jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Zhu | Weitao Dai | Liming Wang | Cong Yao | Chenxi Wang | Bingsong Gu | Dichen Li | Jiankang He
Source: Polymers (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Electroactive Oxidized Alginate/Gelatin/MXene (Ti3C2Tx) Composite Hydrogel with Improved Biocompatibility and Self-Healing Property Conductive hydrogels (CHs) have shown promising potential applied as wearable or epidermal sensors owing to their mechanical adaptability and similarity to natural tissues. However, it remains a great challenge to develop an integrated hydrogel combining outstanding conductive, self-healing and biocompatible performances with simple approaches. In this work, we propose a "one-pot" strategy to synthesize multifunctional CHs by incorporating two-dimensional (2D) transition metal carbides/nitrides (MXenes) multi-layer nano-flakes as nanofillers into oxidized alginate and gelatin hydrogels to form the composite CHs with various MXene contents. The presence of MXene with abundant surface groups and outstanding conductivity could improve the mechanical property and electroactivity of the composite hydrogels compared to pure oxidized alginate dialdehyde-gelatin (ADA-GEL). MXene-ADA-GELs kept good self-healing properties due to the dynamic imine linkage of the ADA-GEL network and have a promoting effect on mouse fibroblast (NH3T3s) attachment and spreading, which could be a result of the integration of MXenes with stimulating conductivity and hydrophily surface. This study suggests that the electroactive MXene-ADA-GELs can serve as an appealing candidate for skin wound healing and flexible bio-electronics. Biocompatibility ; Gelatin ; Self-healing ; Composite number ; Materials science ; Composite material ; Biomedical engineering ; Chemistry ; Medicine ; Organic chemistry MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Photocatalysis Techniques
Authors: Ainara Valverde | Gabriel Ibrahin Tovar | Natalia Ahiova Río-López | Dimas Ignacio Torres | Maibelín Rosales | Stefan Wuttke | Arkaitz Fidalgo-Marijuán | José Porro | Mónica Jiménez‐Ruiz | Victoria García Sakai | Andreína García | José Manuel Laza | José Luis Vilas‐Vilela | Luís Lezama | M.I. Arriortua | Guillermo Javier Copello | Roberto Fernández de Luis
Source: Chemistry of Materials (article, 2022-10-24)
Field: Chemistry — Inorganic Chemistry

Designing Metal-Chelator-like Traps by Encoding Amino Acids in Zirconium-Based Metal–Organic Frameworks Metal chelators and porous sorbents are two of the forefront technologies applied for the recovery and separation of hazardous and/or valuable metal ions from aqueous solutions (i.e., polluted water sources, metal-rich mining wastewaters, acid leachates, and so forth). The transfer of the metal coordination functions of metal chelators to chemically stable host materials had only limited success so far. Here, we report the installation of natural acids (i.e., malic acid, mercaptosuccinic acid, succinic acid, fumaric acid, and citric acid) and amino acids (i.e., histidine, cysteine, and asparagine) within a porous zirconium-based trimesate metal–organic framework (MOF), namely, MOF-808. Applying this strategy, we were able to produce a pore environment spatially decorated with multiple functional groups usually found in commercial chelator molecules. The chemical stability of the amino acid molecules installed by the solvent-assisted ligand exchange has been studied to delimitate the applicability window of these materials. The adsorption affinity of MOF-808@(amino)acids in static and column-bed configurations can be fine-tuned as a function of the amino acid residues installed in the framework. MOF-808(amino)acid columns can be applied efficiently both for water remediation of heavy metals and for the separation of metal ions with different acidities. For instance, the initial trends for the dispersion of rare-earth elements have been identified. Electron paramagnetic resonance and inelastic neutron scattering spectroscopy reveal that MOF-808@(amino)acids stabilize metal centers as isolated and clustered species in a coordination fashion that involves both the amine and thiol functionals and that affects the vibrational freedom of some of the chemical groups of the amino acid molecules. The metal-ion stabilization within amino acid-decorated MOFs opens the avenue for application for pseudo biocatalysis purposes in the near future. Chemistry ; Picolinic acid ; Malic acid ; Zirconium ; Metal ions in aqueous solution ; Chelation ; Amino acid ; Inorganic chemistry ; Metal-organic framework ; Adsorption ; Ligand (biochemistry) ; Metal ; Succinic acid ; Iminodiacetic acid ; Histidine ; Carboxylate ; Citric acid ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Chemical Synthesis and Characterization
Authors: Brandon T. DiTullio | Lisa R. Savagian | Olivier Bardagot | Michel De Keersmaecker | Anna M. Österholm | Natalie Banerji | John R. Reynolds
Source: Journal of the American Chemical Society (article, 2022-12-23)
Field: Materials Science — Polymers and Plastics

Effects of Side-Chain Length and Functionality on Polar Poly(dioxythiophene)s for Saline-Based Organic Electrochemical Transistors Understanding the impact of side chains on the aqueous redox properties of conjugated polymers is crucial to unlocking their potential in bioelectrochemical devices, such as organic electrochemical transistors (OECTs). Here, we report a series of polar propylenedioxythiophene-based copolymers functionalized with glyme side chains of varying lengths as well as an analogue with short hydroxyl side chains. We show that long polar side chains are not required for achieving high volumetric capacitance (<i>C</i>*), as short hydroxy substituents can afford facile doping and high <i>C</i>* in saline-based electrolytes. Furthermore, we demonstrate that varying the length of the polar glyme chains leads to subtle changes in material properties. Increasing the length of glyme side chain is generally associated with an enhancement in OECT performance, doping kinetics, and stability, with the polymer bearing the longest side chains exhibiting the highest performance ([μ<i>C</i>*]<sub>OECT</sub> = 200 ± 8 F cm<sup>-1</sup> V<sup>-1</sup> s<sup>-1</sup>). The origin of this performance enhancement is investigated in different device configurations using <i>in situ</i> techniques (e.g., time-resolved spectroelectrochemistry and chronoamperometry). These studies suggest that the performance improvement is not due to significant changes in <i>C</i>* but rather due to variations in the inferred mobility. Through a thorough comparison of two different architectures, we demonstrate that device geometry can obfuscate the benchmarking of OECT active channel materials, likely due to contact resistance effects. By complementing all electrochemical and spectroscopic experiments with <i>in situ</i> measurements performed within a planar OECT device configuration, this work seeks to unambiguously assign material design principles to fine-tune the properties of poly(dioxythiophene)s relevant for application in OECTs. Side chain ; Chemistry ; Electrochemistry ; Electrolyte ; Conjugated system ; Conductive polymer ; Nanotechnology ; Polymer ; Chemical engineering ; Combinatorial chemistry ; Electrode ; Materials science ; Organic chemistry ; Physical chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Shiwei Lü | Zipeng Wang | Shifa Zhu
Source: Nature Communications (article, 2022-08-25)
Field: Chemistry — Organic Chemistry

Thiol-Yne click chemistry of acetylene-enabled macrocyclization Macrocycles have fascinated scientists for over half a century due to their aesthetically appealing structures and broad utilities in chemical, material, and biological research. However, the efficient preparation of macrocycles remains an ongoing research challenge in organic synthesis because of the high entropic penalty involved in the ring-closing process. Herein we report a photocatalyzed thiol-yne click reaction to forge diverse sulfur-containing macrocycles (up to 35-membered ring) and linear C2-linked 1,2-(S-S/S-P/S-N) functionalized molecules, starting from the simplest alkyne, acetylene. Preliminary mechanistic experiments support a visible light-mediated radical-polar crossover dihydrothiolation process. This operationally straightforward reaction is also amenable to the synthesis of organometallic complexes, bis-sulfoxide ligand and a pleuromutilin antibiotic drug Tiamulin, which provides a practical route to synthesize highly valued compounds from the feedstock acetylene gas. Acetylene ; Chemistry ; Click chemistry ; Combinatorial chemistry ; Alkyne ; Ring (chemistry) ; Thiol ; Retrosynthetic analysis ; Sulfur ; Organic chemistry ; Catalysis ; Total synthesis Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods
Authors: Michael B. Rooney | Benjamin P. MacLeod | Ryan Oldford | Zachary J. Thompson | Kolby L. White | Justin Tungjunyatham | Brian J. Stankiewicz | Curtis P. Berlinguette
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A self-driving laboratory designed to accelerate the discovery of adhesive materials This self-driving laboratory combines a robot for preparing and testing adhesive bonds with an optimizer to rapidly improve adhesive formulations. Adhesive ; Self adhesive ; Materials science ; Robot ; Nanotechnology ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Innovations in Concrete and Construction Materials
Authors: Ting‐Hsiang Hung | Zhi-Xun Xu | Dun‐Yen Kang | Li‐Chiang Lin
Source: The Journal of Physical Chemistry C (article, 2022-01-31)
Field: Chemistry — Inorganic Chemistry

Chemistry-Encoded Convolutional Neural Networks for Predicting Gaseous Adsorption in Porous Materials Metal–organic frameworks (MOFs) are an emerging class of materials possessing significant potential in separation and storage applications. Identifying optimal candidates from tens of thousands of MOFs that have been reported is a challenging task. To this end, machine learning (ML) represents a promising approach to facilitate the selection of best-performing MOFs. In this study, we propose a scheme to develop chemistry-encoded convolutional neural network (CNN) models to predict gaseous adsorption properties, i.e., Henry’s constants of adsorption and adsorption selectivity, in chemically diverse MOFs. To train CNN models, the MOF structures are represented by their atomic locations coupled with associated chemical information of each framework atom including the 6–12 Lennard-Jones parameters (i.e., σ and ε) and point-charge values (i.e., q). Henry’s constants of CH4 and CO2 in approximately 10 000 MOF structures computed via molecular simulations are used for training and testing. Our developed CNN models show a superior prediction accuracy. Models for zeolites are also developed for comparative purposes. Various key aspects of the CNN models, such as data augmentation and spatial resolution, are also systematically investigated for achieving high accuracy. Convolutional neural network ; Adsorption ; Computer science ; Deep learning ; Metal-organic framework ; Selectivity ; Artificial neural network ; Atom (system on chip) ; Resolution (logic) ; Porous medium ; Artificial intelligence ; Chemistry ; Porosity ; Nanotechnology ; Materials science ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Sora Ishioka | Aya Fujiwara | Sunao Nakanowatari | Lauren Takahashi | Toshiaki Taniike | Keisuke Takahashi
Source: ACS Catalysis (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane Catalysts descriptors for representing catalytic activities have been challenging in regard to machine learning. Machine learning and catalyst big data generated from high-throughput experiments are combined to explore the catalyst descriptors. Catalyst descriptors are designed using the physical quantities from the periodic table in the oxidative coupling of methane (OCM) reaction. Machine learning unveils the five key physical quantities representing ethylene/ethane selectivity (C2s) in the OCM reaction, where machine learning predicted three catalysts to have high C2s values. Experiments confirm that the proposed three catalysts have high C2s values in the OCM reaction. Hence, the physical quantities can be used as alternative descriptors for designing heterogeneous catalysts. Catalysis ; Oxidative coupling of methane ; Selectivity ; Chemistry ; Methane ; Ethylene ; Table (database) ; Throughput ; Molecular descriptor ; Biological system ; Computer science ; Machine learning ; Organic chemistry ; Database ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Manu Suvarna | Phil Preikschas | Javier Pérez‐Ramírez
Source: ACS Catalysis (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis <i>via</i> Machine Learning Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, <i>via</i> syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO<sub>2</sub> catalysts (<i>P</i> = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STY<sub>HA</sub>) with an accuracy of <i>R</i> <sup>2</sup> = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO<sub>2</sub> catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems. Rhodium ; Catalysis ; Yield (engineering) ; Transition metal ; Chemistry ; Feature selection ; Alcohol ; Reactivity (psychology) ; Artificial intelligence ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Electrocatalysts for Energy Conversion
Authors: Benjamin W. J. Chen | Bo Wang | Michael B. Sullivan | Armando Borgna | Jia Zhang
Source: ACS Catalysis (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Unraveling the Synergistic Effect of Re and Cs Promoters on Ethylene Epoxidation over Silver Catalysts with Machine Learning-Accelerated First-Principles Simulations Co-promotion of Ag catalysts by Re and Cs leads to ethylene oxide (EO) selectivity higher than that achievable by either promoter alone. However, the atomistic and electronic mechanisms behind this synergistic co-promotion remain unclear. Here, we shed light on how Re and Cs promote ethylene epoxidation by elucidating realistic catalyst models using machine learning-accelerated first-principles simulations, characterizing their electronic properties, and constructing structure–selectivity relationships. Together with a comprehensive set of experimental data, we show that the synergistic effects of co-promotion are twofold. First, Re, in the form of ReO4, increases the dispersion of Cs, thereby maximizing the promotional effects of Cs. Second, combining electron-withdrawing Re and electron-donating Cs balances the amount of charge transferred to the catalyst. Akin to the Sabatier principle, this maximizes EO selectivity by preventing the formation of overly nucleophilic oxygen species that facilitate ethylene combustion and overly electrophilic Ag centers that accelerate EO isomerization. The insights from our work may guide the development of improved promoter combinations, while our methodology may serve as a template for studying other complex, multiply promoted reaction systems. Selectivity ; Catalysis ; Nucleophile ; Ethylene ; Chemistry ; Isomerization ; Electrophile ; Electronic effect ; Combinatorial chemistry ; Nanotechnology ; Photochemistry ; Organic chemistry ; Materials science Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Byung Do Lee | Jin-Woong Lee | Woon Bae Park | Joonseo Park | Min-Young Cho | Satendra Pal Singh | Myoungho Pyo | Kee‐Sun Sohn
Source: Advanced Intelligent Systems (article, 2022-05-22)
Field: Materials Science — Materials Chemistry

Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed. Autoencoder ; Encoder ; Convolutional neural network ; Artificial intelligence ; Computer science ; Multilayer perceptron ; Artificial neural network ; Deep learning ; Dimensionality reduction ; Cluster analysis ; Pattern recognition (psychology) ; Materials science ; Machine learning X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Wenbin Xu | Karsten Reuter | Mie Andersen
Source: Nature Computational Science (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation Representation (politics) ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Markus J. Buehler
Source: Journal of Applied Mechanics (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model Dynamic fracture is an important area of materials analysis, assessing the atomic-level mechanisms by which materials fail over time. Here, we focus on brittle materials failure and show that an atomistically derived progressive transformer diffusion machine learning model can effectively describe the dynamics of fracture, capturing important aspects such as crack dynamics, instabilities, and initiation mechanisms. Trained on a small dataset of atomistic simulations, the model generalizes well and offers a rapid assessment of dynamic fracture mechanisms for complex geometries, expanding well beyond the original set of atomistic simulation results. Various validation cases, progressively more distinct from the data used for training, are presented and analyzed. The validation cases feature distinct geometric details, including microstructures generated by a generative neural network used here to identify novel bio-inspired material designs for mechanical performance. For all cases, the model performs well and captures key aspects of material failure. Computer science ; Brittleness ; Generative grammar ; Fracture (geology) ; Transformer ; Materials science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Rees Chang | Yu-Xiong Wang | Elif Ertekin
Source: npj Computational Materials (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework Abstract While machine learning has emerged in recent years as a useful tool for the rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is often impractical. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data-scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 14 of 19 materials property regression tasks, performing comparably on four of the remaining five. The approach is interpretable, model-agnostic, and scalable to combining an arbitrary number of pre-trained models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community. Overfitting ; Computer science ; Machine learning ; Scalability ; Artificial intelligence ; Pairwise comparison ; Task (project management) ; Property (philosophy) ; Predictive modelling ; Data mining ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals