jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiao Huang | Wei Liu | Jingjing Zhang | Min Young Song | Chang Zhang | Jingwen Li | Jian Zhang | Deli Wang
Source: ACS Applied Materials & Interfaces (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Coupling Co–N–C with MXenes Yields Highly Efficient Catalysts for H<sub>2</sub>O<sub>2</sub> Production in Acidic Media The electrochemical oxygen reduction reaction (ORR) offers a promising method to replace the anthraquinone process for hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) production. However, the efficiency of this process suffers from sluggish kinetics, particularly in an acidic environment. Therefore, employing catalysts with high electroactivity is highly desirable for H<sub>2</sub>O<sub>2</sub> synthesis. Here, an effective strategy for preparing Co-N-C/Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> with high H<sub>2</sub>O<sub>2</sub> selectivity and ORR reactivity is proposed. The acquired Co-N-C/Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> shows excellent H<sub>2</sub>O<sub>2</sub> electrosynthesis performance in acidic media with H<sub>2</sub>O<sub>2</sub> productivity of up to 3200 ppm h<sup>-1</sup>, superior to state-of-the-art catalysts. Interestingly, a H<sub>2</sub>O<sub>2</sub> concentration of 6.0 wt % was obtained after the stability test, and the Co-N-C/Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> catalyst was found to effectively catalyze organic dye degradation. Further analysis reveals that the enhanced H<sub>2</sub>O<sub>2</sub> electrosynthesis performance originates from the layered structure and the oxygen functional groups of Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>. The layered structure can effectively promote increased exposure of active sites, while the oxygen functional groups will fine-tune the electronic structure of Co atoms, allowing a selective ORR pathway to produce H<sub>2</sub>O<sub>2</sub>. This work provides a strategy to design and fabricate highly efficient catalysts for H<sub>2</sub>O<sub>2</sub> production and degradation of organic pollutants. Catalysis ; Electrosynthesis ; Materials science ; Hydrogen peroxide ; Selectivity ; Reactivity (psychology) ; Electrochemistry ; Chemical engineering ; Anthraquinone ; Hydrogen production ; Inorganic chemistry ; Chemistry ; Electrode ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Qiang Gao | Hao Yang | Chi Wang | Xinying Xie | Kaixuan Liu | Ying Lin | Shuangyan Han | Ming-Jun Zhu | Markus Neureiter | Yina Lin | Jianwen Ye
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-07-19)
Field: Materials Science — Biomaterials

Advances and trends in microbial production of polyhydroxyalkanoates and their building blocks With the rapid development of synthetic biology, a variety of biopolymers can be obtained by recombinant microorganisms. Polyhydroxyalkanoates (PHA) is one of the most popular one with promising material properties, such as biodegradability and biocompatibility against the petrol-based plastics. This study reviews the recent studies focusing on the microbial synthesis of PHA, including chassis engineering, pathways engineering for various substrates utilization and PHA monomer synthesis, and PHA synthase modification. In particular, advances in metabolic engineering of dominant workhorses, for example <i>Halomonas, Ralstonia eutropha, Escherichia coli</i> and <i>Pseudomonas,</i> with outstanding PHA accumulation capability, were summarized and discussed, providing a full landscape of diverse PHA biosynthesis. Meanwhile, we also introduced the recent efforts focusing on structural analysis and mutagenesis of PHA synthase, which significantly determines the polymerization activity of varied monomer structures and PHA molecular weight. Besides, perspectives and solutions were thus proposed for achieving scale-up PHA of low cost with customized material property in the coming future. Polyhydroxyalkanoates ; Metabolic engineering ; Biochemical engineering ; Synthetic biology ; Ralstonia ; Chemistry ; Escherichia coli ; Microorganism ; Biochemistry ; Computational biology ; Biotechnology ; Biology ; Bacteria ; Enzyme ; Engineering ; Gene ; Genetics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Md R. Amin | Farhana Yasmin | Sujan Dey | Shafi Mahmud | Md A. Saleh | Talha B. Emran | Imtiaj Hasan | Sultana Rajia | Yukiko Ogawa | Yuki Fujii | Masao Yamada | Yasuhiro Ozeki | Sarkar M. A. Kawsar
Source: Glycoconjugate Journal (article, 2022-01-17)
Field: Chemistry — Organic Chemistry

Methyl β-D-galactopyranoside esters as potential inhibitors for SARS-CoV-2 protease enzyme: synthesis, antimicrobial, PASS, molecular docking, molecular dynamics simulations and quantum computations Molecular dynamics ; Antimicrobial ; Bacillus subtilis ; Docking (animal) ; Acylation ; Molecular mechanics ; Lipase ; Molecular model ; Candida antarctica Carbohydrate Chemistry and Synthesis ; Nonlinear Optical Materials Research ; Seaweed-derived Bioactive Compounds
Authors: Tobias Hilche | Sara L. Younas | Andreas Gansäuer | Jan Streuff
Source: ChemCatChem (article, 2022-05-27)
Field: Chemistry — Organic Chemistry

A Guide to Low‐Valent Titanocene Complexes as Tunable Single‐Electron Transfer Catalysts for Applications in Organic Chemistry Abstract Low‐valent titanocene catalysts are a versatile tool for organic synthesis. They promote inter‐ and intramolecular reactions ranging from homolytic bond cleavages to reductive umpolung reactions to additions and cyclizations in single electron steps. These reactions heavily depend on the redox potential of an in situ formed titanium(III) center, which can be adjusted by the choice of appropriate ligands. We herein review various chiral and achiral ligand‐modified titanocene catalysts and their reduction potentials E p/2 obtained via cyclic voltammetry. The latter are found to correlate with the Hammett parameters σ p of the cyclopentadienyl substituents and to the p K a values of the corresponding acids of the Ti−X ligands. For selected examples, we further discuss how the adjustment of the redox properties through modifications of the titanocene ligands can lead to greatly improved reaction outcomes in titanium(III) catalyzed single‐electron transfer reactions. Chemistry ; Cyclopentadienyl complex ; Intramolecular force ; Umpolung ; Catalysis ; Redox ; Electron transfer ; Homolysis ; Ligand (biochemistry) ; Photochemistry ; Combinatorial chemistry ; Cyclic voltammetry ; Organic chemistry ; Electrochemistry ; Radical ; Physical chemistry ; Electrode Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Oxidative Organic Chemistry Reactions
Authors: Sabine M. Neumayer | Mengwei Si | Junkang Li | Pai-Ying Liao | Tao Lei | Andrew O’Hara | Sokrates T. Pantelides | Peide D. Ye | Petro Maksymovych | Nina Balke
Source: ACS Applied Materials & Interfaces (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Ionic Control over Ferroelectricity in 2D Layered van der Waals Capacitors The van der Waals layered material CuInP<sub>2</sub>S<sub>6</sub> features interesting functional behavior, including the existence of four uniaxial polarization states, polarization reversal against the electric field through Cu ion migration, a negative-capacitance regime, and reversible extraction of Cu ions. At the heart of these characteristics lies the high mobility of Cu ions, which also determines the spontaneous polarization. Therefore, Cu migration across the lattice results in unusual ferroelectric behavior. Here, we demonstrate how the interplay of polar and ionic properties provides a path to ionically controlled ferroelectric behavior, achieved by applying selected DC voltage pulses and subsequently probing ferroelectric switching during fast triangular voltage sweeps. Using current measurements and theoretical calculations, we observe that increasing DC pulse duration results in higher ionic currents, the buildup of an internal electric field that shifts polarization loops, and an increase in total switchable polarization by ∼50% due to the existence of a high polarization phase which is stabilized by the internal electric field. Apart from tuning ferroelectric behavior by selected square pulses, hysteretic polarization switching can even be entirely deactivated and reactivated, resulting in three-state systems where polarization switching is either inhibited or can be performed in two different directions. Ferroelectricity ; Polarization (electrochemistry) ; Materials science ; Electric field ; van der Waals force ; Ionic bonding ; Ion ; Condensed matter physics ; Capacitance ; Chemical physics ; Optoelectronics ; Dielectric ; Electrode ; Physics ; Chemistry ; Molecule ; Physical chemistry 2D Materials and Applications ; Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Raymundo Arróyave | Danial Khatamsaz | Brent Vela | Richard Couperthwaite | Abhilash Molkeri | Prashant Singh | D. D. Johnson | Xiaoning Qian | Ankit Srivastava | Douglas Allaire
Source: MRS Communications (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

A perspective on Bayesian methods applied to materials discovery and design Context (archaeology) ; Computer science ; Data science ; Bayesian probability ; Transformative learning ; Systems engineering ; Management science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Manufacturing Process and Optimization
Authors: Rajendra Prasath | A. Girisha | T. Gunavardhana Naidu
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Advancement of machine learning in materials science Workflow ; Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Characterization (materials science) ; Data science ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced X-ray and CT Imaging
Authors: Rodrigo Freitas | Yifan Cao
Source: MRS Communications (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for crystal defects Abstract Decades of advancements in strategies for the calculation of atomic interactions have culminated in a class of methods known as machine-learning interatomic potentials (MLIAPs). MLIAPs dramatically widen the spectrum of materials systems that can be simulated with high physical fidelity, including their microstructural evolution and kinetics. This framework, in conjunction with cross-scale simulations and in silico microscopy, is poised to bring a paradigm shift to the field of atomistic simulations of materials. In this prospective article we summarize recent progress in the application of MLIAPs to crystal defects. Graphical abstract Materials science ; Fidelity ; Molecular dynamics ; Crystal (programming language) ; High fidelity ; Atomic units ; Scale (ratio) ; Nanotechnology ; Computer science ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Mathias Schreiner | Arghya Bhowmik | Tejs Vegge | Peter Bjørn Jørgensen | Ole Winther
Source: Machine Learning Science and Technology (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

NeuralNEB—neural networks can find reaction paths fast Abstract Quantum mechanical methods like density functional theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale exploration. Machine learning (ML) models have turned out to be excellent emulators of small molecule DFT calculations and could possibly replace DFT in such tasks. For kinetics, success relies primarily on the models’ capability to accurately predict the potential energy surface around transition-states and minimal energy paths. Previously this has not been possible due to scarcity of relevant data in the literature. In this paper we train equivariant graph neural network-based models on data from 10 000 elementary reactions from the recently published Transition1x dataset. We apply the models as potentials for the nudged elastic band algorithm and achieve a mean average error of 0.23 eV and root mean squared error of 0.52 eV on barrier energies on unseen reactions. We compare the results against equivalent models trained on QM9x and ANI1x. We also compare with and outperform Density Functional based Tight Binding on both accuracy and required computational resources. The implication is that ML models are now at a level where they can be applied to studying chemical reaction kinetics given a sufficient amount of data relevant to this task. Density functional theory ; Computer science ; Potential energy surface ; Artificial neural network ; Mean squared error ; Transition state ; Quantum ; Algorithm ; Artificial intelligence ; Mathematics ; Computational chemistry ; Molecule ; Chemistry ; Physics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Udesh M. H. U. Kankanamge | Johannes Reiner | Xingjun Ma | Santiago Corujeira Gallo | Wei Xu
Source: Journal of Materials Science (article, 2022-10-17)
Field: Materials Science — Materials Chemistry

Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys Abstract With the increasing use of CubeSats in space exploration, the demand for reliable high-temperature shape memory alloys (HTSMA) continues to grow. A wide range of HTSMAs has been investigated over the past decade but finding suitable alloys by means of trial-and-error experiments is cumbersome and time-consuming. The present work uses a data-driven approach to identify NiTiHf alloys suitable for actuator applications in space. Seven machine learning (ML) models were evaluated, and the best fit model was selected to identify new alloy compositions with targeted transformation temperature (Ms), thermal hysteresis, and work output. Of the studied models, the K-nearest neighbouring ML model offers more reliable and accurate prediction in developing NiTiHf alloys with balanced functional properties and aids our existing understanding on compositional dependence of transformation temperature, thermal hysteresis and work output. For instance, the transformation temperature of NiTiHf alloys is more sensitive to Ni variation with increasing Hf content. A maximum Ms reduction rate of 6.12 °C per 0.01 at.% Ni is attained at 30 at.% Hf, and with a Ni content between 50 and 51 at.%. Graphical abstract Materials science ; Shape-memory alloy ; Hysteresis ; Alloy ; Work (physics) ; Transformation (genetics) ; Solid mechanics ; Atmospheric temperature range ; Actuator ; Thermal ; Thermodynamics ; Content (measure theory) ; Range (aeronautics) ; Computer science ; Artificial intelligence ; Metallurgy ; Composite material ; Mathematics ; Condensed matter physics ; Mathematical analysis Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Transition Metal Oxide Nanomaterials
Authors: Jun Luo | Jiao Chen | Hui Liu | Cheng Zhi Huang | Jun Zhou
Source: Chemical Communications (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-efficiency synthesis of red carbon dots using machine learning Due to their excellent optical properties, red carbon dots (CDs) have been widely used in cell imaging and biomedical therapy. However, the efficiency of red CD synthesis is deficient, and the synthesis cost is high. Here, we propose an efficient synthesis method based on machine learning to assist researchers in synthesizing red fluorescent CDs. This strategy can quickly and efficiently predict the predesigned conditions of CD synthesis. It avoids invalid synthetic experiments and improves the efficiency of red CD synthesis. Carbon fibers ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Algorithm Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Zhuoqiang Guo | Denghui Lu | Yujin Yan | Siyu Hu | Rongrong Liu | Guangming Tan | Ninghui Sun | Wanrun Jiang | Lijun Liu | Yixiao Chen | Linfeng Zhang | Mohan Chen | Handong Wang | Weile Jia
Source:  (preprint, 2022-03-28)
Field: Materials Science — Materials Chemistry

Extending the limit of molecular dynamics with <i>ab initio</i> accuracy to 10 billion atoms High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 -- 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an unprecedented 17 billion atoms. The strong scaling of a 13.5-million atom copper system shows that the time-to-solution can be 7 times faster, reaching 11.2 nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on ab initio accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications. Computer science ; Supercomputer ; Computational science ; Molecular dynamics ; Kernel (algebra) ; Ab initio ; Parallel computing ; Embedded atom model ; Physics ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Protein Structure and Dynamics
Authors: Fengqing Chen | Longjie Weng | Jinhe Wang | Pin Chieh Wu | Dianpu Ma | Fei Pan | Peng Ding
Source: Composites Science and Technology (article, 2022-11-08)
Field: Materials Science — Polymers and Plastics

An adaptive framework to accelerate optimization of high flame retardant composites using machine learning Fire retardant ; Polymer ; Materials science ; Composite material ; Computer science ; Lasso (programming language) ; Nonlinear system ; Hyperparameter ; Artificial neural network ; Algorithm ; Mechanical engineering ; Artificial intelligence ; Machine learning ; Engineering Flame retardant materials and properties ; Covalent Organic Framework Applications ; Catalysis and Oxidation Reactions
Authors: Binbin Lin | Nima Emami | David A. Santos | Yuting Luo | Sarbajit Banerjee | Bai‐Xiang Xu
Source: npj Computational Materials (article, 2022-04-28)
Field: Materials Science — Surfaces, Coatings and Films

A deep learned nanowire segmentation model using synthetic data augmentation Abstract Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science. Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images. In the present work, synthetic images are applied, resembling the experimental images in terms of geometrical and visual features, to train the state-of-art Mask region-based convolutional neural networks to segment vanadium pentoxide nanowires, a cathode material within optical density-based images acquired using spectromicroscopy. The results demonstrate the instance segmentation power in real optical intensity-based spectromicroscopy images of complex nanowires in overlapped networks and provide reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy images, which are fundamentally different from the training dataset known to the model. The proposed methodology can be extended to any optical intensity-based images of variable particle morphology, material class, and beyond. Artificial intelligence ; Segmentation ; Computer science ; Convolutional neural network ; Pentoxide ; Image segmentation ; Pattern recognition (psychology) ; Computer vision ; Feature (linguistics) ; Nanowire ; Materials science ; Vanadium ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Mohammad Khedri | Nima Beheshtizadeh | Mohammadreza Rostami | Ali Sufali | Sima Rezvantalab | Mohammad Dahri | Reza Maleki | Hélder A. Santos | Mohammad‐Ali Shahbazi
Source: Advanced NanoBiomed Research (article, 2022-03-13)
Field: Materials Science — Biomaterials

Artificial Intelligence Deep Exploration of Influential Parameters on Physicochemical Properties of Curcumin‐Loaded Electrospun Nanofibers Artificial intelligence (AI) methods are explosively considered in the design and optimization of drug discovery and delivery systems. Herein, machine learning methods are used for optimizing the production of curcumin (CUR)‐loaded nanofibers. The required data are mined through the literature survey and two categories, including material‐ and machine‐based parameters, are detected and studied as effective parameters on the final outcomes. AI results show that high‐density polymers have a lower CUR release rate; however, with the increase in polymer density, CUR encapsulation efficiency (EE) increases in many types of polymers. The smallest diameter, highest EE, and highest drug release percentage are obtained at a molecular weight between 100 and 150 kDa and a CUR concentration of 10–15 wt%, with the polymer density in the range of 1.2–1.5 g mL −1 . Also, the optimal distance of ≈23 cm, the flow rate of 3.5–4.5 mL h −1 , and the voltage at the range of 12.5–15 kV result in the highest release rate, highest EE, and the lowest average diameter for fibers. These findings open up new roads for future design and production of drug‐loaded polymeric nanofibers with desirable properties and performances by AI methods. Nanofiber ; Curcumin ; Polymer ; Materials science ; Drug delivery ; Nanotechnology ; Chemical engineering ; Composite material ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications