jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emanuele Boattini | Frank Smallenburg | Laura Filion
Source: Physical Review Letters (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Averaging Local Structure to Predict the Dynamic Propensity in Supercooled Liquids Predicting the local dynamics of supercooled liquids based purely on local structure is a key challenge in our quest for understanding glassy materials. Recent years have seen an explosion of methods for making such a prediction, often via the application of increasingly complex machine learning techniques. The best predictions so far have involved so-called Graph Neural Networks (GNNs) whose accuracy comes at a cost of models that involve on the order of 10^{5} fit parameters. In this Letter, we propose that the key structural ingredient to the GNN method is its ability to consider not only the local structure around a central particle, but also averaged structural features centered around nearby particles. We demonstrate that this insight can be exploited to design a significantly more efficient model that provides essentially the same predictive power at a fraction of the computational complexity (approximately 1000 fit parameters), and demonstrate its success by fitting the dynamic propensity of Kob-Andersen and binary hard-sphere mixtures. We then use this to make predictions regarding the importance of radial and angular descriptors in the dynamics of both models. Supercooling ; Artificial neural network ; Computer science ; Local structure ; Statistical physics ; Machine learning ; Chemical physics ; Physics ; Thermodynamics Material Dynamics and Properties ; Thermoregulation and physiological responses ; Phase Equilibria and Thermodynamics
Authors: Yaling Yan | Zenan Shi | Huilin Li | Lifeng Li | Xiao Yang | Shuhua Li | Hong Liang | Zhiwei Qiao
Source: Chemical Engineering Journal (article, 2021-08-10)
Field: Chemistry — Inorganic Chemistry

Machine learning and in-silico screening of metal–organic frameworks for O2/N2 dynamic adsorption and separation Adsorption ; Metal-organic framework ; Separation (statistics) ; Gas separation ; Materials science ; Diffusion ; Chemistry ; Nanotechnology ; Process engineering ; Topology (electrical circuits) ; Computer science ; Thermodynamics ; Organic chemistry ; Machine learning ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Yan Zu | Huiqin Yao | Yifan Wang | Liang Yan | Zhanjun Gu | Chunying Chen | Lizeng Gao | Wenyan Yin
Source: View (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

The age of bioinspired molybdenum‐involved nanozymes: Synthesis, catalytic mechanisms, and biomedical applications Abstract Molybdenum (Mo), as a nontoxic and low‐cost transition metal, has been employed for synthesis of various Mo‐based nanomaterials with unique structures and physicochemical features to achieve various properties. Especially, bioinspired Mo‐based nanomaterials show great potential for the construction of novel nanozyme catalysts due to their variable oxidation states. Overcoming drawbacks of natural enzymes, bioinspired Mo‐based nanozymes not only provide effective catalytic sites or multivalent elements to mimic natural enzymes, but also present multiple functions for interfacing with various biomicroenvironments. Construction of vast Mo‐based nanozymes has attracted enormous interest in biomedicine. Exogenous/endogenous stimuli enable the user to tailor the catalytic activities of Mo‐based nanozymes. Additionally, tunable physicochemical properties also have a significant influence on their enzyme‐like activity. In this review, we comprehensively summarize typical synthesis strategies, catalytic mechanism, and types of enzyme‐like activity of the bioinspired Mo‐based nanozymes. We mainly highlight desired merits of bioinspired Mo‐based nanozymes related to tunable enzyme‐like activity, stability, and multifunctionality through regulating their physicochemical properties. Furthermore, we intend to discuss their biomedical applications in biosensing and detection, oncotherapy, and combating bacteria. Finally, current challenges and future perspectives of the Mo‐based nanozymes are also proposed. Nanotechnology ; Nanomaterials ; Molybdenum ; Catalysis ; Interfacing ; Biosensor ; Chemistry ; Biomedicine ; Biochemical engineering ; Materials science ; Computer science ; Bioinformatics ; Biology ; Engineering ; Biochemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Zefang Shen | Raphael A. Viscarra Rossel
Source: Scientific Reports (article, 2021-01-08)
Field: Chemistry — Analytical Chemistry

Automated spectroscopic modelling with optimised convolutional neural networks Convolutional neural networks (CNN) for spectroscopic modelling are currently tuned manually, and the effects of their hyperparameters are not analysed. These can result in sub-optimal models. Here, we propose an approach to tune one-dimensional CNN (1D-CNNs) automatically. It consists of a parametric representation of 1D-CNNs and an optimisation of hyperparameters to maximise a model's performance. We used a large European soil spectroscopic database to demonstrate our approach for estimating soil organic carbon (SOC) contents. To assess the optimisation, we compared it to random search, and to understand the effects of the hyperparameters, we calculated their importance using functional Analysis of Variance. Compared to random search, the optimisation produced better final results and showed faster convergence. The optimal model produced the most accurate estimates of SOC with [Formula: see text] (s.d.) and [Formula: see text] (s.d.). The hyperparameters associated with model training and architecture critically affected the model's performance, while those related to the spectral preprocessing had little effect. The optimisation searched through a complex hyperparameter space and returned an optimal 1D-CNN. Our approach simplified the development of 1D-CNNs for spectroscopic modelling by automatically selecting hyperparameters and preprocessing methods. Hyperparameter importance analysis shed light on the tuning process and increased the model's reliability. Convolutional neural network ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Karol Kowalski | Raymond A. Bair | Nicholas P. Bauman | Jeffery S. Boschen | Eric J. Bylaska | Jeff Daily | Wibe A. de Jong | Thom H. Dunning | Niranjan Govind | Robert J. Harrison | Murat Keçeli | Kristopher Keipert | Sriram Krishnamoorthy | Suraj Kumar | Erdal Mutlu | Bruce Palmer | Ajay Panyala | Bo Peng | Ryan M. Richard | Tjerk P. Straatsma | Peter V. Sushko | Edward F. Valeev | Marat Valiev | Hubertus J. J. van Dam | Jonathan M. Waldrop | David B. Williams‐Young | Chao Yang | Marcin Zalewski | Theresa L. Windus
Source: Chemical Reviews (review, 2021-03-31)
Field: Materials Science — Materials Chemistry

From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled. Petascale computing ; Computer science ; Field (mathematics) ; Software ; Massively parallel ; Exascale computing ; Computational science ; Data science ; Paradigm shift ; Chemistry ; Software engineering ; Supercomputer ; Parallel computing ; Programming language ; Epistemology Machine Learning in Materials Science ; Scientific Computing and Data Management ; Cloud Computing and Resource Management
Authors: Wenzhe Zhao | Le An | Shujuan Wang
Source: Polymers (article, 2021-01-18)
Field: Materials Science — Polymers and Plastics

Recyclable High-Performance Epoxy-Anhydride Resins with DMP-30 as the Catalyst of Transesterification Reactions Epoxy-anhydride resins are widely used in engineering fields due to their excellent performance. However, the insolubility and infusibility make the recycling of epoxy resins challenging. The development of degradable epoxy resins with stable covalent networks provides an efficient solution to the recycling of thermosets. In this paper, 2,4,6-tris(dimethylaminomethyl)phenol (DMP-30) is incorporated into the epoxy-glutaric anhydride (GA) system to prepare high-performance epoxy resins that can be recycled below 200 °C at ordinary pressure via ethylene glycol (EG) participated transesterification. The tertiary amine groups in DMP-30 can catalyze the curing reaction of epoxy and anhydride, as well as the transesterification between ester bonds and alcoholic hydroxyl groups. Compared with early recyclable anhydride-cured epoxy resins, the preparation and recycling of diglycidyl ether of bisphenol A (DGEBA)/GA/DMP-30 systems do not need any special catalysts such as TBD, Zn(Ac)<sub>2</sub>, etc., which are usually expensive, toxic, and have poor compatibility with other compounds. The resulting resins have glass transition temperatures and strengths similar to those of conventional epoxy resins. The influences of GA content, DMP-30 content, and temperature on the dissolution rate were studied. The decomposed epoxy oligomer (DEO) is further used as a reaction ingredient to prepare new resins. It is found that the DEO can improve the toughness of epoxy resins significantly. This work provides a simple method to prepare readily recyclable epoxy resins, which is of low-cost and easy to implement. Epoxy ; Transesterification ; Bisphenol A ; Diglycidyl ether ; Materials science ; Gelcoat ; Thermosetting polymer ; Curing (chemistry) ; Organic chemistry ; Synthetic resin ; Catalysis ; Polymer chemistry ; Chemistry ; Composite material Polymer composites and self-healing ; Carbon dioxide utilization in catalysis ; Epoxy Resin Curing Processes
Authors: Lixuan Liu | Kun Ye | Changqing Lin | Zhiyan Jia | Tianyu Xue | Anmin Nie | Yingchun Cheng | Jianyong Xiang | Congpu Mu | Bochong Wang | Fusheng Wen | Kun Zhai | Zhisheng Zhao | Yongji Gong | Zhongyuan Liu | Yongjun Tian
Source: Nature Communications (article, 2021-06-23)
Field: Materials Science — Materials Chemistry

Grain-boundary-rich polycrystalline monolayer WS2 film for attomolar-level Hg2+ sensors Emerging two-dimensional (2D) layered materials have been attracting great attention as sensing materials for next-generation high-performance biological and chemical sensors. The sensor performance of 2D materials is strongly dependent on the structural defects as indispensable active sites for analyte adsorption. However, controllable defect engineering in 2D materials is still challenging. In the present work, we propose exploitation of controllably grown polycrystalline films of 2D layered materials with high-density grain boundaries (GBs) for design of ultra-sensitive ion sensors, where abundant structural defects on GBs act as favorable active sites for ion adsorption. As a proof-of-concept, our fabricated surface plasmon resonance sensors with GB-rich polycrystalline monolayer WS<sub>2</sub> films have exhibited high selectivity and superior attomolar-level sensitivity in Hg<sup>2+</sup> detection owing to high-density GBs. This work provides a promising avenue for design of ultra-sensitive sensors based on GB-rich 2D layered materials. Crystallite ; Monolayer ; Grain boundary ; Materials science ; Adsorption ; Nanotechnology ; Surface plasmon resonance ; Analyte ; Optoelectronics ; Chemistry ; Microstructure ; Nanoparticle ; Composite material ; Physical chemistry ; Metallurgy 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Malte Wittwer | Ulrich Markel | Johannes Schiffels | Jun Okuda | Daniel F. Sauer | Ulrich Schwaneberg
Source: Nature Catalysis (article, 2021-10-20)
Field: Chemistry — Organic Chemistry

Engineering and emerging applications of artificial metalloenzymes with whole cells Directed evolution ; Scope (computer science) ; Biochemical engineering ; Nanotechnology ; Computer science ; Directed Molecular Evolution ; Arms race ; Synthetic biology ; Artificial cell ; Computational biology ; Engineering ; Biology ; Materials science ; Biochemistry ; Gene Cyclopropane Reaction Mechanisms ; Enzyme Catalysis and Immobilization ; Peptidase Inhibition and Analysis
Authors: Sebastian Ament | Maximilian Amsler | Duncan R. Sutherland | Ming-Chiang Chang | Dan Guevarra | Aine Connolly | John M. Gregoire | Michael O. Thompson | Carla P. Gomes | R. B. van Dover
Source: Science Advances (article, 2021-12-17)
Field: Materials Science — Materials Chemistry

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi<sub>2</sub>O<sub>3</sub> system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi<sub>2</sub>O<sub>3</sub> at room temperature, a critical development for electrochemical technologies. Non-equilibrium thermodynamics ; Phase (matter) ; Computer science ; Active matter ; Phase diagram ; Biological system ; Artificial intelligence ; Physics ; Thermodynamics ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Jinjin Wang | Hong Shen | Riyi Yang | Kun Xie | Chao Zhang | Liang‐Yao Chen | Kai‐Ming Ho | Cai‐Zhuang Wang | Songyou Wang
Source: Carbon (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

A deep learning interatomic potential developed for atomistic simulation of carbon materials Interatomic potential ; Graphene ; Transferability ; Materials science ; Molecular dynamics ; Carbon fibers ; Density functional theory ; Amorphous carbon ; Work (physics) ; Amorphous solid ; Stability (learning theory) ; Chemical physics ; Nanotechnology ; Computational chemistry ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Machine learning ; Composite number ; Composite material ; Crystallography Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Thermal properties of materials
Authors: Messaouda Boumaaza | Ahmed Belaadi | Mostefa Bourchak | Mohammad Jawaid | S.A. Hamid
Source: Construction and Building Materials (article, 2021-12-08)
Field: Materials Science — Polymers and Plastics

Comparative study of flexural properties prediction of Washingtonia filifera rachis biochar bio-mortar by ANN and RSM models Biochar ; Mortar ; Flexural strength ; Cement ; Pyrolysis ; Cementitious ; Materials science ; Environmentally friendly ; Pulp and paper industry ; Waste management ; Composite material ; Environmental science ; Engineering Natural Fiber Reinforced Composites ; Concrete and Cement Materials Research ; Innovations in Concrete and Construction Materials
Authors: A. Bafekry | Bohayra Mortazavi | Mehrdad Faraji | Masoud Shahrokhi | Aamir Shafique | Hamad Rahman Jappor | Chương V. Nguyen | Mitra Ghergherehchi | S. A. H. Feghhi
Source: Scientific Reports (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Ab initio prediction of semiconductivity in a novel two-dimensional Sb2X3 (X= S, Se, Te) monolayers with orthorhombic structure [Formula: see text] and [Formula: see text] are well-known layered bulk structures with weak van der Waals interactions. In this work we explore the atomic lattice, dynamical stability, electronic and optical properties of [Formula: see text], [Formula: see text] and [Formula: see text] monolayers using the density functional theory simulations. Molecular dynamics and phonon dispersion results show the desirable thermal and dynamical stability of studied nanosheets. On the basis of HSE06 and PBE/GGA functionals, we show that all the considered novel monolayers are semiconductors. Using the HSE06 functional the electronic bandgap of [Formula: see text], [Formula: see text] and [Formula: see text] monolayers are predicted to be 2.15, 1.35 and 1.37 eV, respectively. Optical simulations show that the first absorption coefficient peak for [Formula: see text], [Formula: see text] and [Formula: see text] monolayers along in-plane polarization is suitable for the absorption of the visible and IR range of light. Interestingly, optically anisotropic character along planar directions can be desirable for polarization-sensitive photodetectors. Furthermore, we systematically investigate the electrical transport properties with combined first-principles and Boltzmann transport theory calculations. At optimal doping concentration, we found the considerable larger power factor values of 2.69, 4.91, and 5.45 for hole-doped [Formula: see text], [Formula: see text], and [Formula: see text], respectively. This study highlights the bright prospect for the application of [Formula: see text], [Formula: see text] and [Formula: see text] nanosheets in novel electronic, optical and energy conversion systems. Physics ; Density functional theory ; Ab initio ; Doping ; Monolayer ; Band gap ; van der Waals force ; Condensed matter physics ; Materials science ; Quantum mechanics ; Nanotechnology ; Molecule 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: M.S. Rabbi | Tansirul Islam | G. M. Sadiqul Islam
Source: International Journal of Mechanical and Materials Engineering (article, 2021-11-10)
Field: Materials Science — Polymers and Plastics

Injection-molded natural fiber-reinforced polymer composites–a review Abstract For the last couple of decades, researchers have been trying to explore eco-friendly materials which would significantly reduce the dependency on synthetic fibers and their composites. Natural fiber-based composites possess several excellent properties. They are biodegradable, non-abrasive, low cost, and lower density, which led to the growing interest in using these materials in industrial applications. However, the properties of composite materials depend on the chemical treatment of the fiber, matrix combination, and fabrication process. This study gives a bibliographic review on bio-composites specially fabricated by the injection-molding method. Technical information of injection-molded natural fiber reinforcement-based composites, especially their type and compounding process prior to molding, are discussed. A wide variety of injection-molding machines was used by the researchers for the composite manufacturing. Injection-molded composites contain natural fiber, including hemp, jute, sisal, flax, abaca, rice husk, kenaf, bamboo, and some miscellaneous kinds of fibers, are considered in this study. Materials science ; Composite material ; SISAL ; Natural fiber ; Kenaf ; Molding (decorative) ; Compounding ; Fiber ; Composite number ; Synthetic fiber ; Textile ; Abrasive Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Additive Manufacturing and 3D Printing Technologies
Authors: J. Y. Ni | X. Y. Li | D. Amoroso | X. He | J. S. Feng | E. J. Kan | S. Picozzi | H. J. Xiang
Source: Physical Review Letters (article, 2021-12-10)
Field: Materials Science — Materials Chemistry

Giant Biquadratic Exchange in 2D Magnets and Its Role in Stabilizing Ferromagnetism of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>NiCl</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Monolayers Two-dimensional (2D) van der Waals (vdW) magnets provide an ideal platform for exploring, on the fundamental side, new microscopic mechanisms and for developing, on the technological side, ultracompact spintronic applications. So far, bilinear spin Hamiltonians have been commonly adopted to investigate the magnetic properties of 2D magnets, neglecting higher order magnetic interactions. However, we here provide quantitative evidence of giant biquadratic exchange interactions in monolayer NiX_{2} (X=Cl, Br and I), by combining first-principles calculations and the newly developed machine learning method for constructing Hamiltonian. Interestingly, we show that the ferromagnetic ground state within NiCl_{2} single layers cannot be explained by means of the bilinear Heisenberg Hamiltonian; rather, the nearest-neighbor biquadratic interaction is found to be crucial. Furthermore, using a three-orbitals Hubbard model, we propose that the giant biquadratic exchange interaction originates from large hopping between unoccupied and occupied orbitals on neighboring magnetic ions. On a general framework, our work suggests biquadratic exchange interactions to be important in 2D magnets with edge-shared octahedra. Ferromagnetism ; Spintronics ; Monolayer ; van der Waals force ; Magnet ; Exchange interaction ; Antiferromagnetism 2D Materials and Applications ; Magnetism in coordination complexes ; Graphene research and applications