jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P K Nitha | Arunkumar Chandrasekhar
Source: Materials Today Energy (article, 2023-08-22)
Field: Materials Science — Polymers and Plastics

Marriage between metal-organic frameworks/covalent-organic frameworks and triboelectric nanogenerator for energy harvesting —A review Triboelectric effect ; Nanogenerator ; Materials science ; Contact electrification ; Nanotechnology ; Metal-organic framework ; Covalent bond ; Mechanical energy ; Composite material ; Adsorption ; Organic chemistry ; Chemistry ; Thermodynamics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Covalent Organic Framework Applications
Authors: Kelsi R. Hall | Chris Joseph | Iván Ayuso‐Fernández | Ashish Tamhankar | Lukas Rieder | Rannei Skaali | Ole Golten | Frank Neese | Åsmund K. Røhr | Sergio A. V. Jannuzzi | Serena DeBeer | Vincent G. H. Eijsink | Morten Sørlie
Source: Journal of the American Chemical Society (article, 2023-08-16)
Field: Chemistry — Inorganic Chemistry

A Conserved Second Sphere Residue Tunes Copper Site Reactivity in Lytic Polysaccharide Monooxygenases Lytic polysaccharide monooxygenases (LPMOs) are powerful monocopper enzymes that can activate strong C-H bonds through a mechanism that remains largely unknown. Herein, we investigated the role of a conserved glutamine/glutamate in the second coordination sphere. Mutation of the Gln in <i>Nc</i>AA9C to Glu, Asp, or Asn showed that the nature and distance of the headgroup to the copper fine-tune LPMO functionality and copper reactivity. The presence of Glu or Asp close to the copper lowered the reduction potential and decreased the ratio between the reduction and reoxidation rates by up to 500-fold. All mutants showed increased enzyme inactivation, likely due to changes in the confinement of radical intermediates, and displayed changes in a protective hole-hopping pathway. Electron paramagnetic resonance (EPR) and X-ray absorption spectroscopic (XAS) studies gave virtually identical results for all <i>Nc</i>AA9C variants, showing that the mutations do not directly perturb the Cu(II) ligand field. DFT calculations indicated that the higher experimental reoxidation rate observed for the Glu mutant could be reconciled if this residue is protonated. Further, for the glutamic acid form, we identified a Cu(III)-hydroxide species formed in a single step on the H<sub>2</sub>O<sub>2</sub> splitting path. This is in contrast to the Cu(II)-hydroxide and hydroxyl intermediates, which are predicted for the WT and the unprotonated glutamate variant. These results show that this second sphere residue is a crucial determinant of the catalytic functioning of the copper-binding histidine brace and provide insights that may help in understanding LPMOs and LPMO-inspired synthetic catalysts. Chemistry ; Coordination sphere ; Protonation ; Electron paramagnetic resonance ; Stereochemistry ; Copper ; Inner sphere electron transfer ; Monooxygenase ; Residue (chemistry) ; Outer sphere electron transfer ; Histidine ; Mutant ; Crystallography ; Photochemistry ; Amino acid ; Enzyme ; Biochemistry ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Enzyme-mediated dye degradation ; Enzyme Catalysis and Immobilization
Authors: Edwin Shigwenya Madivoli | Justine V. Schwarte | Patrick Gachoki Kareru | Anthony Gachanja | Katharina M. Fromm
Source: Polymers (article, 2023-02-21)
Field: Chemistry — Organic Chemistry

Stimuli-Responsive and Antibacterial Cellulose-Chitosan Hydrogels Containing Polydiacetylene Nanosheets Herein, we report a stimuli-responsive hydrogel with inhibitory activity against <i>Escherichia coli</i> prepared by chemical crosslinking of carboxymethyl chitosan (CMCs) and hydroxyethyl cellulose (HEC). The hydrogels were prepared by esterification of chitosan (Cs) with monochloroacetic acid to produce CMCs which were then chemically crosslinked to HEC using citric acid as the crosslinking agent. To impart a stimuli responsiveness property to the hydrogels, polydiacetylene-zinc oxide (PDA-ZnO) nanosheets were synthesized in situ during the crosslinking reaction followed by photopolymerization of the resultant composite. To achieve this, ZnO was anchored on carboxylic groups in 10,12-pentacosadiynoic acid (PCDA) layers to restrict the movement of the alkyl portion of PCDA during crosslinking CMCs and HEC hydrogels. This was followed by irradiating the composite with UV radiation to photopolymerize the PCDA to PDA within the hydrogel matrix so as to impart thermal and pH responsiveness to the hydrogel. From the results obtained, the prepared hydrogel had a pH-dependent swelling capacity as it absorbed more water in acidic media as compared to basic media. The incorporation of PDA-ZnO resulted in a thermochromic composite responsive to pH evidenced by a visible colour transition from pale purple to pale pink. Upon swelling, PDA-ZnO-CMCs-HEC hydrogels had significant inhibitory activity against <i>E. coli</i> attributed to the slow release of the ZnO nanoparticles as compared to CMCs-HEC hydrogels. In conclusion, the developed hydrogel was found to have stimuli-responsive properties and inhibitory activity against E. coli attributed to zinc nanoparticles. Self-healing hydrogels ; Swelling ; Chitosan ; Carboxymethyl cellulose ; Materials science ; Polymer chemistry ; Composite number ; Nanoparticle ; Photopolymer ; Chemical engineering ; Chemistry ; Organic chemistry ; Polymer ; Nanotechnology ; Polymerization ; Composite material ; Sodium Polydiacetylene-based materials and applications ; Ocular Surface and Contact Lens ; Supramolecular Self-Assembly in Materials
Authors: Arianna Balestri | Jacopo Cardellini | Debora Berti
Source: Current Opinion in Colloid & Interface Science (article, 2023-05-29)
Field: Materials Science — Materials Chemistry

Gold and silver nanoparticles as tools to combat multidrug-resistant pathogens Nanotechnology ; Nanomaterials ; Nanoparticle ; Antimicrobial ; Plasmon ; Materials science ; Colloidal gold ; Colloid ; Chemistry ; Organic chemistry Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Inkyu Oh | Michael A. Pence | Nikita G. Lukhanin | Oliver Rodríguez | Charles M. Schroeder | Joaquín Rodríguez‐López
Source: Device (article, 2023-10-10)
Field: Chemistry — Electrochemistry

The Electrolab: An open-source, modular platform for automated characterization of redox-active electrolytes Electrochemical characterization of redox-active molecules in solution requires exploration of manifold conditions (e.g., concentration, electrolyte type, pH, ionic strength), leading to tedious and time-consuming experiments that are prone to user error. Here, we introduce the Electrolab, a modular, automated electrochemical characterization platform that seamlessly interfaces with common laboratory instrumentation and low-cost electromechanical components. We integrated a gantry-type robot carrying a multipurpose nozzle assembly to dispense and mix solutions as well as degas and clean a cell containing multiplexed microelectrochemical arrays. The system operates using Python code and a universal Arduino-based controller. We demonstrate the Electrolab by autonomously analyzing a redox mediator by performing 200 voltammograms and data analysis steps across a range of conditions. In addition, the Electrolab is used to titrate a redox-active polymer solution to identify conditions for optimizing electrochemical performance. Overall, the Electrolab device enables high-throughput, systematic exploration of redox electrolytes, opening new avenues for closed-loop optimization. Modular design ; Redox ; Computer science ; Electrolyte ; Characterization (materials science) ; Materials science ; Nanotechnology ; Electrode ; Chemistry Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Shun Muroga | Yasuaki Miki | Kenji Hata
Source: Advanced Science (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

A Comprehensive and Versatile Multimodal Deep‐Learning Approach for Predicting Diverse Properties of Advanced Materials A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials. Flexibility (engineering) ; Computer science ; Property (philosophy) ; Adaptability ; Generative grammar ; Artificial intelligence ; Deep learning ; Machine learning ; Mathematics Machine Learning in Materials Science ; Composite Material Mechanics ; Fiber-reinforced polymer composites
Authors: Hao Zou | Yueyan Tian | Ligang Zhang | Renhao Xue | Zixuan Deng | Mingming Lu | Jianxin Wang | Libin Liu
Source: Rare Metals (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Integrating machine learning and CALPHAD method for exploring low‐modulus near‐β‐Ti alloys Abstract Traditional theoretical and empirical calculation methods can guide the design of β‐ and metastable β‐alloys for bio‐titanium. However, it is still difficult to obtain novel near‐β‐Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams (CALPHAD) to facilitate the design of near‐β‐Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features (the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three‐step feature selection. With these features, a highly accurate model was built for predicting the modulus of near‐β‐Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well‐trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near‐β‐Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy (Ti–26Nb–4Zr–4Sn–1Mo–Ta) exhibits an ultra‐low modulus (36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method. CALPHAD ; Materials science ; Titanium alloy ; Electronegativity ; Modulus ; Elastic modulus ; Thermodynamics ; Phase diagram ; Alloy ; Phase (matter) ; Metallurgy ; Composite material Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Intermetallics and Advanced Alloy Properties
Authors: Weiye Jin | Jiayun Pei | Pu Xie | Jincong Chen | Haiyan Zhao
Source: ACS Applied Nano Materials (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Mechanical Properties and Performance of Nickel–Graphene Nanocomposites Using Molecular Dynamics Simulation Data Accurately predicting the mechanical properties of graphene-reinforced metal matrix composites is of utmost importance due to its critical role in the design and utilization of nanocomposite materials. The conventional approach of employing molecular dynamics (MD) simulations for this purpose faces a substantial increase in computational costs when considering the combined effects of multiple factors. In contrast, machine learning (ML) models offer a rapid and efficient alternative by swiftly comprehending and predicting material properties following adequate training. In this paper, we employed a long short-term memory (LSTM) model, based on MD calculation data, to accurately predict the mechanical response and key mechanical properties of nickel–graphene composite nanomaterials. Specifically, we thoroughly investigated the comprehensive impact of temperature, graphene orientation angle, and graphene volume fraction on the mechanical properties. Our verification process revealed that high graphene volume and high orientation angles led to increased dislocation absorption, consequently weakening the composite material. To assess the hardness prediction capabilities, we conducted a comparative analysis between the LSTM model and classical multilayer perceptron (MLP) neural networks, as well as the traditional nonlinear regression method, support vector machine (SVM). The obtained results demonstrated that the LSTM models exhibited a remarkable ability to accurately predict the mechanical properties of nickel–graphene composite nanomaterials, showcasing Pearson correlation coefficients exceeding 0.95 when compared to the calculation data. Moreover, the LSTM model effectively comprehends and predicts the complete indentation depth–force curve, thus providing enhanced predictions of material properties. This study proposes an innovative combination of MD simulations and ML models, which holds significant application potential in predicting and designing the performance of graphene-reinforced metal matrix composite materials. Graphene ; Materials science ; Nanocomposite ; Nanomaterials ; Artificial intelligence ; Machine learning ; Support vector machine ; Molecular dynamics ; Volume fraction ; Artificial neural network ; Computer science ; Composite material ; Mechanical engineering ; Nanotechnology ; Engineering ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Hongshuai Wang | Jie Feng | Zhihao Dong | Lujie Jin | Miaomiao Li | Jianyu Yuan | Youyong Li
Source: npj Computational Materials (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Efficient screening framework for organic solar cells with deep learning and ensemble learning Abstract Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost, lightweight, and flexible power sources. Functional molecular design and synthesis have been put forward to accelerate the discovery of ideal organic semiconductors. However, it is extremely expensive to conduct experimental screening of the wide organic compound space. Here we develop a framework by combining a deep learning model (graph neural network) and an ensemble learning model (Light Gradient Boosting Machine), which enables rapid and accurate screening of organic photovoltaic molecules. This framework establishes the relationship between molecular structure, molecular properties, and device efficiency. Our framework evaluates the chemical structure of the organic photovoltaic molecules directly and accurately. Since it does not involve density functional theory calculations, it makes fast predictions. The reliability of our framework is verified with data from previous reports and our newly synthesized organic molecules. Our work provides an efficient method for developing new organic optoelectronic materials. Organic solar cell ; Photovoltaic system ; Boosting (machine learning) ; Chemical space ; Computer science ; Organic semiconductor ; Deep learning ; Artificial intelligence ; Artificial neural network ; Organic molecules ; Ensemble learning ; Reliability (semiconductor) ; Materials science ; Machine learning ; Nanotechnology ; Molecule ; Power (physics) ; Chemistry ; Engineering ; Electrical engineering ; Optoelectronics ; Physics ; Drug discovery Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Rakesh Suthar | T. Abhijith | Supravat Karak
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors The machine learning approach was employed to explore the relationship between molecular structure and photovoltaic properties using frontier molecular orbital and RDKit descriptors, which enabled us to screen and identify potential donor and acceptor combinations for efficient organic solar cells. Photovoltaic system ; Organic solar cell ; Fullerene ; Materials science ; Acceptor ; Artificial intelligence ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Physics ; Organic chemistry ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Amir Kotobi | Kanishka Singh | Daniel Höche | Sadia Bari | Robert H. Meißner | Annika Bande
Source: Journal of the American Chemical Society (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models' predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum. Interpretability ; X-ray absorption spectroscopy ; Chemistry ; Atomic orbital ; Absorption spectroscopy ; Spectral line ; Artificial neural network ; Flattening ; Graph ; Biological system ; Molecule ; Set (abstract data type) ; Artificial intelligence ; Computer science ; Theoretical computer science ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Junho Song | Jae Hoon Lee | Namjung Kim | Kyoungmin Min
Source: International Journal of Precision Engineering and Manufacturing (review, 2023-08-07)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in the Design of Innovative Metamaterials: A Comprehensive Review Metamaterial ; Computer science ; Key (lock) ; Generative grammar ; Artificial intelligence ; Systems engineering ; Property (philosophy) ; Engineering ; Materials science Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Cellular and Composite Structures
Authors: Peter A. Beaucage | Tyler B. Martin
Source: Chemistry of Materials (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using X-ray and Neutron Scattering The application of machine learning techniques to X-ray scattering experiments has been of significant recent interest, offering advances in areas such as the study of complex oxides. Despite these success stories, few applications of these techniques into soft materials have been reported, likely due in part to the highly nonequilibrium nature of soft materials phase spaces and the complexities associated with autonomous formulation preparation. Here, we report the design of the Autonomous Formulation Laboratory, a robust platform for the automated synthesis and measurement of complex liquid mixtures using X-ray and neutron scattering, readily extensible to system-specific complementary techniques such as spectroscopy and rheometry. We describe the application of the platform to generate dense, highly reproducible data sets on material systems ranging from silica nanoparticles to block copolymer micelles. We expect the platform to prove revolutionary to the understanding of the stability of complex liquid formulations and the resulting data sets to provide fertile ground for the development of machine learning techniques for complex soft materials phase spaces. Rheometry ; Neutron scattering ; Materials science ; Small-angle neutron scattering ; Scattering ; Extensibility ; Characterization (materials science) ; Small-angle X-ray scattering ; Computer science ; Nanotechnology ; Physics ; Rheology ; Optics ; Composite material Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Scientific Computing and Data Management
Authors: Marcos F. Calegari Andrade | Roberto Car | Annabella Selloni
Source: Proceedings of the National Academy of Sciences (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H<sub>2</sub>O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H<sub>2</sub>O, and a distance of [Formula: see text] 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process. Hydronium ; Molecular dynamics ; Ionization ; Chemistry ; Chemical physics ; Dissociation (chemistry) ; Ion ; Potential of mean force ; Electrostatics ; Density functional theory ; Acid dissociation constant ; Ab initio ; Water model ; Aqueous solution ; Solvation ; Self-ionization of water ; Umbrella sampling ; Solvation shell ; Atomic physics ; Computational chemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics