jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huta R. Banjade | Sandro Hauri | Shanshan Zhang | Francesco Ricci | Weiyi Gong | Geoffroy Hautier | Slobodan Vucetic | Qimin Yan
Source: Science Advances (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Structure motif–centric learning framework for inorganic crystalline systems Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling's rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles. Artificial neural network ; Graph ; Motif (music) ; Architecture ; Network structure ; Deep learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks
Authors: A.K. Singh | Partha Kumbhakar | Aravind Krishnamoorthy | Aiichiro Nakano | Kishor Kumar Sadasivuni | Priya Vashishta | Ajit K. Roy | Vidya Kochat | Chandra Sekhar Tiwary
Source: iScience (review, 2021-11-29)
Field: Materials Science — Materials Chemistry

Review of strategies toward the development of alloy two-dimensional (2D) transition metal dichalcogenides Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted significant attention owing to their prosperity in material research. The inimitable features of TMDCs triggered the emerging applications in diverse areas. In this review, we focus on the tailored and engineering of the crystal lattice of TMDCs that finally enhance the efficiency of the material properties. We highlight several preparation techniques and recent advancements in compositional engineering of TMDCs structure. We summarize different approaches for TMDCs such as doping and alloying with different materials, alloying with other 2D metals, and scrutinize the technological potential of these methods. Beyond that, we also highlight the recent significant advancement in preparing 2D quasicrystals and alloying the 2D TMDCs with MAX phases. Finally, we highlight the future perspectives for crystal engineering in TMDC materials for structure stability, machine learning concept marge with materials, and their emerging applications. Materials science ; Nanotechnology ; Transition metal ; Prosperity ; Alloy ; Doping ; Engineering physics ; Metallurgy ; Engineering ; Political science ; Chemistry ; Optoelectronics ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Pan Liu | Yan Song | Mengyu Chai | Zelin Han | Yu Zhang
Source: Materials (article, 2021-12-07)
Field: Materials Science — Metals and Alloys

Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface The precise identification of micro-features on 2.25Cr1Mo0.25V steel is of great significance for understanding the mechanism of hydrogen embrittlement (HE) and evaluating the alloy's properties of HE resistance. Presently, the convolution neural network (CNN) of deep learning is widely applied in the micro-features identification of alloy. However, with the development of the transformer in image recognition, the transformer-based neural network performs better on the learning of global and long-range semantic information than CNN and achieves higher prediction accuracy. In this work, a new transformer-based neural network model Swin-UNet++ was proposed. Specifically, the architecture of the decoder was redesigned to more precisely detect and identify the micro-feature with complex morphology (i.e., dimples) of 2.25Cr1Mo0.25V steel fracture surface. Swin-UNet++ and other segmentation models performed state-of-the-art (SOTA) were compared on the dimple dataset constructed in this work, which consists of 830 dimple scanning electron microscopy (SEM) images on 2.25Cr1Mo0.25V steel fracture surface. The segmentation results show Swin-UNet++ not only realizes the accurate identification of dimples but displays a much higher prediction accuracy and stronger robustness than Swin-Unet and UNet. Moreover, efforts from this work will also provide an important reference value to the identification of other micro-features with complex morphologies. Dimple ; Segmentation ; Artificial intelligence ; Robustness (evolution) ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network ; Deep learning ; Materials science ; Artificial neural network ; Composite material ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Pei‐Lin Kang | Cheng Shang | Zhi‐Pan Liu
Source: Chinese Journal of Chemical Physics (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Recent implementations in LASP 3.0: Global neural network potential with multiple elements and better long-range description LASP (large-scale atomistic simulation with neural network potential) software developed by our group since 2018 is a powerful platform (www.lasphub.com) for performing atomic simulation of complex materials. The software integrates the neural network (NN) potential technique with the global potential energy surface exploration method, and thus can be utilized widely for structure prediction and reaction mechanism exploration. Here we introduce our recent update on the LASP program version 3.0, focusing on the new functionalities including the advanced neural network training based on the multi-network framework, the newly-introduced S7 and S8 power type structure descriptor (PTSD). These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multiple-element systems. Taking Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example, we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input. The obtained double-network potential CuCHO is robust in simulation and the introduction of S7 and S8 PTSDs can reduce the root-mean-square errors of energy by a factor of two. Artificial neural network ; Computer science ; Software ; Implementation ; Network simulation ; Artificial intelligence ; Distributed computing ; Software engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Yiming Ma | Mengmeng Sun | Yanbo Liu | Mingyang Chen | Songgu Wu | Mengwei Wang | Lingyu Wang | Zhenguo Gao | Dandan Han | Lande Liu | Jingkang Wang | Junbo Gong
Source: ACS Sustainable Chemistry & Engineering (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Design of Spherical Crystallization of Active Pharmaceutical Ingredients via a Highly Efficient Strategy: From Screening to Preparation This work aims to develop a highly efficient spherical crystallization from screening to preparation stage based on liquid–liquid phase separation (LLPS). Mixtures than can undergo an LLPS split into two liquid phases with different physical properties, and the oil droplets formed during that process make LLPS a promising approach to prepare spherical particles of an active pharmaceutical ingredient (API). In the screening stage, three machine learning (ML) models (artificial neural network, support vector machine, and logistic regression) were established for predicting LLPS for an API. Two linear models, a simple linear model and a machine learning-based linear model, were also constructed to produce further optimization. The ML-based prediction of LLPS was first established in this work and showed high accuracy and reliability. Also, when compared to a method where the screening depended on the results of experiments, the prediction model highly reduced the use of chemical substances and saved labor and time. In the preparation stage, water and ethanol, which have low toxicity to mammals and have environmental advantages over other organic solvents, were applied as the solvents of LLPS-based spherical crystallization. The LLPS-based preparation process of spherical particles possesses advantages in terms of reduction of the number of unit operations as well as energy consumption and processing cost. Crystallization ; Active ingredient ; Work (physics) ; Cleaning agent ; Process engineering ; Computer science ; Process (computing) ; Materials science ; Unit operation ; Biological system ; Chemical engineering ; Chemistry ; Thermodynamics ; Physics ; Engineering ; Organic chemistry Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography
Authors: Giuseppe Emanuele Lio | Antonio Ferraro
Source: Photonics (article, 2021-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

LIDAR and Beam Steering Tailored by Neuromorphic Metasurfaces Dipped in a Tunable Surrounding Medium The control of amplitude, losses and deflection of light with elements of an optical array is of paramount importance for realizing dynamic beam steering for light detection and ranging applications (LIDAR). In this paper, we propose an optical beam steering device, operating at a wavelength of 1550 nm, based on high index material as molybdenum disulfide (MoS2) where the direction of the light is actively controlled by means of liquid crystal. The metasurface have been designed by a deep machine learning algorithm jointed with an optimizer in order to obtain univocal optical responses. The achieved numerical results represent a promising way for the realization of novel LIDAR for future applications with increase control and precision. Beam steering ; Lidar ; Optics ; Neuromorphic engineering ; Materials science ; Deflection (physics) ; Ranging ; Computer science ; Beam (structure) ; Realization (probability) ; Optoelectronics ; Phased-array optics ; Wavelength ; Physics ; Telecommunications ; Artificial intelligence ; Artificial neural network ; Phased array Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Optical Imaging Technologies
Authors: Masaya Nakajima | Tetsuhiro Nemoto
Source: Scientific Reports (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES Machine learning to create models on the basis of big data enables predictions from new input data. Many tasks formerly performed by humans can now be achieved by machine learning algorithms in various fields, including scientific areas. Hypervalent iodine compounds (HVIs) have long been applied as useful reactive molecules. The bond dissociation enthalpy (BDE) value is an important indicator of reactivity and stability. Experimentally measuring the BDE value of HVIs is difficult, however, and the value has been estimated by quantum calculations, especially density functional theory (DFT) calculations. Although DFT calculations can access the BDE value with high accuracy, the process is highly time-consuming. Thus, we aimed to reduce the time for predicting the BDE by applying machine learning. We calculated the BDE of more than 1000 HVIs using DFT calculations, and performed machine learning. Converting SMILES strings to Avalon fingerprints and learning using a traditional Elastic Net made it possible to predict the BDE value with high accuracy. Furthermore, an applicability domain search revealed that the learning model could accurately predict the BDE even for uncovered inputs that were not completely included in the training data. Hypervalent molecule ; Computer science ; Machine learning ; Density functional theory ; Dissociation (chemistry) ; Artificial intelligence ; Enthalpy ; Bond-dissociation energy ; Computational chemistry ; Statistical physics ; Algorithm ; Chemistry ; Iodine ; Thermodynamics ; Physical chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Pavlo Golub | Andrej Antalík | Libor Veis | Jiří Brabec
Source: Journal of Chemical Theory and Computation (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)-porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies. Chemical space ; Computer science ; Atomic orbital ; Density matrix renormalization group ; Betweenness centrality ; Physics ; Renormalization group ; Chemistry ; Quantum mechanics ; Mathematics ; Centrality Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Surface Chemistry and Catalysis
Authors: Mohd Zaki | Jayadeva | N. M. Anoop Krishnan
Source: Chemical Engineering and Processing - Process Intensification (article, 2021-08-27)
Field: Materials Science — Ceramics and Composites

Extracting processing and testing parameters from materials science literature for improved property prediction of glasses Computer science ; Property (philosophy) ; Process (computing) ; Inverse ; Heuristic ; Machine learning ; Artificial intelligence ; Mathematics Glass properties and applications ; Machine Learning in Materials Science
Authors: Vidhyadhar Manee | Roberto Baratti | José A. Romagnoli
Source: Process Safety and Environmental Protection (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Learning to navigate a crystallization model with Deep Reinforcement Learning Reinforcement learning ; Computer science ; Controller (irrigation) ; Convolutional neural network ; Process (computing) ; Crystallization ; Artificial intelligence ; Face (sociological concept) ; Control theory (sociology) ; Control (management) ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Neural Networks and Applications
Authors: Songyu Li | Yang Ma | Nabonswendé Aïda Nadège Ouedraogo | Fa-Min Liu | Congya You | Wenjie Deng | Yongzhe Zhang
Source: Nano Research (article, 2021-07-24)
Field: Materials Science — Materials Chemistry

p-/n-Type modulation of 2D transition metal dichalcogenides for electronic and optoelectronic devices Two-dimensional layered transition metal dichalcogenides (TMDCs) have demonstrated a huge potential in the broad fields of optoelectronic devices, logic electronics, electronic integration, as well as neural networks. To take full advantage of TMDC characteristics and efficiently design the device structures, one of the most key processes is to control their p-/n-type modulation. In this review, we summarize the p-/n-type modulation of TMDCs based on diverse strategies consisting of intrinsic defect tailoring, substitutional doping, surface charge transfer, chemical intercalation, electrostatic modulation, and dielectric interface engineering. The modulation mechanisms and comparisons of these strategies are analyzed together with a discussion of their corresponding device applications in electronics and optoelectronics. Finally, challenges and outlooks for p-/n-type modulation of TMDCs are presented to provide references for future studies. Modulation (music) ; Electronics ; Materials science ; Transition metal ; Optoelectronics ; Doping ; Nanotechnology ; Key (lock) ; Dielectric ; Engineering physics ; Electrical engineering ; Computer science ; Physics ; Chemistry ; Engineering 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing
Authors: Zeus A. De los Santos | Zhiwei Lin | Ming Zheng
Source: Journal of the American Chemical Society (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Optical Detection of Stereoselective Interactions with DNA-Wrapped Single-Wall Carbon Nanotubes DNA-wrapped carbon nanotubes have been explored increasingly as sensitive near-infrared fluorescence probes for biomolecules. However, notably missing in previous studies is an inquiry on stereoselective interactions between DNA-wrapped carbon nanotubes and biomolecules. Here, enantiopure (+) and (-)(6,5), and (-)(8,3) as well as achiral (11,0) carbon nanotubes wrapped with specific resolving DNA sequences are used to demonstrate their stereoselective detection of amino acid enantiomers. Furthermore, stereoselective sensing abilities are found to be retained by dispersions containing a multitude of chiral nanotube structures. The fluorescence response profiles of six different DNA-wrapped carbon nanotube dispersions to nine standard amino acids, and their enantiomers, demonstrate that DNA-wrapped carbon nanotubes are exquisitely sensitive to the stereoconfiguration and side-chain functionality of amino acids in a manner that is dependent on both DNA sequence and nanotube chirality. Implications of our findings are discussed in the context of developing a machine learning-aided multiplexed biosensing scheme called a molecular perceptron. Carbon nanotube ; Chemistry ; Chirality (physics) ; Enantiomer ; Biomolecule ; Stereoselectivity ; DNA ; Context (archaeology) ; Enantiopure drug ; Asymmetric carbon ; Fluorescence ; Nanotechnology ; Enantioselective synthesis ; Stereochemistry ; Organic chemistry ; Materials science ; Biochemistry ; Alkyl ; Physics Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies ; Mechanical and Optical Resonators
Authors: Mohammed Al‐Yaari | Ibrahim Dubdub
Source: Polymers (article, 2021-12-13)
Field: Materials Science — Materials Chemistry

Pyrolytic Behavior of Polyvinyl Chloride: Kinetics, Mechanisms, Thermodynamics, and Artificial Neural Network Application Pyrolysis of waste polyvinyl chloride (PVC) is considered a promising and highly efficient treatment method. This work aims to investigate the kinetics, and thermodynamics of the process of PVC pyrolysis. Thermogravimetry of PVC pyrolysis at three heating rates (5, 10, and 20 K/min) showed two reaction stages covering the temperature ranges of 490-675 K, and 675-825 K, respectively. Three integral isoconversional models, namely Flynn-Wall-Qzawa (FWO), Kissinger-Akahira-Sunose (KAS), and Starink, were used to obtain the activation energy (<i>E<sub>a</sub></i>), and pre-exponential factor (<i>A</i>) of the PVC pyrolysis. On the other hand, the Coats-Redfern non-isoconversional model was used to determine the most appropriate solid-state reaction mechanism/s for both stages. Values of <i>E<sub>a</sub></i>, and <i>A</i>, obtained by the isoconversional models, were very close and the average values were, for stage I: <i>E<sub>a</sub></i> = 75 kJ/mol, <i>A</i> = 1.81 × 10<sup>6</sup> min<sup>-1</sup>; for stage II: <i>E<sub>a</sub></i> = 140 kJ/mol, <i>A</i> = 4.84 × 10<sup>9</sup> min<sup>-1</sup>. In addition, while the recommended mechanism of the first stage reaction was P2, F3 was the most suitable mechanism for the reaction of stage II. The appropriateness of the mechanisms was confirmed by the compensation effect. Thermodynamic study of the process of PVC pyrolysis confirmed that both reactions are endothermic and nonspontaneous with promising production of bioenergy. Furthermore, a highly efficient artificial neural network (ANN) model has been developed to predict the weight left % during the PVC pyrolysis as a function of the temperature and heating rate. The 2-10-10-1 topology with TANSIG-LOGSIG transfer function and feed-forward back-propagation characteristics was used. Pyrolysis ; Activation energy ; Polyvinyl chloride ; Thermogravimetry ; Materials science ; Kinetics ; Thermodynamics ; Endothermic process ; Pyrolytic carbon ; Chemical engineering ; Physical chemistry ; Chemistry ; Organic chemistry ; Composite material ; Adsorption Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Polymer Science and PVC