jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marcin Maździarz
Source: Journal of Applied Physics (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Transferability of interatomic potentials for germanene (2D germanium) The capacities of various interatomic potentials available for elemental germanium, with the scope to choose the potential suitable for the modeling of germanene (2D germanium) allotropes,f were investigated. Structural and mechanical properties of the flat, low-buckled, trigonal dumbbell, and large honeycomb dumbbell single-layer germanium (germanene) phases, were obtained using the density functional theory and molecular statics computations with Tersoff, modified embedded atom method, Stillinger–Weber, environment-dependent interatomic potential, ReaxFF, and machine-learning-based interatomic potentials. A systematic quantitative comparative study and discussion of the findings are given. Germanene ; Germanium ; Interatomic potential ; Materials science ; Density functional theory ; Atom (system on chip) ; Silicene ; Dumbbell ; Chemical physics ; Chemistry ; Molecular dynamics ; Computational chemistry ; Molecular physics ; Condensed matter physics ; Nanotechnology ; Silicon ; Graphene ; Physics ; Metallurgy ; Computer science Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: C. Moussa | Hao Wang | Mauricio Araya‐Polo | Thomas Bäck | Vedran Dunjko
Source: arXiv (Cornell University) (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Application of quantum-inspired generative models to small molecular datasets Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions point toward generative modeling as a promising direction to realize the first examples of real-world quantum advantages from these technologies. A few empirical studies also demonstrate such potential, especially when considering quantum-inspired models based on tensor networks. In this work, we apply tensor-network-based generative models to the problem of molecular discovery. In our approach, we utilize two small molecular datasets: a subset of $4989$ molecules from the QM9 dataset and a small in-house dataset of $516$ validated antioxidants from TotalEnergies. We compare several tensor network models against a generative adversarial network using different sample-based metrics, which reflect their learning performances on each task, and multiobjective performances using $3$ relevant molecular metrics per task. We also combined the output of the models and demonstrate empirically that such a combination can be beneficial, advocating for the unification of classical and quantum(-inspired) generative learning. Generative grammar ; Computer science ; Quantum ; Artificial intelligence ; Field (mathematics) ; Task (project management) ; Tensor (intrinsic definition) ; Machine learning ; Generative model ; Unification ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Protein Structure and Dynamics
Authors: Daniel R. Cassar
Source: arXiv (Cornell University) (preprint, 2023-03-27)
Field: Materials Science — Ceramics and Composites

GlassNet: a multitask deep neural network for predicting many glass properties A multitask deep neural network model was trained on more than 218k different glass compositions. This model, called GlassNet, can predict 85 different properties (such as optical, electrical, dielectric, mechanical, and thermal properties, as well as density, viscosity/relaxation, crystallization, surface tension, and liquidus temperature) of glasses and glass-forming liquids of different chemistries (such as oxides, chalcogenides, halides, and others). The model and the data used to train it are available in the GlassPy Python module as free and open source software for the community to use and build upon. As a proof of concept, GlassNet was used with the MYEGA viscosity equation to predict the temperature dependence of viscosity and outperformed another general purpose viscosity model available in the literature (ViscNet) on unseen data. An explainable AI algorithm (SHAP) was used to extract knowledge correlating the input (physicochemical information) and output (glass properties) of the model, providing valuable insights for glass manufacturing and design. It is hoped that GlassNet, with its free and open source nature, can be used to enable faster and better computer-aided design of new technological glasses. Liquidus ; Viscosity ; Python (programming language) ; Surface tension ; Artificial neural network ; Computer science ; Spurious relationship ; Software ; Materials science ; Dielectric ; Artificial intelligence ; Thermodynamics ; Mineralogy ; Machine learning ; Chemistry ; Composite material ; Physics ; Optoelectronics Glass properties and applications ; Phase-change materials and chalcogenides ; Pigment Synthesis and Properties
Authors: Binjie Xu | Xuefeng Han | Suocheng Xu | Deren Yang | Xiaodong Pi
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Optimization of the Thermal Field of 8-Inch Sic Crystal Growth by Pvt Method with “3 Separation Heater Method” Materials science ; Thermal ; Separation (statistics) ; Field (mathematics) ; Mechanics ; Mechanical engineering ; Nuclear engineering ; Engineering ; Thermodynamics ; Computer science ; Mathematics ; Physics Solidification and crystal growth phenomena ; Metallurgy and Material Forming
Authors: See Yoong Wong | Sarah L. Harmer | Wil Gardner | Alex K. Schenk | Davide Ballabio | Paul J. Pigram
Source: Advanced Materials Interfaces (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Enhanced Visualization and Interpretation of XMCD‐PEEM Data Using SOM‐RPM Machine Learning Abstract Photoemission electron microscopy (PEEM) is a powerful technique for surface characterization that provides detailed information on the chemical and structural properties of materials at the nanoscale. In this study, the potential is explored using a machine learning algorithm called self‐organizing map with a relational perspective map (SOM‐RPM) for visualizing and analyzing complex PEEM‐generated datasets. The application of SOM‐RPM is demonstrated using synchrotron‐based X‐ray magnetic circular dichroism (XMCD)‐PEEM data acquired from a pyrrhotite sample. Traditional visualization approaches for XMCD‐PEEM data may not fully capture the complexity of the sample, especially in the case of heterogeneous materials. By applying SOM‐RPM to the XMCD‐PEEM data, a colored topographic map is created that represents the spectral similarities and dissimilarities among the pixels. This approach allows for a more intuitive and easily interpretable representation of the data without the need of data binning or spectral smoothing. The results of the SOM‐RPM analysis are compared to the conventional visualization approach, highlighting the advantages of SOM‐RPM in revealing features that are not readily observable in the conventional method. This study suggests that the SOM‐RPM approach can be used complimentarily for other PEEM‐based measurements, such as core level and valence band X‐ray photoelectron spectroscopy. Materials science ; Photoemission electron microscopy ; Visualization ; Pixel ; Magnetic circular dichroism ; Artificial intelligence ; Optics ; Computer science ; Physics ; Spectral line Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: C. Marchesi | M. Rani | S. Federici | Matteo Lancini | Laura E. Depero
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

COMPARATIVE STUDY OF CHEMOMETRIC APPROACHES AND MACHINE LEARNING FOR MINIATURIZED NEAR-INFRARED (MICRONIR) SPECTROSCOPY IN PLASTICWASTE SORTING The plastic recycling industry necessitates fast and reliable methods to recognize the different polymer types to improve the recycling capacity.In this contribution, the coupling of a miniaturized Near-Infrared (NIR) spectroscopy technique with a robust data analysis is presented.Comparison of multiple machine learning techniques, such as Support-Vector Machines (SVM), Fine Tree, Bagged Tree, and Ensemble Learning, and chemometric approaches, such as Principal Component Analysis (PCA) and Partial Least Squares -Discriminant Analysis (PLS -DA), were combined to provide a broad overview and a rational means for selecting the approach in analysing NIR data for plastic waste sorting. Principal component analysis ; Sorting ; Support vector machine ; Linear discriminant analysis ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Near-infrared spectroscopy ; Partial least squares regression ; Machine learning ; Chemometrics ; Algorithm Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Rasmus Tranås | Ole Martin Løvvik | Kristian Berland
Source: arXiv (Cornell University) (preprint, 2023-09-13)
Field: Materials Science — Materials Chemistry

Lattice Thermal Conductivity from First Principles and Active Learning with Gaussian Process Regression The lattice thermal conductivity ($κ_{\ell}$) is a key materials property in power electronics, thermal barriers, and thermoelectric devices. Identifying a wide pool of compounds with low $κ_{\ell}$ is particularly important in the development of materials with high thermoelectric efficiency. The present study contributed to this with a reliable machine learning (ML) model based on a training set consisting of 268 cubic compounds. For those, $κ_{\ell}$ was calculated from first principles using the temperature-dependent effective potential (TDEP) method based on forces and phonons calculated by density functional theory (DFT). 238 of these were preselected and used to train an initial ML model employing Gaussian process regression (GPR). The model was then improved with active learning (AL) by selecting the 30 compounds with the highest GPR uncertainty as new members of an expanded training set. This was used to predict $κ_{\ell}$ of the 1574 cubic compounds in the \textsc{Materials Project} (MP) database with a validation R2-score of 0.81 and Spearman correlation of 0.93. Out of these, 27 compounds were predicted to have very low values of $κ_{\ell}$ ($\leq 1.3$ at 300~K), which was verified by DFT calculations. Some of these have not previously been reported in the literature, suggesting further investigations of their electronic thermoelectric properties. Thermal conductivity ; Lattice (music) ; Thermoelectric effect ; Phonon ; Gaussian ; Kappa ; Kriging ; Mathematics ; Materials science ; Machine learning ; Condensed matter physics ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Computational chemistry ; Geometry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Adriana Ladera | Ravi Kashikar | S. Lisenkov | I. Ponomareva
Source: Advanced Theory and Simulations (article, 2023-01-22)
Field: Materials Science — Materials Chemistry

Machine Learning Reveals Memory of the Parent Phases in Ferroelectric Relaxors Ba(Ti1−x$_{1-x}$,Zr<sub><i>x</i></sub>)O<sub>3</sub> Abstract Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here an unsupervised machine learning workflow is developed and used within a framework of first‐principles‐based atomistic simulations to investigate phases, phase transitions, and their structural origins in ferroelectric relaxors, Ba(Ti,Zr x )O 3 . The applicability of the workflow is first demonstrated to identify phases and phase transitions in the parent compound, a prototypical ferroelectric BaTiO 3 . Then the workflow is applied for Ba(Ti,Zr x )O 3 with to reveal i) that some of the compounds bear a subtle memory of BaTiO 3 phases beyond the point of the pinched phase transition, which could contribute to their enhanced electromechanical response; ii) the existence of peculiar phases with delocalized precursors of nanodomains—likely candidates for the controversial polar nanoregions; and iii) nanodomain phases for the largest concentrations of x . Ferroelectricity ; Workflow ; Delocalized electron ; Materials science ; Phase transition ; Condensed matter physics ; Polar ; Phase (matter) ; Chemical physics ; Nanotechnology ; Crystallography ; Computer science ; Chemistry ; Physics ; Optoelectronics ; Dielectric ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Yi Wang | Xiaoxu Huang | Guoliang Xie | Nianpeng Zhang
Source: Journal of Materials Research and Technology (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

A high-precision automatic recognition method based on target detection for nanometer scaled precipitates or carbides in different alloys The recognition and statistical analysis of nanometer scaled second phase (NSP), such as precipitates or carbides in alloys is considered to be very important for optimization of mechanical properties. Statistical analysis of the size, number, and distribution of NSPs is required, and manual statistics are difficult and inefficient. In order to solve the above problems, this paper provides an object detection technology for the automatic identification of the NSPs in different alloys. A dataset is constructed by labeling the NSPs in typical transmission electron micrographs (TEM) photographs using a labelImg software. First of all, based on the YOLOv3 object detection network in deep learning, changing the network input size is compatible with the TEM image size. Secondly, using two improved schemes, a priori box K-means clustering and CBAM attention mechanism, a variety of class NSPs detection network is proposed. The statistical results show that the network developed herein has strong applicability. This network can be used for identification for the NSP in several types of alloys, i.e., Cu-Sc alloy, 4Cr5MoSiV1 and Cu-Ni-Si alloy. The accuracy of this network for the recognition of these NSP in Cu-Sc alloy (Cu4Sc precipitates), 4Cr5MoSiV1 (carbides) and Cu-Ni-Si alloy (Ni2Si precipitates) is 93.55%, 81.82% and 93.13%, respectively. Alloy ; Materials science ; Nanometre ; Carbide ; Cluster analysis ; Artificial neural network ; Phase (matter) ; Transmission electron microscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Metallurgy ; Nanotechnology ; Composite material ; Physics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Electron and X-Ray Spectroscopy Techniques
Authors: Fatin Abrar Shams | Rashed Hasan Ratul | Ahnaf Islam Naf | Syed Shaek Hossain Samir | Mirza Muntasir Nishat | Fahim Faisal | Md. Ashraful Hoque
Source:  (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation since their discovery. However, superconductors at normal temperatures have yet to be identified. Additionally, there are still many unknown factors and gaps of understanding regarding this unique phenomenon, particularly the connection between superconductivity and the fundamental criteria to estimate the critical temperature. To bridge the gap, numerous machine learning techniques have been established to estimate critical temperatures as it is extremely challenging to determine. Furthermore, the need for a sophisticated and feasible method for determining the temperature range that goes beyond the scope of the standard empirical formula appears to be strongly emphasized by various machine-learning approaches. This paper uses a stacking machine learning approach to train itself on the complex characteristics of superconductive materials in order to accurately predict critical temperatures. In comparison to other previous accessible research investigations, this model demonstrated a promising performance with an RMSE of 9.68 and an R2 score of 0.922. The findings presented here could be a viable technique to shed new insight on the efficient implementation of the stacking ensemble method with hyperparameter optimization (HPO). Superconductivity ; Computer science ; High-temperature superconductivity ; Materials science ; Condensed matter physics ; Physics Machine Learning in Materials Science
Authors: Ian Dunn | David Ryan Koes
Source: PubMed (preprint, 2023-11-22)
Field: Materials Science — Materials Chemistry

Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural networks (GNNs) with graph size as well as the relatively slow inference speeds inherent to diffusion models, many existing molecular diffusion models rely on coarse-grained representations of protein structure to make training and inference feasible. However, such coarse-grained representations discard essential information for modeling molecular interactions and impair the quality of generated structures. In this work, we present a novel GNN-based architecture for learning latent representations of molecular structure. When trained end-to-end with a diffusion model for <i>de novo</i> ligand design, our model achieves comparable performance to one with an all-atom protein representation while exhibiting a 3-fold reduction in inference time. Inference ; Computer science ; Graph ; Representation (politics) ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Davide Alessandro Martella | Leor Ariel Rose | Nadia Rouatbi | Chenlei Gu | Valeria Caprettini | Magnus Jensen | Cathleen Hagemann | Andrea Serio | Khuloud T. Al‐Jamal | Maddy Parsons | Mads S. Bergholt | Paul Brennan | Assaf Zaritsky | Ciro Chiappini
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-03-12)
Field: Chemistry — Spectroscopy

Nondestructive Spatial Lipidomics for Glioma Classification Abstract Mapping the molecular composition of tissues using spatial biology provides high-content information for molecular diagnostics. However, spatial biology approaches require invasive procedures to collect samples and destroy the investigated tissue, limiting the extent of analysis, particularly for highly functional tissues such as those of the brain. To address these limitations, we developed a workflow to harvest biomolecules from brain tissues using nanoneedles and characterise the distribution of lipids using desorption electrospray ionization mass spectrometry imaging. The nanoneedles preserved the original tissue while harvesting a reliable molecular profile and retaining the original lipid distribution for mouse and human brain samples, accurately outlining the morphology of key regions within the brain and tumour lesions. The deep neural network analysis of a cohort containing 23 human glioma biopsies showed that nanoneedle samples maintain the molecular signatures required to accurately classify disease state. Thus, nanoneedles provide a route for tissue-preserving spatial lipidomic and molecular diagnostics. Lipidomics ; Mass spectrometry imaging ; Molecular imaging ; Mass cytometry ; Computational biology ; Human brain ; Glioma ; Pathology ; Nanotechnology ; Mass spectrometry ; Computer science ; Biology ; Chemistry ; Materials science ; Neuroscience ; Bioinformatics ; Medicine ; Biochemistry ; In vivo ; Chromatography ; Cancer research Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Raul Ortega Ochoa | Bardi Benediktsson | Renata Sechi | Peter Bjørn Jørgensen | Arghya Bhowmik
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Materials funnel 2.0 – data-driven hierarchical search for exploration of vast chemical spaces We propose a novel HTCS accelerated inverse design in a very large materials space combining the benefits of generative modeling, computationally efficient machine learning surrogate and high-quality physics-based simulation. Funnel ; Computer science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Kavian Khosravinia | Amirkianoosh Kiani
Source: STAR Protocols (article, 2023-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Protocol for fabricating pseudocapacitor electrodes using ultra-short laser pulses for in situ nanostructure generation Ultra-short laser pulses for in situ nanostructure generation (ULPING) enable the production of high-performance capacitive electrodes for pseudocapacitors, opening avenues for optimal electrode design. Here, we present a protocol for fabricating pseudocapacitor electrodes using ULPING. We describe steps for electrode fabrication, coin cell assembly, material characterization, and electrochemical analysis. Additionally, we present strategies for generating data and constructing machine learning algorithms to predict the electrochemical properties of the fabricated electrodes. For complete details on the use and execution of this protocol, please refer to Khosravinia et al. (2023).<sup>1</sup>. Pseudocapacitor ; Electrode ; Materials science ; Nanostructure ; Nanotechnology ; In situ ; Electrochemistry ; Protocol (science) ; Laser ; Computer science ; Optoelectronics ; Supercapacitor ; Chemistry ; Optics Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors ; Neuroscience and Neural Engineering
Authors: Youtian Li | Yangyu Guo | Shiyun Xiong | Hong-Liang Yi
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Enhanced Heat Transport in Amorphous Silicon Via Microstructure Modulation Microstructure ; Modulation (music) ; Amorphous silicon ; Amorphous solid ; Silicon ; Materials science ; Optoelectronics ; Chemistry ; Metallurgy ; Crystallography ; Physics ; Crystalline silicon ; Acoustics Thermal properties of materials ; Thin-Film Transistor Technologies ; Phase-change materials and chalcogenides