jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yufeng Luo | Mengke Li | Hongmei Yuan | Huijun Liu | Ying Fang
Source: npj Computational Materials (review, 2023-01-10)
Field: Materials Science — Materials Chemistry

Predicting lattice thermal conductivity via machine learning: a mini review Abstract Over the past few decades, molecular dynamics simulations and first-principles calculations have become two major approaches to predict the lattice thermal conductivity ( κ L ), which are however limited by insufficient accuracy and high computational cost, respectively. To overcome such inherent disadvantages, machine learning (ML) has been successfully used to accurately predict κ L in a high-throughput style. In this review, we give some introductions of recent ML works on the direct and indirect prediction of κ L , where the derivations and applications of data-driven models are discussed in details. A brief summary of current works and future perspectives are given in the end. Thermal conductivity ; Computer science ; Lattice (music) ; Artificial intelligence ; Machine learning ; Thermal ; Throughput ; Statistical physics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Sarah B. Ulaeto | Rajimol Puthenpurackal Ravi | Inime Ime Udoh | Gincy Marina Mathew | T.P.D. Rajan
Source: Corrosion and Materials Degradation (review, 2023-04-29)
Field: Materials Science — Materials Chemistry

Polymer-Based Coating for Steel Protection, Highlighting Metal–Organic Framework as Functional Actives: A Review Polymer-based coatings are a long-established category of protective coatings for metals and alloys regarding corrosion inhibition. The polymer films can degrade, and when coated on metallic substrates, the degradation facilitates moisture and oxygen penetration, reducing the polymer film’s adhesion to the metallic substrate and exposing the substrate to extreme conditions capable of corrosion. For this reason, pigments, inhibitors, and other compatible blends are added to the polymer coating formulations to enhance adhesion and protection. To prevent the possible deterioration of inhibitor-spiked polymer coatings, inhibitors are encapsulated through diverse techniques to avoid leakage and to provide a controlled release in response to the corrosion trigger. This review discusses polymer-based coating performance in corrosion-causing environments to protect metals, focusing more on commercial steels, a readily available construction-relevant material used in extensive applications. It further beams a searchlight on advances made on polymer-based coatings that employ metal–organic frameworks (MOFs) as functional additives. MOFs possess a tailorable structure of metal ions and organic linkers and have a large loading capacity, which is crucial for corrosion inhibitor delivery. Results from reviewed works show that polymer-based coatings provide barrier protection against the ingress of corrosive species and offer the chance to add several functions to coatings, further enhancing their anti-corrosion properties. Corrosion ; Materials science ; Coating ; Polymer ; Metal ; Polymer substrate ; Substrate (aquarium) ; Organic polymer ; Composite material ; Nanotechnology ; Metallurgy Corrosion Behavior and Inhibition ; Metal-Organic Frameworks: Synthesis and Applications ; Concrete Corrosion and Durability
Authors: Qiang Zhang | Zehao He | Zhenwei Xie | Qiaofeng Tan | Yunlong Sheng | Guofan Jin | Liangcai Cao | Xiaocong Yuan
Source: Photonics Insights (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diffractive optical elements 75 years on: from micro-optics to metasurfaces Diffractive optical elements (DOEs) are intricately designed devices with the purpose of manipulating light fields by precisely modifying their wavefronts. The concept of DOEs has its origins dating back to 1948 when D. Gabor first introduced holography. Subsequently, researchers introduced binary optical elements (BOEs), including computer-generated holograms (CGHs), as a distinct category within the realm of DOEs. This was the first revolution in optical devices. The next major breakthrough in light field manipulation occurred during the early 21st century, marked by the advent of metamaterials and metasurfaces. Metasurfaces are particularly appealing due to their ultra-thin, ultra-compact properties and their capacity to exert precise control over virtually every aspect of light fields, including amplitude, phase, polarization, wavelength/frequency, angular momentum, etc. The advancement of light field manipulation with micro/nano-structures has also enabled various applications in fields such as information acquisition, transmission, storage, processing, and display. In this review, we cover the fundamental science, cutting-edge technologies, and wide-ranging applications associated with micro/nano-scale optical devices for regulating light fields. We also delve into the prevailing challenges in the pursuit of developing viable technology for real-world applications. Furthermore, we offer insights into potential future research trends and directions within the realm of light field manipulation. Holography ; Wavefront ; Metamaterial ; Optics ; Nanophotonics ; Polarization (electrochemistry) ; Light field ; Physics ; Computer science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Optical Wireless Communication Technologies
Authors: Dierk Raabe | Jaber Rezaei Mianroodi | Jörg Neugebauer
Source: Nature Computational Science (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Accelerating the design of compositionally complex materials via physics-informed artificial intelligence Management science ; Nanotechnology ; Computer science ; Engineering ; Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Yifeng Liu | Zhiqiang Liu | Hui Yu | Liang Wang | Jian Zhang | Xianfeng Yi | Wei Chen | Chengtao Wang | Hai Wang | Yucai Qin | Lijuan Song | Anmin Zheng | Feng‐Shou Xiao
Source: Nature Communications (article, 2023-05-03)
Field: Chemistry — Organic Chemistry

Rhodium nanoparticles supported on silanol-rich zeolites beyond the homogeneous Wilkinson’s catalyst for hydroformylation of olefins Hydroformylation is one of the largest industrially homogeneous processes that strongly relies on catalysts with phosphine ligands such as the Wilkinson's catalyst (triphenylphosphine coordinated Rh). Heterogeneous catalysts for olefin hydroformylation are highly desired but suffer from poor activity compared with homogeneous catalysts. Herein, we demonstrate that rhodium nanoparticles supported on siliceous MFI zeolite with abundant silanol nests are very active for hydroformylation, giving a turnover frequency as high as ~50,000 h<sup>-1</sup> that even outperforms the classical Wilkinson's catalyst. Mechanism study reveals that the siliceous zeolite with silanol nests could efficiently enrich olefin molecules to adjacent rhodium nanoparticles, enhancing the hydroformylation reaction. Hydroformylation ; Silanol ; Rhodium ; Catalysis ; Olefin fiber ; Phosphine ; Zeolite ; Nanoparticle ; Chemistry ; Organic chemistry ; Materials science ; Nanotechnology Organometallic Complex Synthesis and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Yunfei Shi | Zhengxin Yang | Sicong Ma | Pei‐Lin Kang | Cheng Shang | P. Hu | Zhi‐Pan Liu
Source: Engineering (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Machine Learning for Chemistry: Basics and Applications The past decade has seen a sharp increase in machine learning (ML) applications in scientific research. This review introduces the basic constituents of ML, including databases, features, and algorithms, and highlights a few important achievements in chemistry that have been aided by ML techniques. The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations. Important two-dimensional (2D) and three-dimensional (3D) features representing the chemical environment of molecules and solids are briefly introduced. Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios. Three important fields of ML in chemistry are discussed: ① retrosynthesis, in which ML predicts the likely routes of organic synthesis; ② atomic simulations, which utilize the ML potential to accelerate potential energy surface sampling; and ③ heterogeneous catalysis, in which ML assists in various aspects of catalytic design, ranging from synthetic condition optimization to reaction mechanism exploration. Finally, a prospect on future ML applications is provided. Retrosynthetic analysis ; Computer science ; Decision tree ; Artificial neural network ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Materials science ; Organic chemistry ; Total synthesis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Raimondas Galvelis | Alejandro Varela‐Rial | Stefan Doerr | Roberto Fino | Peter Eastman | Thomas E. Markland | John D. Chodera | Gianni De Fabritiis
Source: Journal of Chemical Information and Modeling (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 μs for each complex, marking the longest simulations ever reported for this class of simulations. Metadynamics ; Molecular dynamics ; Molecular mechanics ; Computer science ; Statistical physics ; Computational science ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ziyu Chen | Yutao Ye | Xiaoyi Feng | Yan Wang | Xiaowei Han | Yu Zhu | Shiqun Wu | Senyao Wang | Wenda Yang | Lingzhi Wang | Jinlong Zhang
Source: Nature Communications (article, 2023-04-10)
Field: Chemistry — Inorganic Chemistry

High-density frustrated Lewis pairs based on Lamellar Nb2O5 for photocatalytic non-oxidative methane coupling Photocatalytic methane conversion requires a strong polarization environment composed of abundant activation sites with the robust stretching ability for C-H scissoring. High-density frustrated Lewis pairs consisting of low-valence Lewis acid Nb and Lewis base Nb-OH are fabricated on lamellar Nb<sub>2</sub>O<sub>5</sub> through a thermal-reduction promoted phase-transition process. Benefitting from the planar atomic arrangement of lamellar Nb<sub>2</sub>O<sub>5</sub>, the frustrated Lewis pairs sites are highly exposed and accessible to reactants, which results in a superior methane conversion rate of 1456 μmol g<sup>-1</sup> h<sup>-1</sup> for photocatalytic non-oxidative methane coupling without the assistance of noble metals. The time-dependent DFT calculation demonstrates the photo-induced electron transfer from LA to LB sites enhances their intensities in a concerted way, promoting the C-H cleavage through the coupling of LA and LB. This work provides in-depth insight into designing and constructing a polarization micro-environment for photocatalytic C-H activation of methane without the assistance of noble metals. Oxidative coupling of methane ; Methane ; Photocatalysis ; Coupling (piping) ; Lamellar structure ; Frustrated Lewis pair ; Lewis acids and bases ; Materials science ; Oxidative phosphorylation ; Chemistry ; Crystallography ; Organic chemistry ; Catalysis ; Biochemistry ; Composite material Inorganic Fluorides and Related Compounds ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Ahmed A. Al‐Amiery | Emad͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏ Yousif | Wan Nor Roslam Wan Isahak | Waleed Khalid Al‐Azzawi
Source: Tribology in Industry (review, 2023-06-15)
Field: Materials Science — Materials Chemistry

A Review of Inorganic Corrosion Inhibitors: Types, Mechanisms, and Applications This review paper provides an overview of inorganic corrosion inhibitors, including their types, mechanisms of action, applications, recent advances, and future directions. Inorganic corrosion inhibitors have been widely used to protect metals and alloys from corrosion in various industries, such as oil and gas, chemical, and construction industries. The different types of inorganic corrosion inhibitors discussed in this review include metal-based, metal oxide-based, phosphate-based, silicate-based, and other inorganic inhibitors. The mechanisms of action of inorganic corrosion inhibitors are mainly related to their adsorption on metal surfaces, formation of protective films, and cathodic and anodic polarization. The paper also highlights the applications of inorganic corrosion inhibitors in different industries and discusses their effectiveness and limitations. Recent advances in the field of inorganic corrosion inhibitors, such as nanotechnology-based inhibitors, green inhibitors, combination inhibitors, and computational studies, are also reviewed. In conclusion, this paper summarizes the key findings of the review and provides a future outlook for the development of inorganic corrosion inhibitors. The review concludes that further research is needed to develop more effective, environmentally friendly, and economical inorganic corrosion inhibitors for various industrial applications. Corrosion ; Computer science ; Nanotechnology ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Synthesis and properties of polymers ; Inorganic and Organometallic Chemistry
Authors: Santosh K. Tiwari | Sarvesh Kumar Pandey | Raunak Pandey | Nannan Wang | M. Bystrzejewski | Yogendra Kumar Mishra | Yanqiu Zhu
Source: Small (review, 2023-06-29)
Field: Materials Science — Materials Chemistry

Stone–Wales Defect in Graphene 2D graphene the most investigated structures from nanocarbon family studied in the last three decades. It is projected as an excellent material useful for quantum computing, artificial intelligence, and next generation advanced technologies. Graphene exists in several forms and its extraordinary thermal, mechanical, and electronic properties, principally depend on the kind of perfection of the hexagonal atomic lattice. Defects are always considered as undesired components but certain defects in graphene could be an asset for electrochemistry and quantum electronics due to the engineered electronclouds and quantum tunnelling. The authors carefully discuss the Stone-Wales imperfections in graphene and its derivatives comprehensively. A specific emphasis is focused on the experimental and theoretical aspects of the Stone-Wales defects in graphene with respect to structure-property relationships. The corroboration of extrinsic defects like external atomic doping, functionalization, edge distortion in the graphene consisting of Stone-Wales imperfections, which are very significant in designing graphene-based electronic devices, are summarized. Graphene ; Materials science ; Nanotechnology Graphene research and applications ; Ion-surface interactions and analysis ; Diamond and Carbon-based Materials Research
Authors: Navaj B. Mullani | Dhananjay D. Kumbhar | Do‐Hyeon Lee | Mi Ji Kwon | Su‐yeon Cho | Nuri Oh | Eui‐Tae Kim | Tukaram D. Dongale | Sang Yong Nam | Jun Hong Park
Source: Advanced Functional Materials (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Surface Modification of a Titanium Carbide MXene Memristor to Enhance Memory Window and Low‐Power Operation Abstract With the demand for low‐power‐operating artificial intelligence systems, bio‐inspired memristor devices exhibit potential in terms of high‐density memory functions and the emulation of the synaptic dynamics of the human brain. The 2D material MXene attracts considerable interest for use in resistive‐switching memory and artificial synapse devices owing to its excellent physicochemical properties in memristor devices. However, few memristive and synaptic MXene devices that display increased switching performances are reported, with no significant results. Herein, the conductivity of MXene (Ti 3 C 2 T x ) is engineered via etching and oxidation to enhance the switching performance of the device. The exceptional properties of partially oxidized MXene memristors include large memory windows and low threshold biases, and the complex spike‐timing‐dependent plasticity synaptic rules are also emulated. The low threshold potential distribution, reliable retention time (10 4 s), and distinct resistance states with a high ON–OFF ratio (&gt;10 4 ) are the main memory‐related features of this device. The experimentally determined switching potentials of the optimized device are also uniformly distributed, according to a statistical probability‐based approach. This investigation may promote the essential material properties for use in high‐density non‐volatile memory storage and artificial synapse systems in the field of innovative nanoelectronic devices. Memristor ; Materials science ; Neuromorphic engineering ; Resistive random-access memory ; Emulation ; Nanotechnology ; Synapse ; Optoelectronics ; Non-volatile memory ; Computer science ; Electronic engineering ; Voltage ; Artificial neural network ; Electrical engineering ; Artificial intelligence ; Neuroscience MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Teng Zhang | Christopher E. Shuck | Kateryna Shevchuk | Mark Anayee | Yury Gogotsi
Source: Journal of the American Chemical Society (article, 2023-10-03)
Field: Materials Science — Materials Chemistry

Synthesis of Three Families of Titanium Carbonitride MXenes Layered MAX phases and two-dimensional (2D) MXenes derived from them are among the most studied materials due to their attractive properties and numerous potential applications. The tunability of their structure and composition allows for every property to be modulated over a wide range. Particularly, elemental replacement and formation of a solid solution without changing the structure allow fine-tuning of material properties. While solid solutions on the M (metal) site have received attention, the partial replacement of carbon with nitrogen (carbonitrides) has received little attention. By applying this concept, herein we report the synthesis of three families of titanium carbonitride Ti<sub><i>n</i>+1</sub>Al(C<sub>1-<i>y</i></sub>N<sub><i>y</i></sub>)<sub><i>n</i></sub> MAX phases and Ti<sub><i>n</i>+1</sub>(C<sub>1-<i>y</i></sub>N<sub><i>y</i></sub>)<sub><i>n</i></sub>T<sub><i>x</i></sub> MXenes with one, two, and three C/N layers. This greatly expands the variety of known MAX phases and MXenes to encompass 16 titanium carbonitrides with tunable X-site chemistries and different 2D layer thicknesses, including MXenes in the Ti<sub>4</sub>(C<sub>1-<i>y</i></sub>N<sub><i>y</i></sub>)<sub>3</sub>T<sub><i>x</i></sub> system, which have not been previously reported. We further investigated the relationship among the composition, structure, stability, and synthesis conditions of the MXenes and their respective Al-based MAX phases. This range of materials will enable fundamental studies of the N/C ratio effect on optoelectronic, electromagnetic, and mechanical properties of MXenes, as well as tuning those properties for specific applications. MXenes ; Tin ; Titanium ; Chemistry ; MAX phases ; Titanium nitride ; Nitride ; Chemical engineering ; Nanotechnology ; Layer (electronics) ; Carbide ; Materials science ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene and Nanomaterials Applications
Authors: Krishna Chattopadhyay | Manas Mandal | Dilip K. Maiti
Source: Materials Advances (review, 2023-11-21)
Field: Chemistry — Inorganic Chemistry

A review on zirconium-based metal–organic frameworks: synthetic approaches and biomedical applications Zr-MOFs, known for stability, low cytotoxicity and high drug-loading capacity; have been thoroughly investigated for biomedical uses. This article reviews the various synthetic methods and recent biomedical applications of important Zr-based MOFs. Zirconium ; Metal-organic framework ; Nanotechnology ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Dendrimers and Hyperbranched Polymers
Authors: Rishi Gurnani | Christopher Kuenneth | Aubrey Toland | Rampi Ramprasad
Source: Chemistry of Materials (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Polymer Informatics at Scale with Multitask Graph Neural Networks Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units-a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly "machine learning" important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand. Our approach-based on graph neural networks, multitask learning, and other advanced deep learning techniques-speeds up feature extraction by 1-2 orders of magnitude relative to presently adopted handcrafted methods without compromising model accuracy for a variety of polymer property prediction tasks. We anticipate that our approach, which unlocks the screening of truly massive polymer libraries at scale, will enable more sophisticated and large scale screening technologies in the field of polymer informatics. Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Artificial neural network ; Polymer ; Task (project management) ; Variety (cybernetics) ; Scale (ratio) ; Informatics ; Graph ; Materials informatics ; Field (mathematics) ; Data science ; Theoretical computer science ; Health informatics ; Materials science ; Systems engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies