jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaiqi Yang | Yifan Cao | Youtian Zhang | Shaoxun Fan | Ming Tang | Daniel Åberg | Babak Sadigh | Fei Zhou
Source: Patterns (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures assessed herein and are capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time-stepping efficiency and offers a useful alternative, especially when the material parameters or governing PDEs are not well determined. Computer science ; Artificial intelligence ; Convolutional neural network ; Parametric statistics ; Microstructure ; Artificial neural network ; Term (time) ; Partial differential equation ; Algorithm ; Machine learning ; Mathematics ; Materials science ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Jafar Abdi | Fahimeh Hadavimoghaddam | Masoud Hadipoor | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2021-12-28)
Field: Chemistry — Inorganic Chemistry

Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m<sup>2</sup>/g) and pore volume (cm<sup>3</sup>/g) of the MOFs and the output was CO<sub>2</sub> uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO<sub>2</sub> adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO<sub>2</sub> adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications. Gradient boosting ; Metal-organic framework ; Adsorption ; Decision tree ; Boosting (machine learning) ; Mean squared error ; Random forest ; Computer science ; Materials science ; Data mining ; Environmental science ; Machine learning ; Mathematics ; Chemistry ; Statistics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Sławomir Kozieł | Muhammad Syafiq Abdullah
Source: IEEE Transactions on Microwave Theory and Techniques (article, 2021-03-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces Popularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite potential benefits, a practical obstacle hindering widespread metasurface utilization is the lack of systematic design procedures. Conventional approaches are largely intuition-inspired and demand heavy designer's interaction while exploring the parameter space and pursuing optimum unit cell geometries. Not surprisingly, these are unable to identify truly optimum solutions. In this article, we introduce a novel machine-learning-based framework for automated and computationally efficient design of metasurfaces realizing broadband RCS reduction. Our methodology is a three-stage procedure that involves global surrogate-assisted optimization of the unit cells, followed by their local refinement. The last stage is direct EM-driven maximization of the RCS reduction bandwidth, facilitated by appropriate formulation of the objective function involving regularization terms. The appealing feature of the proposed framework is that it optimizes the RCS reduction bandwidth directly at the level of the entire metasurface as opposed to merely optimizing unit cell geometries. Computational feasibility of the optimization process, especially its last stage, is ensured by high-quality initial designs rendered during the first two stages. To corroborate the utility of our procedure, it has been applied to several metasurface designs reported in the literature, leading to the RCS reduction bandwidth improvement by 15%-25% when compared with the original designs. Furthermore, it was used to design a novel metasurface featuring over 100% of relative bandwidth. Although the procedure has been used in the context of RCS design, it can be generalized to handle metasurface development for other application areas. Computer science ; Radar cross-section ; Broadband ; Bandwidth (computing) ; Electronic engineering ; Computation ; Computer engineering ; Radar ; Algorithm ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xiangang Luo | Fei Zhang | Mingbo Pu | Yinghui Guo | Xiong Li | Xiaoliang Ma
Source: Nanophotonics (review, 2021-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances of wide‐angle metalenses: principle, design, and applications Optical imaging systems, like microscopes, cameras, and telescopes, continue to expand the scope of human observation of the world. As one of the key indicators of imaging systems, the field-of-view (FOV) is often limited by coma aberration. Expanding it generally relies on a combination of complex lenses, leading to a bulky and cumbersome system. Recently, the emergency of meta-optics provides an alternative to constructing compact and lightweight large-FOV metalens through elaborated phase modulation within a flat surface, showing great potential in surveillance, unmanned vehicles, onboard planes or satellites, medical science, and other new applications. In this article, we review recent advances of wide-angle metalenses, including operation principles, design strategies, and application demos. Firstly, basic principles of wide-angle imaging using a single metalens are interpreted. Secondly, some advanced methods for designing subwavelength structures with high angle robustness and high efficiency are discussed. Thirdly, some representative functional devices and applications are surveyed. Finally, we conclude with an outlook on future potentials and challenges that need to be overcome. Computer science ; Scope (computer science) ; Robustness (evolution) ; Optics ; Nanotechnology ; Physics ; Materials science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Coatings and Gratings
Authors: Yu Chen | Yuqin Yang | Asuka A. Orr | Pandeeswar Makam | Boris Redko | Elvira Haimov | Yannan Wang | Linda J. W. Shimon | Sigal Rencus‐Lazar | Meiting Ju | Phanourios Tamamis | Hao Dong | Ehud Gazit
Source: Angewandte Chemie International Edition (article, 2021-05-20)
Field: Materials Science — Biomaterials

Self‐Assembled Peptide Nano‐Superstructure towards Enzyme Mimicking Hydrolysis The structural arrangement of amino acid residues in native enzymes underlies their remarkable catalytic properties, thus providing a notable point of reference for designing potent yet simple biomimetic catalysts. Herein, we describe a minimalistic approach to construct a dipeptide-based nano-superstructure with enzyme-like activity. The self-assembled biocatalyst comprises one peptide as a single building block, readily synthesized from histidine. Through coordination with zinc ion, the peptide self-assembly procedure allows the formation of supramolecular β-sheet ordered nanocrystals, which can be used as basic units to further construct higher-order superstructure. As a result, remarkable hydrolysis activity and enduring stability are demonstrated. Our work exemplifies the use of a bioinspired supramolecular assembly approach to develop next-generation biocatalysts for biotechnological applications. Superstructure ; Hydrolysis ; Peptide ; Nano- ; Enzymatic hydrolysis ; Self-assembly ; Enzyme ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Biochemistry ; Engineering ; Composite material Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Advanced Nanomaterials in Catalysis
Authors: Yun Zhang | Xiaojie Xu
Source: Molecular Crystals and Liquid Crystals (article, 2021-07-05)
Field: Materials Science — Materials Chemistry

Machine learning glass transition temperature of polymethacrylates The glass transition temperature, Tg, is an important thermophysical property for polymethacrylates, which can be difficult to determine experimentally. Data-driven modeling approaches provide alternative methods to predict Tg in a rapid and robust way. Here, we develop the Gaussian process regression model to shed light on the relationship between quantum chemical descriptors and the glass transition temperature for the polymethacrylate. A total of 37 samples with the glass transition temperature ranging from 203 K to 428 K are examined. The model is highly stable and accurate that contributes to fast and low-cost estimations of the glass transition temperature. Glass transition ; Materials science ; Transition temperature ; Thermodynamics ; Gaussian ; Statistical physics ; Polymer ; Composite material ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Álvaro V. Terán-Alcocer | Francisco Bravo-Plascencia | Carlos Cevallos-Morillo | Alex Palma‐Cando
Source: Nanomaterials (review, 2021-01-19)
Field: Materials Science — Polymers and Plastics

Electrochemical Sensors Based on Conducting Polymers for the Aqueous Detection of Biologically Relevant Molecules Electrochemical sensors appear as low-cost, rapid, easy to use, and in situ devices for determination of diverse analytes in a liquid solution. In that context, conducting polymers are much-explored sensor building materials because of their semiconductivity, structural versatility, multiple synthetic pathways, and stability in environmental conditions. In this state-of-the-art review, synthetic processes, morphological characterization, and nanostructure formation are analyzed for relevant literature about electrochemical sensors based on conducting polymers for the determination of molecules that (i) have a fundamental role in the human body function regulation, and (ii) are considered as water emergent pollutants. Special focus is put on the different types of micro- and nanostructures generated for the polymer itself or the combination with different materials in a composite, and how the rough morphology of the conducting polymers based electrochemical sensors affect their limit of detection. Polypyrroles, polyanilines, and polythiophenes appear as the most recurrent conducting polymers for the construction of electrochemical sensors. These conducting polymers are usually built starting from bifunctional precursor monomers resulting in linear and branched polymer structures; however, opportunities for sensitivity enhancement in electrochemical sensors have been recently reported by using conjugated microporous polymers synthesized from multifunctional monomers. Polymer ; Materials science ; Conductive polymer ; Nanotechnology ; Microporous material ; Monomer ; Electrochemistry ; Context (archaeology) ; Conjugated microporous polymer ; Conjugated system ; Chemical engineering ; Chemistry ; Electrode ; Composite material Conducting polymers and applications ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Sneha Yadav | Ranjana Dixit | Shivani Sharma | Sriparna Dutta | Kanika Solanki | Rakesh Kumar Sharma
Source: Materials Advances (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Magnetic metal–organic framework composites: structurally advanced catalytic materials for organic transformations The review aims to present the recent developments in the synthesis and applications of magnetic MOF composite-based catalytic materials for expediting a broad array of industrially significant organic transformations. Expediting ; Catalysis ; Materials science ; Metal-organic framework ; Metal ; Composite number ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Metallurgy ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Multicomponent Synthesis of Heterocycles
Authors: Wai‐Lun Chan | Chen Xie | Wai‐Sum Lo | Jean‐Claude G. Bünzli | Wai‐Kwok Wong | Ka‐Leung Wong
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Lanthanide–tetrapyrrole complexes: synthesis, redox chemistry, photophysical properties, and photonic applications Tetrapyrrole derivatives such as porphyrins, phthalocyanines, naphthalocyanines, and porpholactones, are highly stable macrocyclic compounds that play important roles in many phenomena linked to the development of life. Their complexes with lanthanides are known for more than 60 years and present breath-taking properties such as a range of easily accessible redox states leading to photo- and electro-chromism, paramagnetism, large non-linear optical parameters, and remarkable light emission in the visible and near-infrared (NIR) ranges. They are at the centre of many applications with an increasing focus on their ability to generate singlet oxygen for photodynamic therapy coupled with bioimaging and biosensing properties. This review first describes the synthetic paths leading to lanthanide-tetrapyrrole complexes together with their structures. The initial synthetic protocols were plagued by low yields and long reaction times; they have now been replaced with much more efficient and faster routes, thanks to the stunning advances in synthetic organic chemistry, so that quite complex multinuclear edifices are presently routinely obtained. Aspects such as redox properties, sensitization of NIR-emitting lanthanide ions, and non-linear optical properties are then presented. The spectacular improvements in the quantum yield and brightness of Yb<sup>III</sup>-containing tetrapyrrole complexes achieved in the past five years are representative of the vitality of the field and open welcome opportunities for the bio-applications described in the last section. Perspectives for the field are vast and exciting as new derivatizations of the macrocycles may lead to sensitization of other Ln<sup>III</sup> NIR-emitting ions with luminescence in the NIR-II and NIR-III biological windows, while conjugation with peptides and aptamers opens the way for lanthanide-tetrapyrrole theranostics. Tetrapyrrole ; Chemistry ; Lanthanide ; Biophotonics ; Redox ; Prussian blue ; Nanotechnology ; Combinatorial chemistry ; Photonics ; Photochemistry ; Organic chemistry ; Electrochemistry ; Materials science ; Physical chemistry Porphyrin and Phthalocyanine Chemistry ; Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes
Authors: Emily Been | Wei-Sheng Lee | Harold Y. Hwang | Yi Cui | Jan Zaanen | Thomas Devereaux | Brian Moritz | Chunjing Jia
Source: Physical Review X (article, 2021-03-11)
Field: Materials Science — Materials Chemistry

Electronic Structure Trends Across the Rare-Earth Series in Superconducting Infinite-Layer Nickelates A complex interplay of electric and magnetic behaviors resides in the parent compound of a nickel oxide material known to host high-temperature superconductivity, a finding that may guide studies into this phenomenon. Electronic structure ; Superconductivity ; Series (stratigraphy) ; Nickel ; Magnetic structure ; Oxide ; Complex oxide Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism ; Magnetic and transport properties of perovskites and related materials
Authors: Emily Ford | Kailasnath Maneparambil | Subramaniam Rajan | Narayanan Neithalath
Source: Computational Materials Science (article, 2021-02-06)
Field: Materials Science — Materials Chemistry

Machine learning-based accelerated property prediction of two-phase materials using microstructural descriptors and finite element analysis Finite element method ; Property (philosophy) ; Phase (matter) ; Materials science ; Computer science ; Artificial intelligence ; Biological system ; Composite material ; Machine learning ; Structural engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Rock Mechanics and Modeling
Authors: Nathan J. Szymanski | Christopher J. Bartel | Yan Zeng | Qingsong Tu | Gerbrand Ceder
Source: Chemistry of Materials (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra Autonomous synthesis and characterization of inorganic materials require the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials. Interpretation (philosophy) ; Diffraction ; Probabilistic logic ; Phase (matter) ; Spectral line ; Artificial intelligence ; Computer science ; Materials science ; Algorithm ; Optics ; Chemistry ; Physics ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Tian Xie | Xiang Fu | Octavian-Eugen Ganea | Regina Barzilay | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Crystal Diffusion Variational Autoencoder for Periodic Material Generation Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms: 1) the coordinates must lie in the local energy minimum defined by quantum mechanics, and 2) global stability also requires the structure to follow the complex, yet specific bonding preferences between different atom types. Existing methods fail to incorporate these factors and often lack proper invariances. We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder generates materials in a diffusion process that moves atomic coordinates towards a lower energy state and updates atom types to satisfy bonding preferences between neighbors. Our model also explicitly encodes interactions across periodic boundaries and respects permutation, translation, rotation, and periodic invariances. We significantly outperform past methods in three tasks: 1) reconstructing the input structure, 2) generating valid, diverse, and realistic materials, and 3) generating materials that optimize a specific property. We also provide several standard datasets and evaluation metrics for the broader machine learning community. Stability (learning theory) ; Autoencoder ; Crystal structure prediction ; Rotation (mathematics) ; Computer science ; Property (philosophy) ; Periodic boundary conditions ; Crystal (programming language) ; Statistical physics ; Subspace topology ; Diffusion ; Translation (biology) ; Algorithm ; Artificial intelligence ; Mathematics ; Crystal structure ; Boundary value problem ; Machine learning ; Physics ; Deep learning ; Mathematical analysis ; Quantum mechanics ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Fábio Luiz Paranhos Costa | Ana Carolina Ferreira de Albuquerque | Rodolfo G. Fiorot | Luciano M. Lião | Lucas H. Martorano | Gunar Vingre da Silva Mota | Alessandra L. Valverde | José Walkimar de M. Carneiro | Fernando Martins dos Santos
Source: Organic Chemistry Frontiers (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Structural characterisation of natural products by means of quantum chemical calculations of NMR parameters: new insights In this review, we focus in all aspects of NMR simulation of natural products, from the fundamentals to the new computational toolboxes available, combining advanced quantum chemical calculations with upstream data processing and machine learning. Quantum chemical ; Chemistry ; Upstream (networking) ; Natural (archaeology) ; Focus (optics) ; Biochemical engineering ; Computational chemistry ; Organic chemistry ; Computer science ; Molecule ; Engineering ; Physics ; Optics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods