jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yixuan Sun | Surya Mitra Ayalasomayajula | Abhas Deva | Guang Lin | R. Edwin Garcı́a
Source: Scientific Reports (article, 2022-08-04)
Field: Materials Science — Materials Chemistry

Artificial intelligence inferred microstructural properties from voltage–capacity curves The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn[Formula: see text]O[Formula: see text] chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman's exponent and shape factor with 0.97 [Formula: see text] score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries. Computer science ; Materials science Machine Learning in Materials Science ; Fault Detection and Control Systems ; Fuel Cells and Related Materials
Authors: Dmitry S. Polyanichenko | А. В. Чернов | Oleg O. Kartashov | A. A. Alexandrov | Vera V. Butova | Maria A. Butakova
Source:  (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Intelligent Detection of the Nanomaterials Spatial Structure with Synthetic Electron Microscopy Images One of the priority tasks in the process of chemical synthesis is study of nanoparticles morphological features. The key characteristics of materials at the molecular level are the size and geometric shape of the structure of the nanomaterials studied. One of the most effective methods for characterizing the morphology of nanoparticles is the transmission electron microscopy method. The main problem of applying this approach under the conditions of streaming synthesis of nanomaterials is the laboriousness and routine processing of electron microscopy images by a researcher. The creation of methods for automating the determination of the shape and size of nanoparticles will reduce the level of time and resource costs for diagnostics based on the results of the chemical synthesis of nanomaterials. This fact will positively affect the efficiency of scientific research in the field of synthesis and diagnostics of new functional nanomaterials. This paper proposes an accelerated method for generating synthetic images of transmission electron microscopy as a basis for creating software tools to accelerate the process of diagnosing and characterizing metal-organic frameworks in the process of chemical synthesis based on the use of deep learning technologies. The approach of automatic generation of the spatial structure of synthesized materials at the nanolevel is considered, which makes it possible to simulate a wide range of possible outcomes during laboratory synthesis. Procedures are proposed for generating sets of synthetic transmission electron microscopy images with support for automatic segmentation and extraction of areas of interest for machine and deep learning applications. The data sets obtained were tested and evaluated to solve the designated problem using the Yolo v5 object detection deep learning algorithm. Nanomaterials ; Computer science ; Nanotechnology ; Process (computing) ; Transmission electron microscopy ; Artificial intelligence ; Nanoparticle ; Materials science ; Segmentation Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Şerif Rüzgar | Emrullah Acar
Source: Indian Journal of Physics (article, 2022-02-13)
Field: Materials Science — Materials Chemistry

The statistical neural network-based regression approach for prediction of optical band gap of CuO Band gap ; Semiconductor ; Materials science ; Artificial neural network ; Doping ; Computer science ; Optoelectronics ; Machine learning Copper-based nanomaterials and applications ; ZnO doping and properties ; Chalcogenide Semiconductor Thin Films
Authors: Davoud Hejazi | Neda Kari Rezapour | John Ferrier | Sarah Ostadabbas | Swastik Kar
Source: Materials Today (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Dispersion-free highly accurate color recognition using excitonic 2D materials and machine learning Artificial intelligence ; Computer science ; Dispersion (optics) ; Spectral color ; Colored ; Optics ; Primary color ; Computer vision ; Color space ; Color model ; Materials science ; Physics ; Image (mathematics) 2D Materials and Applications ; Perovskite Materials and Applications ; Coral and Marine Ecosystems Studies
Authors: Iryna Didych | Oleh Yasniy | Sergiy Fedak | Yuri Lapusta
Source: Procedia Structural Integrity (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of jump-like creep using preliminary plastic strain There was studied a jump-like creep of AMg6 aluminum alloy on its preliminary plastic strain during the tensile test in the soft mode of loading. Experimental studies showed that the creep of AMg6 aluminum alloy is accompanied by jump-like increments of strain, the values of which are similar to the "steps" of the tensile strain. The methods of experimental studies are often complicated and time-consuming. Therefore, it is more efficient to predict the magnitude of the jump during the creep by methods of machine learning, in particular, neural networks, which show the best results. It was determined that in this case the error of neural networks method was equal to 4.8%. Creep ; Jump ; Materials science ; Ultimate tensile strength ; Alloy ; Strain (injury) ; Composite material ; Artificial neural network ; Tensile testing ; Aluminium ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Aerospace, Electronics, Mathematical Modeling
Authors: Ziduo Yang | Weihe Zhong | Qiujie Lv | Calvin Yu‐Chian Chen
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Multitask deep learning with dynamic task balancing for quantum mechanical properties prediction Predicting quantum mechanical properties (QMPs) is very important for the innovation of material and chemistry science. Multitask deep learning models have been widely used in QMPs prediction. However, existing multitask learning models often train multiple QMPs prediction tasks simultaneously without considering the internal relationships and differences between tasks, which may cause the model to overfit easy tasks. In this study, we first proposed a multiscale dynamic attention graph neural network (MDGNN) for molecular representation learning. The MDGNN was designed in a multitask learning fashion that can solve multiple learning tasks at the same time. We then introduced a dynamic task balancing (DTB) strategy combining task differences and difficulties to reduce overfitting across multiple tasks. Finally, we adopted gradient-weighted class activation mapping (Grad-CAM) to analyze a deep learning model for frontier molecular orbital, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy level predictions. We evaluated our approach using two large QMPs datasets and compared the proposed method to the state-of-the-art multitask learning models. The MDGNN outperforms other multitask learning approaches on two datasets. The DTB strategy can further improve the performance of MDGNN significantly. Moreover, we show that Grad-CAM creates explanations that are consistent with the molecular orbitals theory. These advantages demonstrate that the proposed method improves the generalization and interpretation capability of QMPs prediction modeling. Overfitting ; Computer science ; Multi-task learning ; Artificial intelligence ; Machine learning ; Deep learning ; Generalization ; Task (project management) ; Graph ; MNIST database ; Representation (politics) ; Artificial neural network ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Fiorella Cravero | Santiago A. Schustik | María Jimena Martínez | Mónica F. Díaz | Ignacio Ponzoni
Source: Science and Technology of Advanced Materials Methods (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

How can polydispersity information be integrated in the QSPR modeling of mechanical properties? Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. In particular, machine learning methods are useful to infer QSPR (Quantitative Structure Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affect the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers. Quantitative structure–activity relationship ; Dispersity ; Representation (politics) ; Molecular descriptor ; Computer science ; Polymer ; Biological system ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: W. Sitek | J. Trzaska | Wendimu Fanta Gemechu
Source: Archives of Foundry Engineering (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Modelling and Analysis of the Synergistic Alloying Elements Effect on Hardenability of Steel The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models.Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods.Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve.The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions.It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application. Hardenability ; Materials science ; Metallurgy ; Alloy Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Material Properties and Applications
Authors: Changming Ke | Zijing Lin | Shi Liu
Source: ACS Catalysis (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Three-Dimensional Activity Volcano Plot under an External Electric Field An external electric field (EEF) can impact a broad range of catalytic processes beyond redox systems. Computational design of catalysts under EEFs targeting specific operation conditions essentially requires accurate predictions of the response of a complex physicochemical system to collective parameters such as EEF strength/direction and temperature. Here, we develop a multiscale approach that progressively bridges finite-field density functional theory, chemical reaction network theory, microkinetic modeling, and machine learning-assisted high-throughput computations, which leads to the construction of a three-dimensional activity volcano plot under EEFs for thousands of metallic alloys. Taking steam reforming of methanol as an example, we discover a nontrivial collective effect of EEF and temperature on the conversion of methanol: a positive EEF can increase the conversion at high temperatures but strongly suppress the conversion at low temperatures, highlighting the necessity of multiscale modeling for catalyst design under EEFs. Catalysis ; Field (mathematics) ; Methanol ; Electric field ; Density functional theory ; Thermal ; Computation ; Materials science ; Chemistry ; Nanotechnology ; Physics ; Computer science ; Computational chemistry ; Thermodynamics ; Quantum mechanics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jian-Gang Kong | Qingxu Li | Jian Li | Yu Liu | Jia-Ji Zhu
Source: Chinese Physics Letters (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Self-Supervised Graph Neural Networks for Accurate Prediction of Néel Temperature Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. On the other hand, an accurate and efficient theoretical method is highly demanded for determining critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNNs) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNNs on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us to obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties. Antiferromagnetism ; Graph ; Computer science ; Artificial neural network ; Artificial intelligence ; Magnetism ; Convolutional neural network ; Machine learning ; Quantum ; Pattern recognition (psychology) ; Theoretical computer science ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Takashi Kojima | Takashi Washio | Satoshi Hara | Masataka KOISHI
Source: Computational Materials Science (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Search strategy for rare microstructure to optimize material properties of filled rubber using machine learning based simulation A shortcut to understand microstructure-property relationship, such as relationship between filler morphology and its modulus, is sampling and analysis of microstructures that induce the desired material property. However, the morphologies that induces the desired property, e.g. extremely high modulus, are very limited and hard to be searched by simple random sampling. Particularly, in the case of filled rubber, the simulation of complex filler morphology involves hundreds of filler particles. This makes the random sampling infeasible, because the number of parameters is O3n when using coordinates of the n particles as the search objective. Recent advancement of the sampling methods reported as a part of the materials informatics remains efficient sampling of the targeted microstructure characterized by 10 parameters at most. In this paper, we propose a novel and effective three-step search method to efficiently sample the filler morphology inducing extremely high modulus in a several hundred dimensional parameter space. We demonstrated the efficiency of the proposed method through the comparison with the random sampling in 750 dimensional parameter space for obtaining the morphologies providing the extremely high modulus. Half of the sampled morphologies provided higher stresses than the top 0.1% morphologies found by the random sampling. This is the first work that developed the efficient sampling method of complex material microstructures having targeted properties in a very high dimensional parameter space. Microstructure ; Sampling (signal processing) ; Modulus ; Natural rubber ; Materials science ; Composite material ; Simple random sample ; Biological system ; Computer science ; Population Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Liangyong Wang | Yaolong Zhu
Source: Processes (article, 2022-11-12)
Field: Materials Science — Materials Chemistry

Neural-Network-Based Nonlinear Model Predictive Control of Multiscale Crystallization Process The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization process, and the mathematical statistical method is adopted to obtain the mean size of the crystal population. A feedforward neural network is subsequently trained and employed in a nonlinear model predictive control formulation to obtain the optimal profile of the manipulated variable. The effectiveness of the proposed nonlinear model predictive control method is evaluated using alum cooling crystallization experiments. Experimental results demonstrate benefits of the proposed combination of feedforward neural network and nonlinear model predictive control method for the multiscale crystallization process. Model predictive control ; Artificial neural network ; Nonlinear system ; Crystallization ; Feedforward neural network ; Feed forward ; Computer science ; Process (computing) ; Artificial intelligence ; Control theory (sociology) ; Control (management) ; Control engineering ; Engineering Crystallization and Solubility Studies ; Advanced Control Systems Optimization
Authors: Eike Caldeweyher | Masha Elkin | Golsa Gheibi | Magnus J. Johansson | Christian Sköld | Per‐Ola Norrby | John F. Hartwig
Source: ChemRxiv (preprint, 2022-11-03)
Field: Chemistry — Inorganic Chemistry

A Hybrid Machine-Learning Approach to Predict the Iridium-Catalyzed Borylation of C–H Bonds The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective functionalization of C–H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the aryl C–H bonds that are most acidic and least sterically hindered, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semi-empirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp2)–H bonds during pharmaceutical development. Borylation ; Steric effects ; Chemistry ; Iridium ; Aryl ; Density functional theory ; Combinatorial chemistry ; Cheminformatics ; Molecule ; Catalysis ; Computational chemistry ; Stereochemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry ; Catalytic Cross-Coupling Reactions
Authors: Lai Wei | Qinyang Li | Yuqi Song | Stanislav Stefanov | Edirisuriya M. Dilanga Siriwardane | Fanglin Chen | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials. Our model is built on the blank filling language model for text generation and has demonstrated unique advantages in learning the "materials grammars" together with high-quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7\% charge neutrality and 84.8\% balanced electronegativity, which are more than 4 and 8 times higher compared to a pseudo random sampling baseline. The probabilistic generation process of BLMM allows it to recommend tinkering operations based on learned materials chemistry and makes it useful for materials doping. Combined with the TCSP crysal structure prediction algorithm, We have applied our model to discover a set of new materials as validated using DFT calculations. Our work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user-friendly web app has been developed for computational materials doping and can be accessed freely at \url{www.materialsatlas.org/blmtinker}. Computer science ; Artificial intelligence ; Generative model ; Transformer ; Language model ; Generative grammar ; Artificial neural network ; Interpretability ; Probabilistic logic ; Machine learning ; Natural language processing ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography