jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yue Li | Yutong Wang | Weidong Fan | Daofeng Sun
Source: Dalton Transactions (review, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Flexible metal–organic frameworks for gas storage and separation Flexible metal-organic frameworks (MOFs) have gradually attracted much attention due to their reversible structural changes and flexible structural responses. The basic research of flexible MOFs is to study their dynamic responses under different external stimuli and translate the responses into applications. Most research studies on flexible MOFs focus on gas storage and separation, but lack a systematic summary. Here, we review the development of flexible MOFs, the structural transformation under the external effects of temperature, pressure, and guest molecules, and their applications in gas storage and separation. Microporous MOFs with flexible structures provide unique opportunities for fine-tuning their performance because the pore shape and size can be controlled by external stimuli. The characteristics of breathing phenomena and large specific surface area make flexible MOFs suitable candidates for gas storage and separation. Finally, the application prospects of flexible MOFs are reported. Metal-organic framework ; Separation (statistics) ; Gas separation ; Metal ; Process engineering ; Materials science ; Chemistry ; Environmental science ; Nanotechnology ; Computer science ; Engineering ; Metallurgy ; Organic chemistry ; Adsorption ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Andrew Rosen | Victor Fung | Patrick Huck | Cody T. O’Donnell | Matthew K. Horton | Donald G. Truhlar | Kristin A. Persson | Justin M. Notestein | Randall Q. Snurr
Source: npj Computational Materials (article, 2022-05-17)
Field: Chemistry — Inorganic Chemistry

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3 d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project. Hybrid functional ; Density functional theory ; Computer science ; Artificial neural network ; Band gap ; Local-density approximation ; Electronic structure ; Materials science ; Physics ; Machine learning ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Dmitry B. Eremin | Alexey S. Galushko | Daniil A. Boiko | Evgeniy O. Pentsak | Igor V. Chistyakov | Valentine P. Ananikov
Source: Journal of the American Chemical Society (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particles Homogeneous catalysis is typically considered "well-defined" from the standpoint of catalyst structure unambiguity. In contrast, heterogeneous nanocatalysis often falls into the realm of "poorly defined" systems. Supported catalysts are difficult to characterize due to their heterogeneity, variety of morphologies, and large size at the nanoscale. Furthermore, an assortment of active metal nanoparticles examined on the support are negligible compared to those in the bulk catalyst used. To solve these challenges, we studied individual particles of the supported catalyst. We made a significant step forward to fully characterize individual catalyst particles. Combining a nanomanipulation technique inside a field-emission scanning electron microscope with neural network analysis of selected individual particles unexpectedly revealed important aspects of activity for widespread and commercially important Pd/C catalysts. The proposed approach unleashed an unprecedented turnover number of 10<sup>9</sup> attributed to individual palladium on a nanoglobular carbon particle. Offered in the present study is the Totally Defined Catalysis concept that has tremendous potential for the mechanistic research and development of high-performance catalysts. Catalysis ; Chemistry ; Nanotechnology ; Nanoscopic scale ; Homogeneous ; Nanoparticle ; Palladium ; Particle (ecology) ; Heterogeneous catalysis ; Chemical engineering ; Chemical physics ; Materials science ; Physics ; Statistical physics ; Organic chemistry Machine Learning in Materials Science ; Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science
Authors: Nikita Serov | Vladimir V. Vinogradov
Source: Advanced Drug Delivery Reviews (review, 2022-03-10)
Field: Materials Science — Materials Chemistry

Artificial intelligence to bring nanomedicine to life Nanomedicine ; Computer science ; Throughput ; Rational design ; Nanotechnology ; Artificial intelligence ; Risk analysis (engineering) ; Data science ; Medicine Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Zhuo Wang | Zhehao Sun | Hang Yin | Xinghui Liu | Jinlan Wang | Haitao Zhao | Cheng Heng Pang | Tao Wu | Shuzhou Li | Zongyou Yin | Xue‐Feng Yu
Source: Advanced Materials (review, 2022-04-22)
Field: Materials Science — Materials Chemistry

Data‐Driven Materials Innovation and Applications Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted. Field (mathematics) ; Nanotechnology ; Materials informatics ; Data-driven ; Computer science ; Data science ; Electronics ; Data management ; Materials science ; Systems engineering ; Artificial intelligence ; Data mining ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Xiang Fu | Zhenghao Wu | Wujie Wang | Tian Xie | Sinan Keten | Rafael Gómez‐Bombarelli | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2022-10-13)
Field: Materials Science — Materials Chemistry

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab-initio simulations by predicting forces directly from atomic structures. Despite significant progress in this area, such techniques are primarily benchmarked by their force/energy prediction errors, even though the practical use case would be to produce realistic MD trajectories. We aim to fill this gap by introducing a novel benchmark suite for learned MD simulation. We curate representative MD systems, including water, organic molecules, a peptide, and materials, and design evaluation metrics corresponding to the scientific objectives of respective systems. We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics. We demonstrate when and how selected SOTA methods fail, along with offering directions for further improvement. Specifically, we identify stability as a key metric for ML models to improve. Our benchmark suite comes with a comprehensive open-source codebase for training and simulation with ML FFs to facilitate future work. Codebase ; Benchmark (surveying) ; Suite ; Computer science ; Metric (unit) ; Force field (fiction) ; Field (mathematics) ; Stability (learning theory) ; Machine learning ; Key (lock) ; Molecular dynamics ; Artificial intelligence ; Software ; Engineering ; Physics ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Jiyoung Lee | Hongwei Liao | Qiyue Wang | Jieun Han | Jun‐Hyeok Han | Ha Eun Shin | Minghua Ge | Wooram Park | Fangyuan Li
Source: Exploration (review, 2022-01-25)
Field: Materials Science — Materials Chemistry

Exploration of nanozymes in viral diagnosis and therapy Nanozymes are nanomaterials with similar catalytic activities to natural enzymes. Compared with natural enzymes, they have numerous advantages, including higher physiochemical stability, versatility, and suitability for mass production. In the past decade, the synthesis of nanozymes and their catalytic mechanisms have advanced beyond the simple replacement of natural enzymes, allowing for fascinating applications in various fields such as biosensing and disease treatment. In particular, the exploration of nanozymes as powerful toolkits in diagnostic viral testing and antiviral therapy has attracted growing attention. It can address the great challenges faced by current natural enzyme-based viral testing technologies, such as high cost and storage difficulties. Therefore, nanozyme can provide a novel nanozyme-based antiviral therapeutic regime with broader availability and generalizability that are keys to fighting a pandemic such as COVID-19. Herein, we provide a timely review of the state-of-the-art nanozymes regarding their catalytic activities, as well as a focused discussion on recent research into the use of nanozymes in viral testing and therapy. The remaining challenges and future perspectives will also be outlined. Ultimately, this review will inform readers of the current knowledge of nanozymes and inspire more innovative studies to push forward the frontier of this field. Coronavirus disease 2019 (COVID-19) ; Computer science ; Nanotechnology ; Risk analysis (engineering) ; Biochemical engineering ; Pandemic ; Disease ; Engineering ; Medicine ; Materials science ; Infectious disease (medical specialty) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Tao Wu | Paul R. C. Kent | Yury Gogotsi | De‐en Jiang
Source: Chemistry of Materials (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

How Water Attacks MXene Two-dimensional (2D) transition metal carbides and nitrides (MXenes) have shown outstanding performances in electrochemical energy storage and many other applications. However, the stability of MXene remains a concern, especially its quick degradation in aqueous solutions under ambient conditions. Here, we report on the water/Ti3C2O2-MXene interfacial chemistry from first-principles molecular dynamics simulations at room temperature. Surprisingly, we find that the water molecules can attack the basal plane of Ti3C2O2 and pull the surface Ti atoms out, thereby reconstructing the surface. By tracking close encounters of water molecules and surface Ti atoms on the basal plane of Ti3C2O2, we show that the attack is initiated by the chemisorption of a water molecule on a surface Ti atom, followed by the breaking of Ti–C bonds and deprotonation of the water molecule, leading to the formation of Ti–OH on the Ti3C2O2 surface and a hydronium ion in the aqueous phase. Our finding highlights the susceptibility of Ti3C2O2 MXene to water attack, supporting recent experimental observations. Furthermore, we demonstrate that preventing close encounters of water molecules and the surface Ti atoms is key to the stability of the basal plane and can be realized by negatively charging the surface (thereby reorienting the O atoms of water away from the surface) or converting the surface O to −OH groups (thereby shifting the water layer further away from the surface). Our insights and approach highlight the importance of the reactivity of water when interfacing with 2D materials such as MXenes. MXenes ; Chemical physics ; Molecule ; Materials science ; Aqueous solution ; Hydronium ; Deprotonation ; Chemisorption ; Surface modification ; Transition metal ; Chemistry ; Nanotechnology ; Ion ; Adsorption ; Physical chemistry ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Christopher J. Bartel
Source: Journal of Materials Science (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Review of computational approaches to predict the thermodynamic stability of inorganic solids Stability (learning theory) ; Context (archaeology) ; Chemical stability ; Decomposition ; Solid mechanics ; Computer science ; Supercomputer ; Thermodynamics ; Materials science ; Statistical physics ; Chemistry ; Physics ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: José S. Câmara | Rosa Perestrelo | Cristina V. Berenguer | Carolina Andrade | Telma M. Gomes | Basit Olayanju | Abuzar Kabir | Crístina M.R. Rocha | J. A. Teixeira | Jorge A. M. Pereira
Source: Molecules (review, 2022-05-06)
Field: Chemistry — Analytical Chemistry

Green Extraction Techniques as Advanced Sample Preparation Approaches in Biological, Food, and Environmental Matrices: A Review Green extraction techniques (GreETs) emerged in the last decade as greener and sustainable alternatives to classical sample preparation procedures aiming to improve the selectivity and sensitivity of analytical methods, simultaneously reducing the deleterious side effects of classical extraction techniques (CETs) for both the operator and the environment. The implementation of improved processes that overcome the main constraints of classical methods in terms of efficiency and ability to minimize or eliminate the use and generation of harmful substances will promote more efficient use of energy and resources in close association with the principles supporting the concept of green chemistry. The current review aims to update the state of the art of some cutting-edge GreETs developed and implemented in recent years focusing on the improvement of the main analytical features, practical aspects, and relevant applications in the biological, food, and environmental fields. Approaches to improve and accelerate the extraction efficiency and to lower solvent consumption, including sorbent-based techniques, such as solid-phase microextraction (SPME) and fabric-phase sorbent extraction (FPSE), and solvent-based techniques (μQuEChERS; micro quick, easy, cheap, effective, rugged, and safe), ultrasound-assisted extraction (UAE), and microwave-assisted extraction (MAE), in addition to supercritical fluid extraction (SFE) and pressurized solvent extraction (PSE), are highlighted. Quechers ; Sorbent ; Supercritical fluid extraction ; Process engineering ; Extraction (chemistry) ; Sample preparation ; Computer science ; Biochemical engineering ; Environmentally friendly ; Environmental analysis ; Chemistry ; Chromatography ; Engineering Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Electrochemical Analysis and Applications
Authors: Yonghua Wei | Jin Wu | Yixuan Wu | Hongjiang Liu | Fanqiang Meng | Qiqi Liu | Adam C. Midgley | Xiangyun Zhang | Tianyi Qi | Helong Kang | Rui Chen | Deling Kong | Jie Zhuang | Xiyun Yan | Xinglu Huang
Source: Advanced Materials (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Prediction and Design of Nanozymes using Explainable Machine Learning An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R<sup>2</sup> (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science. Computer science ; Consistency (knowledge bases) ; Nanotechnology ; Biochemical engineering ; Nanomaterials ; Machine learning ; Materials science ; Artificial intelligence ; Engineering Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Byunghoon Ryu | Luqing Wang | Hongting Pu | Maria K. Y. Chan | Junhong Chen
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Understanding, discovery, and synthesis of 2D materials enabled by machine learning Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures. Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Vishal Chaudhary | Naveed Ashraf | Mohammad Khalid | Rashmi Walvekar | Ya Yang | Ajeet Kaushik | Yogendra Kumar Mishra
Source: Advanced Functional Materials (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Emergence of MXene–Polymer Hybrid Nanocomposites as High‐Performance Next‐Generation Chemiresistors for Efficient Air Quality Monitoring Abstract Air contamination is one of the foremost concerns of environmentalists worldwide, which has elevated global public health concerns for monitoring air contaminants and implementing appropriate safety policies. These facts have generated nascent global demand for exploring sustainable and translational strategies required to engineer affordable, intelligent, and miniaturized sensors because commercially available sensors lack lower detection limits, room temperature operation, and poor selectivity. The state‐of‐the‐art sensors are concerned with architecting advanced nanomaterials to achieve desired sensing performance. Recent studies demonstrate that neither pristine metal carbides/nitrides (MXenes) nor polymers (P) can address these practical challenges. However, synergistic combinations of various precursors as hybrid‐nanocomposites (MXP‐HNCs) have emerged as superior sensing materials to develop next‐generation intelligent environmental, industrial, and biomedical sensors. The expected outcomes could be manipulative due to optimizing physicochemical and morphological attributes like tunable interlayer‐distance, optimum porosity, enlarged effective surface area, rich surface functionalities, mechanical flexibility, and tunable conductivity. This review intends to detail a comprehensive summary of the advancements in state‐of‐the‐art MXP‐HNCs chemiresistors. Moreover, the underlying sensing phenomenon, chemiresistor architecture, and their monitoring performance are highlighted. Besides, an overview of challenges, potential solutions, and prospects of MXP‐HNCs as next‐generation intelligent field‐deployable sensors with the integration of IoT and AI are outlined. Materials science ; Nanocomposite ; Nanotechnology ; Quality (philosophy) ; Process engineering ; Systems engineering ; Engineering MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing