jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: T. Rekis | A.S. Larsen | A.Ø. Madsen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Data-driven approach for the solution of the phase problem in crystallography: first insights For a crystal structure to be solved from the diffraction data, it is necessary to obtain the complex structure factors, F H , of the measured reflections.Their squared moduli, |F H | 2 , are proportional to the measured intensities, but the phase angles to reconstruct the complete complex numbers cannot be determined experimentally.Since the exact solution to this phase problem is not known, several methods to overcome this crucial step in crystal structure determination have been developed over time.For example, direct methods or charge flipping algorithm can be used for most organic, inorganic, and metal-organic structures.Nevertheless, these methods fail if, for example, the available data resolution is not sufficiently high.Furthermore, they cannot be used to solve protein structures intrinsically having large unit cells with hundreds of atoms in the asymmetric units.In our study, we have attempted to solve the phase problem using a neural network.We focused on small organic molecule and metal-organic structures in the most common space group P2 1 /c.Millions of fictive structures containing metal atoms and/or molecular fragments were generated to train the neural network.Validation set consisted of over a thousand structures retrieved from the Cambridge Structural Database for which the structure factor amplitudes, |F H |, were generated at several resolution limits and fed into the trained network to output phases.The phases could be retrieved with a striking accuracy leading to correct structure solutions for over 99% of the validation set entries.Furthermore, the phase accuracy was also high if the resolution limit was chosen to be low, i.e. d min = 1.9 .Several dozen of experimentally measured diffraction data sets were also used for validation.Our results hint that deep learning could be used to obtain electron density maps of structures for which only a limited resolution data can be obtained and which are problematic to solve using currently available methods. Crystallography ; Phase (matter) ; Materials science ; Computer science ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Lei He | Qiuxiang Yao | Duo Ma | Yongqi Liu | Lingyang Wang | Qingqing Hao | Ma Li | Ming Sun | Xiaoxun Ma
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Coal-Oil Co-Refining Oil Cracking to Btex Over Different Morphology Mfi Zeolites：Structure-Activity Relationship Derived by Machine Learning Refining (metallurgy) ; Fluid catalytic cracking ; Cracking ; BTEX ; Oil refinery ; Petroleum engineering ; Coal ; Morphology (biology) ; Materials science ; Environmental science ; Metallurgy ; Chemical engineering ; Chemistry ; Geology ; Engineering ; Composite material ; Catalysis ; Organic chemistry ; Ethylbenzene Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Catalysis and Hydrodesulfurization Studies
Authors: Isaac Chantrenne
Source: theses.fr (ABES) (preprint, 2022-12-16)
Field: Materials Science — Materials Chemistry

Découverte de nouveaux clusters thermoélectriques assistée par machine learning The aim of this PhD is the conception of new condensed molybdenum cluster phases, which derive of Chevrel phases, with enhanced thermoelectric properties. The chosen approach combines theoretical calculations (at a DFT level) and machine learning, an artificial intelligence method that will be used to predict the properties of the new compounds, on a larger scale. The objective of the first chapter is to make the link between the crystalline and electronic structures of these condensed clusters phases, which possess an « ideal » VEC (Valence Electrons Concentration) for which they display semiconducting properties. The second chapter lists the reported thermoelectric properties of these phases. Those properties are promising for high temperature thermoelectric applications, especially because of the very low thermal conductivity of those phases. Finally, a DFT calculation process of the Seebeck coefficient is detailed in chapter 2. The third chapter begins with the creation of a database, composed of the computed Seebeck coefficient of 92 hypothetical, semiconducting formulas. Finally, a machine learning model, based on regression algorithms using decisional trees, is trained to predict the Seebeck coefficient of more than 900 hypothetical formulas. Some of those formulas, that we think may display enhanced thermoelectric properties, are proposed as candidates for synthesis. Thermoelectric effect ; Data science ; Computer science ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: T. Rekis | A.S. Larsen | A.Ø. Madsen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Data-driven approach for the solution of the phase problem in crystallography: first insights For a crystal structure to be solved from the diffraction data, it is necessary to obtain the complex structure factors, F H , of the measured reflections.Their squared moduli, |F H | 2 , are proportional to the measured intensities, but the phase angles to reconstruct the complete complex numbers cannot be determined experimentally.Since the exact solution to this phase problem is not known, several methods to overcome this crucial step in crystal structure determination have been developed over time.For example, direct methods or charge flipping algorithm can be used for most organic, inorganic, and metal-organic structures.Nevertheless, these methods fail if, for example, the available data resolution is not sufficiently high.Furthermore, they cannot be used to solve protein structures intrinsically having large unit cells with hundreds of atoms in the asymmetric units.In our study, we have attempted to solve the phase problem using a neural network.We focused on small organic molecule and metal-organic structures in the most common space group P2 1 /c.Millions of fictive structures containing metal atoms and/or molecular fragments were generated to train the neural network.Validation set consisted of over a thousand structures retrieved from the Cambridge Structural Database for which the structure factor amplitudes, |F H |, were generated at several resolution limits and fed into the trained network to output phases.The phases could be retrieved with a striking accuracy leading to correct structure solutions for over 99% of the validation set entries.Furthermore, the phase accuracy was also high if the resolution limit was chosen to be low, i.e. d min = 1.9 .Several dozen of experimentally measured diffraction data sets were also used for validation.Our results hint that deep learning could be used to obtain electron density maps of structures for which only a limited resolution data can be obtained and which are problematic to solve using currently available methods. Crystallography ; Phase (matter) ; Materials science ; Phase problem ; Computer science ; Chemistry ; Diffraction ; Physics ; Optics ; Organic chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Lara Kassab | Scott Howland | Henry Kvinge | Keerti Sahithi Kappagantula | Tegan Emerson
Source: arXiv (Cornell University) (preprint, 2022-04-01)
Field: Materials Science — Materials Chemistry

TopTemp: Parsing Precipitate Structure from Temper Topology Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties. Representation (politics) ; Parsing ; Process (computing) ; Domain (mathematical analysis) ; Work (physics) ; Microstructure ; Computer science ; Topology (electrical circuits) ; Property (philosophy) ; Development (topology) ; Artificial intelligence ; Materials science ; Mechanical engineering ; Mathematics ; Engineering ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topological and Geometric Data Analysis
Authors: Anantha Rao | S. H. Carr | Charles Snider | D. E. Feldman | Chandrasekhar Ramanathan | V. F. Mitrović
Source: arXiv (Cornell University) (preprint, 2022-12-04)
Field: Materials Science — Materials Chemistry

Machine learning assisted determination of electronic correlations from magnetic resonance In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here, we study how machine learning can extract material parameters and help interpret magnetic response experiments. A low-dimensional representation that classifies the total interaction strength is discovered by unsupervised learning. Supervised learning generates models that predict the spatial extent of electronic correlations and the total interaction strength. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials. Spins ; Representation (politics) ; Coupling (piping) ; Hyperfine structure ; Artificial intelligence ; Coupling strength ; Computer science ; Spin (aerodynamics) ; Range (aeronautics) ; Physics ; Machine learning ; Pattern recognition (psychology) ; Condensed matter physics ; Materials science ; Atomic physics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Haijun Zhu | Ting Zhang | Xiao-E He | Ailong Shi | Tian Wen | Wei-Jian Tan | Hua-Liang Xue | Zhenxie Yi | Xuehua Wang
Source: Research Square (preprint, 2022-03-10)
Field: Chemistry — Analytical Chemistry

Estimation of chlorophyll and nitrogen in double-cropping rice based on machine learning Abstract It can solve the disadvantages of traditional crop biochemical parameters measurement methods on combining hyperspectral technology with machine learning technology to retrieve crop growth index. Four nitrogen application levels were set by taking rice varieties Zhuliangyou 819 and Zhuliangyou 39 as materials. Through the rice test in 2020, the spectral data of rice leaves at different growth stages and their corresponding SPAD values and nitrogen contents were collected. Savitzky-Golay Smoothing (SG), Standard Normal Variable (SNV), Principal Component Analysis (PCA) and Multiplicative Scattering Correction (MSC) were used to preprocess spectral reflectance. Three classifiers, Support Vector Machine (SVM), Partial Least Squares Regression (PLSR) and Random Forest (RF) were used to build the model. The results showed that SG-PLSR combination had the highest accuracy in predicting chlorophyll and nitrogen in double-cropping rice. The test results provide a good reference for other indexes of rice. Mathematics ; Partial least squares regression ; Principal component analysis ; Smoothing ; Support vector machine ; Hyperspectral imaging ; Artificial intelligence ; Agronomy ; Statistics ; Computer science ; Biology Spectroscopy and Chemometric Analyses
Source: Indian Journal of Fibre & Textile Research (review, 2022-07-12)
Field: Materials Science — Polymers and Plastics

A critical review on prediction of functional &amp; performance attributes of textiles by artificial neural network Prediction of functional and performance properties of textiles before the actual commencement of fabric production and testing can serve as an effective tool in characterization and designing of fabrics for any desired application. The thermo-physiological properties of textile materials can be predicted by a variety of models, such as statistical, mechanistic and artificial neural network models. Statistical models can give good prediction performance, provided a large data set is presented to make the model and relationship exists between input parameters and response variables. The effect of input parameters on thermo-physiological properties of fabrics cannot be studied in isolation, owing to interdependence and nonlinear relationship of parameters with each other. Statistical models fail to present satisfactory analysis of relationship in such cases. Mechanistic models are useful tools in understanding the fundamentals and physics involved in heat, moisture and liquid transfer through textiles. However, the assumptions considered in the simplification of mechanistic models may not be valid in all conditions and can lead to high prediction errors in real conditions, owing to inherent variability in the textile structures. Moreover the model becomes more complicated as the number of parameters and assumptions increase, thereby limiting the model’s accuracy of prediction. Artificial neural network is a powerful and potent modelling tool which can understand any complex relationship between input and response variables and predicts the thermo-physiological properties of fabrics by considering all fabric parameters at a time. The network exhibits the ability of simulating the functioning of a biological neuron and, in turn, each network component poses analogy to the actual constituents or operations of a biological neuron. In this study, an attempt has been made to highlight the significance of artificial neural network in prediction of comfort properties of textiles. Artificial neural network ; Nonlinear system ; Biological system ; Limiting ; Component (thermodynamics) ; Textile ; Predictive modelling ; Computer science ; Set (abstract data type) ; Biochemical engineering ; Machine learning ; Artificial intelligence ; Engineering ; Mechanical engineering ; Materials science Textile materials and evaluations ; Color Science and Applications ; Dyeing and Modifying Textile Fibers
Authors: Yijun Mai | Zhixing Zheng | Lei Hou | Hao Chen
Source: Journal of Physics Conference Series (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

Study on property control of intercalated melt-blown nonwovens Abstract Melt-blown nonwovens have fine fiber diameter, excellent filtration performance, small pores, and large specific surface area. However, the fluffiness of the material is not good enough. Adding conventional fibers into the melt-blown material can increase the thickness and fluffiness of the material, and improve the sound-absorbing performance and heat preservation performance of the material. To study how much fiber can be added to make melt-blown nonwovens better, grey correlation analysis is used to explore the influence of intercalation rate on each parameter. BP neural network is used for training, with process parameters as the input index and structural variables as the output index, and the results are obtained. According to the principle of statistics, the data of the results are analyzed and the process parameters of the highest filtration are obtained. Finally, the random forest regression model is used to establish the objective programming function to predict the added constraints. Materials science ; Filtration (mathematics) ; Composite material ; Fiber ; Process (computing) ; Artificial neural network ; Process engineering ; Biological system ; Computer science ; Mathematics ; Statistics ; Artificial intelligence ; Engineering Textile materials and evaluations ; Infrastructure Maintenance and Monitoring
Authors: Kai Sasaki | Mayu Muramatsu | Kenta Hirayama | Katsuhiro Endo | Mitsuhiro Murayama
Source: Research Square (preprint, 2022-01-05)
Field: Materials Science — Surfaces, Coatings and Films

An Evaluation Framework Combining Real-Time Transmission Electron Microscopy and Integrated Machine Learning-Particle Filter Estimation Enables Detection and Quantitative Tracking of Nanoscale Defects During Plastic Deformation Processes Abstract Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis. Nanoscopic scale ; Tracking (education) ; Particle filter ; Computer science ; Artificial intelligence ; Materials science ; Projection (relational algebra) ; Deformation (meteorology) ; Particle (ecology) ; Filter (signal processing) ; Computer vision ; Biological system ; Machine learning ; Nanotechnology ; Algorithm ; Geology Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques ; Advanced Materials Characterization Techniques
Authors: Hossein Hajiabolhassan | Zahra Taheri | Ali Hojatnia | Yavar Taheri Yeganeh
Source: arXiv (Cornell University) (preprint, 2022-07-18)
Field: Materials Science — Materials Chemistry

FunQG: Molecular Representation Learning Via Quotient Graphs Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, and over-squashing. Also, GNNs usually have high computational costs because of the large-scale number of parameters. Typically, such limitations emerge or increase when facing relatively large-size graphs or using a deeper GNN model architecture. An idea to overcome these problems is to simplify a molecular graph into a small, rich, and informative one, which is more efficient and less challenging to train GNNs. To this end, we propose a novel molecular graph coarsening framework named FunQG utilizing Functional groups, as influential building blocks of a molecule to determine its properties, based on a graph-theoretic concept called Quotient Graph. By experiments, we show that the resulting informative graphs are much smaller than the molecular graphs and thus are good candidates for training GNNs. We apply the FunQG on popular molecular property prediction benchmarks and then compare the performance of some popular baseline GNNs on the obtained datasets with the performance of several state-of-the-art baselines on the original datasets. By experiments, this method significantly outperforms previous baselines on various datasets, besides its dramatic reduction in the number of parameters and low computational costs. Therefore, the FunQG can be used as a simple, cost-effective, and robust method for solving the molecular representation learning problem. Computer science ; Quotient ; Graph ; Theoretical computer science ; Molecular graph ; Smoothing ; Artificial intelligence ; Representation (politics) ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sterling G. Baird | Taylor D. Sparks
Source: Journal of Applied Crystallography (article, 2022-02-27)
Field: Materials Science — Materials Chemistry

High-throughput calculation of atomic planar density for compounds A large collection of element-wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods, where the planar density is given by the total fractional area of atoms intersecting a supercell's crystallographic plane divided by the area of the supercell's crystallographic plane. It is demonstrated that the element-wise maximum lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open-source Mathematica package hosted at https://github.com/sgbaird/LatticePlane. Supercell ; Planar ; Plane (geometry) ; Geometry ; Lattice (music) ; Molecular physics ; Crystallography ; Physics ; Computational science ; Computational physics ; Materials science ; Chemistry ; Computer science ; Mathematics ; Computer graphics (images) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions