning applications based on both simulations and experiments are introduced. • Applications of machine learning, deep learning, generative modeling, and machine learning force fields are investigated. • Challenges and limitations of the current machine learning paradigm are discussed. Carbon dioxide ; Metal-organic framework ; Environmental science ; Process engineering ; Computer science ; Chemical engineering ; Artificial intelligence ; Chemistry ; Engineering ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Chaochao Dun | Xinyi Wang | Linfeng Chen | Sichi Li | Hanna Breunig | Jeffrey J. Urban
Source: Nano Research (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H2) storage likewise holds strong potential, though with distinct challenges and mechanisms. H2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H2 storage systems by optimizing H2 adsorption at the nanoscale and improving the kinetics of H2 uptake and release. We discuss various H2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H2 storage, particularly in prototype vehicles, from a techno-economic perspective. This forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand. Hydrogen storage ; Energy storage ; Physisorption ; Materials science ; Nanotechnology ; Scalability ; Process engineering ; MXenes ; Computer science ; Adsorption ; Chemistry ; Engineering ; Alloy ; Thermodynamics Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials
Authors: Taoyong Cui | Chenyu Tang | Mao Su | Shufei Zhang | Yuqiang Li | Lei Bai | Yuhan Dong | Xin-Gao Gong | Wanli Ouyang
Source: Nature Machine Intelligence (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Geometry-enhanced pretraining on interatomic potentials Materials science ; Geometry ; Physics ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Force Microscopy Techniques and Applications
Authors: Muhammad Ahmad Mudassir | Zainab Tazeen | Shazia Kousar | Muhan Li | Xiaoyu Zhao
Source: International Journal of Hydrogen Energy (review, 2024-12-14)
Field: Chemistry — Inorganic Chemistry

Metal‒organic framework/graphene-based nanocatalysts for hydrogen evolution reaction: A comprehensive review Nanomaterial-based catalyst ; Graphene ; Metal-organic framework ; Materials science ; Hydrogen ; Metal ; Nanotechnology ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: J. Roshni | T. Karthick
Source: Journal of Molecular Structure (review, 2024-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Comprehensive Review on Theoretical Screening Methods for Pharmaceutical Cocrystals Cocrystal ; Chemistry ; Virtual screening ; Biochemical engineering ; Cheminformatics ; Computational chemistry ; Molecular dynamics ; Organic chemistry ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Zihao Ye | Bo Shen | Dohun Kang | Jiahong Shen | Jin Huang | Zhe Wang | Liliang Huang | Christopher Wolverton | Chad A. Mirkin
Source: Nature Synthesis (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

A data-driven approach for the guided regulation of exposed facets in nanoparticles Nanoparticle ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Chang‐Ho Hong | Sangmin Oh | Hyungmin An | Purun-hanul Kim | Yaeji Kim | Jae-hyeon Ko | J.A. Sue | Dongyean Oh | Sung-Kye Park | Seungwu Han
Source: ACS Applied Materials & Interfaces (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

Atomistic Simulation of HF Etching Process of Amorphous Si<sub>3</sub>N<sub>4</sub> Using Machine Learning Potential An atomistic understanding of dry-etching processes with reactive molecules is crucial for achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics (MD) simulations are instrumental, but the lack of reliable force fields hinders the widespread use of MD in etching simulations. In this work, we develop an accurate neural network potential (NNP) for simulating the etching process of amorphous Si<sub>3</sub>N<sub>4</sub> with HF molecules. The surface reactions in diverse local environments are considered by incorporating several types of training sets: baseline structures, reaction-specific data, and general-purpose training sets. Furthermore, the NNP is refined through iterative comparisons with the density functional theory results. Using the trained NNP, we carry out etching simulations, which allow for detailed observation and analysis of key processes such as preferential sputtering, surface modification, etching yield, threshold energy, and the distribution of etching products. Additionally, we develop a simple continuum model, built from the MD simulation results, which effectively reproduces the surface composition obtained with MD simulations. By establishing a computational framework for atomistic etching simulation and scale bridging, this work will pave the way for more accurate and efficient design of etching processes in the semiconductor industry, enhancing device performance and manufacturing precision. Materials science ; Amorphous solid ; Etching (microfabrication) ; Process (computing) ; Nanotechnology ; Crystallography ; Computer science Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Metal and Thin Film Mechanics
Authors: Ayush Jain | Connor D. Armstrong | V. Roshan Joseph | Rampi Ramprasad | H. Jerry Qi
Source: ACS Applied Materials & Interfaces (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Machine-Guided Discovery of Acrylate Photopolymer Compositions Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space. Materials science ; Monomer ; Polymer ; Elastomer ; Modulus ; Copolymer ; Acrylate ; Thermoplastic elastomer ; Photopolymer ; Composite material ; Thermosetting polymer ; Computer science Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Injection Molding Process and Properties
Authors: Ruoqun Zhang | Hongzhe He | Yuneng Tang | Zhicheng Zhang | Hailiang Zhou | Jianglong Yu | Lian Zhang | Baiqian Dai
Source: ChemCatChem (review, 2024-05-12)
Field: Materials Science — Materials Chemistry

A Review on Fe<sub>2</sub>O<sub>3</sub>‐Based Catalysts for Toluene Oxidation: Catalysts Design and Optimization with the Formation of Abundant Oxygen Vacancies Abstract Volatile organic compounds (VOCs) are typical pollutants with hazards for humans and the environment, which can be efficiently mitigated by catalytic combustion. Fe 2 O 3 ‐based catalysts are a promising choice due to their low cost and strong redox ability. Several attempts have been made to promote the catalytic performance for Fe 2 O 3 ‐based catalysts at low temperatures. This review summarizes the research progress on Fe 2 O 3 ‐based catalysts for the oxidation of toluene, one of the most common and harmful VOC. Firstly, the structural properties and catalytic performances for Fe 2 O 3 ‐based catalysts have been summarized, and the reaction mechanisms for toluene oxidation on the surface of Fe 2 O 3 ‐based catalysts were detailed to comprehend the role of oxygen vacancies. Then, the modification for single Fe 2 O 3 catalysts, including synthesis parameters, structure and morphology control, is introduced to reveal the correlation between physicochemical properties of catalysts and their activity for toluene oxidation. In addition, composite Fe 2 O 3 catalysts, which can promote catalytic performance significantly by the synergetic effect between different components, were presented in detail. Moreover, waste‐derived Fe 2 O 3 catalysts with sustainable merit as converting waste into worth have been discussed. Finally, the advanced machine learning tools, which are helpful in accelerating catalyst design, configuration optimization and reactivity prediction, have been introduced as an emerging research opportunity for the future. Catalysis ; Toluene ; Oxygen ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Marvin Friede | Christian Hölzer | Sebastian Ehlert | Stefan Grimme
Source: The Journal of Chemical Physics (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

<i>dxtb</i>—An efficient and fully differentiable framework for extended tight-binding Automatic differentiation (AD) emerged as an integral part of machine learning, accelerating model development by enabling gradient-based optimization without explicit analytical derivatives. Recently, the benefits of AD and computing arbitrary-order derivatives with respect to any variable were also recognized in the field of quantum chemistry. In this work, we present dxtb-an open-source, fully differentiable framework for semiempirical extended tight-binding (xTB) methods. Developed entirely in Python and leveraging PyTorch for array operations, dxtb facilitates extensibility and rapid prototyping while maintaining computational efficiency. Through comprehensive code vectorization and optimization, we essentially reach the speed of compiled xTB programs for high-throughput calculations of small molecules. The excellent performance also scales to large systems, and batch operability yields additional benefits for execution on parallel hardware. In particular, energy evaluations are on par with existing programs, whereas the speed of automatically differentiated nuclear derivatives is only 2 to 5 times slower compared to their analytical counterparts. We showcase the utility of AD in dxtb by calculating various molecular and spectroscopic properties, highlighting its capacity to enhance and simplify such evaluations. Furthermore, the framework streamlines optimization tasks and offers seamless integration of semiempirical quantum chemistry in machine learning, paving the way for physics-inspired end-to-end differentiable models. Ultimately, dxtb aims to further advance the capabilities of semiempirical methods, providing an extensible foundation for future developments and hybrid machine learning applications. The framework is accessible at https://github.com/grimme-lab/dxtb. Automatic differentiation ; Computer science ; Differentiable function ; Python (programming language) ; Computational science ; Extensibility ; Codebase ; Source code ; Programming language ; Computer engineering ; Theoretical computer science ; Computation ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Heng Zhao | Tim Gould | Stefan Vuckovic
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Deep Mind 21 functional does not extrapolate to transition metal chemistry The development of density functional approximations stands at a crossroads: while machine-learned functionals show potential to surpass their human-designed counterparts, their extrapolation to unseen chemistry lags behind. Here we assess how well the recent Deep Mind 21 (DM21) machine-learned functional [<i>Science</i>, 2021, <b>374</b>, 1385-1389], trained on main-group chemistry, extrapolates to transition metal chemistry (TMC). We show that DM21 demonstrates comparable or occasionally superior accuracy to B3LYP for TMC, but consistently struggles with achieving self-consistent field convergence for TMC molecules. We also compare main-group and TMC machine-learning DM21 features to shed light on DM21's challenges in TMC. We finally propose strategies to overcome limitations in the extrapolative capabilities of machine-learned functionals in TMC. Extrapolation ; Density functional theory ; Chemistry ; Artificial intelligence ; Computational chemistry ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Setyo Budi | Muhamad Akrom | Harun Al Azies | Usman Sudibyo | T. Sutojo | Gustina Alfa Trisnapradika | Aprilyani Nur Safitri | Ayu Pertiwi | Supriadi Rustad
Source: KnE Engineering (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors Historically, the exploration of corrosion inhibitor technology has relied extensively on experimental methodologies, which are inherently associated with substantial costs, prolonged durations, and significant resource utilization. However, the emergence of ML approaches has recently garnered attention as a promising avenue for investigating potential materials with corrosion inhibition properties. This study endeavors to enhance the predictive capacity of ML models by leveraging polynomial functions. Specifically, the investigation focuses on assessing the effectiveness of pyridinequinoline compounds in mitigating corrosion. Diverse ML models were systematically evaluated, integrating polynomial functions to augment their predictive capabilities. The integration of polynomial functions notably amplifies the predictive accuracy across all tested models. Notably, the SVR model emerges as the most adept, exhibiting R² of 0.936 and RMSE of 0.093. The outcomes of this inquiry underscore a significant enhancement in predictive accuracy facilitated by the incorporation of polynomial functions within ML models. The proposed SVR model stands out as a robust tool for prognosticating the corrosion inhibition potential of pyridine-quinoline compounds. This pioneering approach contributes invaluable insights into advancing machine learning methodologies geared toward designing and engineering materials with promising corrosion inhibition properties. Keywords: machine learning, polynomial, corrosion inhibition, pyridine-quinoline Quinoline ; Corrosion ; Pyridine ; Corrosion inhibitor ; Materials science ; Computer science ; Biological system ; Combinatorial chemistry ; Chemistry ; Metallurgy ; Organic chemistry Material Properties and Failure Mechanisms
Authors: Sebastian Lukas | Ardeshir Esteki | Nico Rademacher | Vikas Jangra | Michael Groß | Zhenxing Wang | Ha-Duong Ngo | Manuel Bäuscher | Piotr Mackowiak | K. Höppner | Dominique Wehenkel | Richard M. van Rijn | Max C. Lemme
Source: ACS Nano (article, 2024-09-08)
Field: Materials Science — Materials Chemistry

High-Yield Large-Scale Suspended Graphene Membranes over Closed Cavities for Sensor Applications Suspended membranes of monatomic graphene exhibit great potential for applications in electronic and nanoelectromechanical devices. In this work, a "hot and dry" transfer process is demonstrated to address the fabrication and patterning challenges of large-area graphene membranes on top of closed, sealed cavities. Here, "hot" refers to the use of high temperature during transfer, promoting the adhesion. Additionally, "dry" refers to the absence of liquids when graphene and target substrate are brought into contact. The method leads to higher yields of intact suspended monolayer chemical vapor deposition (CVD) graphene and artificially stacked double-layer CVD graphene membranes than previously reported. The yield evaluation is performed using neural-network-based object detection in scanning electron microscopy (SEM) images, ascertaining high yields of intact membranes with large statistical accuracy. The suspended membranes are examined by Raman tomography and atomic force microscopy (AFM). The method is verified by applying the suspended graphene devices as piezoresistive pressure sensors. Our technology advances the application of suspended graphene membranes and can be extended to other two-dimensional materials. Graphene ; Membrane ; Materials science ; Nanotechnology ; Graphene oxide paper ; Chemical vapor deposition ; Fabrication ; Graphene foam ; Graphene nanoribbons ; Substrate (aquarium) ; Raman spectroscopy ; Scanning electron microscope ; Chemical engineering ; Composite material ; Chemistry ; Optics Graphene research and applications ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors