jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. Н. Филанович | А. А. Повзнер
Source: AIP conference proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Simulation of lattice properties of SnSe based on Ab initio calculations and machine learning We develop a thermodynamic model of the lattice properties of a promising thermoelec-tric material SnSe. The model is based on the results of first principle calculations of the ground state energy of SnSe as well as on the parameters of lattice vibrations estimated using machine learning methods. Ab initio ; Lattice (music) ; Computer science ; Ab initio quantum chemistry methods ; Materials science ; Artificial intelligence ; Condensed matter physics ; Statistical physics ; Machine learning ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Zhichao Han | David S. Kammer | Olga Fink
Source: Repository for Publications and Research Data (ETH Zurich) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

Learning Physics-Consistent Particle Interactions Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part that allows to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration. We test the proposed methodology on multiple datasets and demonstrate that it achieves superior performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets. The proposed framework is scalable to larger systems and transferable to any type of particle interactions, contrary to the previously proposed purely data-driven solutions. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence guide the design of materials with targeted properties. Pairwise comparison ; Computer science ; Scalability ; Node (physics) ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Graph ; Physics Machine Learning in Materials Science
Authors: Diego Morales Bravo | Miguel De‐la‐Torre | Brenda Acevedo-Juárez | Gabriel Alberto García‐Mireles
Source:  (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Systematic mapping: Use of neural networks for analysis in transmission electron microscopy micrographs Microscopy techniques have been prominently part of advances in fields such as biology, medicine, and the study and development of materials over the last decade. The characteriza- tion of nanoparticles from distinct materials is challenging due to differences in morphology, size and shape. In order to evaluate these properties, tools such as the optical microscope, the atomic force microscope and the transmission electron microscope are essential. However, even an apparently simple measurement such as the size of a particle can be a challenge, this task becomes more difficult if you are working with materials for which particles are far from the ideal shape. In recent years, artificial neural networks (RNA) and especially convolutional neural networks (RNC) have been showing an enormous capacity for complex vision tasks such as detection, segmentation and classification.In this paper, we show the results of a mapping study on the work related to implementations of RNA for the analysis of electron transmission microscope micrographs. In addition to reviewing the techniques and tools most commonly used in RNA implementations. Results indicate a growing interest of the scientific community to propose related solutions, with RNC as the leader technique in micrograph analysis. Micrograph ; Computer science ; Convolutional neural network ; Microscope ; Transmission electron microscopy ; Artificial intelligence ; Artificial neural network ; Microscopy ; Electron microscope ; Segmentation ; Transmission (telecommunications) ; Nanotechnology ; Pattern recognition (psychology) ; Computer vision ; Materials science ; Scanning electron microscope ; Optics ; Physics ; Telecommunications Machine Learning in Materials Science
Authors: Qingzhao Chu | Chenguang Wang | Dongping Chen
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Toward Full Ab Initio Modeling of Soot Formation in a Nanoreactor Nanoreactor ; Ab initio ; Soot ; Computational chemistry ; Materials science ; Statistical physics ; Chemistry ; Combustion ; Nanotechnology ; Physical chemistry ; Physics ; Organic chemistry ; Nanoparticle Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Kumar Ghosh | Sumit Ghosh
Source: arXiv (Cornell University) (preprint, 2022-07-26)
Field: Materials Science — Materials Chemistry

Classical and quantum machine learning applications in spintronics In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurity we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. By mapping this quantum mechanical problem onto a classification problem, we are able to obtain much higher accuracy beyond the linear response regime compared to the prediction obtained with conventional regression methods. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is applicable for solid state devices as well as for molecular systems. These outcomes are crucial in predicting the behavior of large-scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nano devices. Spintronics ; Quantum ; Computer science ; Spin (aerodynamics) ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Ferromagnetism Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Advancements in Semiconductor Devices and Circuit Design
Authors: Edwin Cartlidge | Patricia Daukantas | Meeri Kim | Sarah Michaud | Karen Kwon | Stewart Wills | Alessia Kirkland
Source: Optics and Photonics News (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Research and Industry News VUV metalens / Table-top X-ray laser / Machine learning speeds up PV development / Near-zero-index materials / Making a “magic window” / Event Horizon Telescope / News from Ayar Labs and IonQ / THz rays reveal hidden inscription MAGIC (telescope) ; Window (computing) ; Optics ; Laser ; Telescope ; Event (particle physics) ; Horizon ; Physics ; Astronomy ; Computer science ; Astrophysics ; World Wide Web Machine Learning in Materials Science ; Technology Assessment and Management ; Sustainable Development and Policies
Authors: Matthew Helmi Leth Larsen | Frederik Dahl | David Christoffer Bisp Nielsen | Lars P. Hansen | Bastian Barton | Christian Kisielowski | Ole Winther | Thomas W. Hansen | Stig Helveg | Jakob Schiøtz
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Machine-Learning Assisted Exit-wave Reconstruction for Quantitative Feature Extraction Reconstruction of the exit wave is a powerful tool to extract the maximal amount of information from High-resolution Transmission Electron Microscopy (HRTEM). In addition to the three-dimensional structure of the nanoparticle, the reconstructed exit waves also contained information about the beam-stimulated vibrations of the atoms nearthe edge of the nanoparticle. We have recently demonstrated that convolutional neural networks are able to reconstruct the exit wave fromafocal serieswith a low number of images. We train the neural networks on simulated images. The simulated images are produced with the multislice algorithm using the abTEM software, both the exit wave function and images produced with three different values of the defocus are saved. The neural network is then trained to reconstruct the exit wave from the images. The network is validated on a different set of simulated images, and if applicable applied to experimentally obtained data. We demonstrated that it is possible to train neural networks to reconstruct the exit wave for a varied set of samples consisting of all structures in the Computational 2D Materials Database (C2DB). For a specialized dataset such asMolybdenum Disulphide (MoS2) supported on graphene, a slightlylower error rate can be obtained(Figure 2), and realistic results can be obtained when the network is applied to experimental data. In this work, we investigate how far the convolutional neural networks can be optimized towards obtaining quantitative information from experimental data, with a particular focus on the kind of data i.e.,reconstructing exit waves with sufficient accuracy to extract the three-dimensional structure and the amplitudes of the atomic vibrations. This can be realized with more flexible training sets than in our previous publicationand by training the network to ignore the support when reconstructing the exit wave. Extraction (chemistry) ; Artificial intelligence ; Feature extraction ; Feature (linguistics) ; Computer science ; Pattern recognition (psychology) ; Chromatography ; Chemistry ; Philosophy 2D Materials and Applications ; Neural Networks and Reservoir Computing ; Topological Materials and Phenomena
Authors: Bo Xu | Zhanpeng Gong | Jingran Liu | Yunfei Hong | Yang Yang | Lou Li | Yilun Liu | Junkai Deng | Jefferson Zhe Liu
Source: arXiv (Cornell University) (preprint, 2022-04-11)
Field: Materials Science — Materials Chemistry

Tunable ferroelectric topological defects on 2D topological surfaces: strain engineering skyrmion-like polar structures in 2D materials Polar topological structures in ferroelectric thin films have recently drawn significant interest due to their fascinating physical behaviors and promising applications in high-density nonvolatile memories. However, most polar topological patterns are only observed in the perovskites superlattices. Here, we report the discovery of the tunable ferroelectric polar topological defective structures designed and achieved by strain engineering in two-dimensional PbX (X=S, Se, and Te) materials using multiscale computational simulations. First, the first-principles calculations demonstrate the strain-induced recoverable ferroelectric phase transition in such 2D materials. The unique polar topological vortex pattern is then induced by applied mechanical indentation, evidenced by molecular dynamics simulations based on a developed deep-learning potential. According to the strain phase diagram and applied complex strain loadings, the diverse polar topological structures, including antivortex structure and flux-closure structure, are predicted to be emergent through the finite-element simulations. We conclude that strain engineering is promising to tailor various designed reversible polar topologies in ultra-flexible 2D materials, which provide excellent opportunities for next-generation nanoelectronics and sensor devices. Ferroelectricity ; Polar ; Topology (electrical circuits) ; Materials science ; Strain engineering ; Superlattice ; Topological defect ; Condensed matter physics ; Phase transition ; Nanotechnology ; Optoelectronics ; Physics ; Dielectric ; Engineering Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Carles Aliod
Source:  (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Machine learning the C5H5 potential energy surface. MotivationDevelop computational tools to enable faster exploration of elementary reactions that play a role in the initial stages of soot formation. Approach Find stationary points and IRC pathways at DFT level on C m H n PESs using our automated kinetics workflow tool, KinBot. 2 Create a database of geometries and calculated energies and forces around the stationary points using normal mode sampling. Train a neural network (NN) PES on these points. Use the NN PES to explore similar, but new PESs faster in order to accelerate discovery and the calculation of pressure-and temperature dependent rate coefficients using a master equation. To aid the NN PES creation, we explore the benefits of force training, and to eventually achieve chemical accuracy, we develop a multi-level approach.Machine learning the C 5 H 5 potential energy surface NN PES We used the atomic environment vector (AEV) symmetry function based formalism, which provides rotational, translational and permutational invariance. 4 The NNPES construction employs element-specific subnetworks.Atom-specific energies are combined to obtain the total energy of the system. Active learning employing 5 learners and query by committee (QBC) is used to enrich training data in regions with high prediction uncertainty. Computer science ; Energy (signal processing) ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Chemical Physics Studies
Authors: Anastasia Yu. Sholokhova | Oksana I. Grinevich | Dmitriy D. Matyushin | Aleksey K. Buryak
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Machine Learning Assisted Non-Target Analysis of the Highly Complex Mixture of the Possible Toxic Unsymmetrical Dimethylhydrazine Products with Chromatography-Mass Spectrometry Dimethylhydrazine ; Mass spectrometry ; Chromatography ; Chemistry ; Medicine ; Colorectal cancer Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Maxim Ziatdinov | Yongtao Liu | Kyle P. Kelley | Rama K. Vasudevan | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2022-05-30)
Field: Materials Science — Materials Chemistry

Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian Process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian Processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. The discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods, and can be particularly impactful for destructive or irreversible measurements. Artificial intelligence ; Machine learning ; Computer science ; Gaussian process ; Bayesian inference ; Bayesian probability ; Workflow ; Gaussian ; Physics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Machine Learning and Algorithms
Authors: Ioanna Aslanidou | Jerol Soibam
Source: Linköping electronic conference proceedings (article, 2022-10-28)
Field: Chemistry — Analytical Chemistry

Comparison of machine learning approaches for spectroscopy applications In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models. Hyperspectral imaging ; Computer science ; Calibration ; Imaging spectrometer ; Imaging spectroscopy ; Chemical imaging ; Spectrometer ; Remote sensing ; Data acquisition ; Artificial intelligence ; Process engineering ; Key (lock) ; Data mining ; Engineering ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Arun Baskaran | Yulin Lin | Jianguo Wen | Maria K. Y. Chan
Source: arXiv (Cornell University) (preprint, 2022-09-05)
Field: Materials Science — Materials Chemistry

Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images Automated analysis of electron microscopy datasets poses multiple challenges, such as limitation in the size of the training dataset, variation in data distribution induced by variation in sample quality and experiment conditions, etc. It is crucial for the trained model to continue to provide acceptable segmentation/classification performance on new data, and quantify the uncertainty associated with its predictions. Among the broad applications of machine learning, various approaches have been adopted to quantify uncertainty, such as Bayesian modeling, Monte Carlo dropout, ensembles, etc. With the aim of addressing the challenges specific to the data domain of electron microscopy, two different types of ensembles of pre-trained neural networks were implemented in this work. The ensembles performed semantic segmentation of ice crystal within a two-phase mixture, thereby tracking its phase transformation to water. The first ensemble (EA) is composed of U-net style networks having different underlying architectures, whereas the second series of ensembles (ER-i) are composed of randomly initialized U-net style networks, wherein each base learner has the same underlying architecture 'i'. The encoders of the base learners were pre-trained on the Imagenet dataset. The performance of EA and ER were evaluated on three different metrics: accuracy, calibration, and uncertainty. It is seen that EA exhibits a greater classification accuracy and is better calibrated, as compared to ER. While the uncertainty quantification of these two types of ensembles are comparable, the uncertainty scores exhibited by ER were found to be dependent on the specific architecture of its base member ('i') and not consistently better than EA. Thus, the challenges posed for the analysis of electron microscopy datasets appear to be better addressed by an ensemble design like EA, as compared to an ensemble design like ER. Computer science ; Artificial intelligence ; Segmentation ; Artificial neural network ; Dropout (neural networks) ; Pattern recognition (psychology) ; Bayesian probability ; Machine learning ; Transformation (genetics) Machine Learning in Materials Science ; Nuclear Physics and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Fan Yang
Source: theses.fr (ABES) (preprint, 2022-12-20)
Field: Chemistry — Spectroscopy

Avancée dans le domaine du monitoring et de l'analyse de suspects et d'approche non ciblée par spectrométrie de masse à haute résolution et mobilité ionique. Due to global industrialization and anthropic activities, millions of tonnes of chemicals were consumed per year in the European Union member states (EU). Chemicals can release into the environment by various resources.Thus, this research project aims to develop non-target analysis workflows for a large scale of CECs by using gas/liquid chromatography coupling with HRMS and IMS. An in-house database was built with accurate mass, isotopic pattern, fragment ions, and CCS values. This manuscript presents and discusses the major advancements in diverse aspects of NTA.Chapter 1: Bibliography research: The 1st chapter describes the workflow of non-target analysis. Fundamental concepts and the state-of-the-art of the HRMS and IMS are discussed.Chapter 2: Presents the development and application of the non-target analysis method using GC-APCI-IMS-HRMS. The method was optimized by PCB congeners and PBDE congeners. The benefits of combining APCI with IMS for Cl/Br chemicals are highlighted.Chapter 3: Details the development of a non-target analysis method based on UHPLC-HRMS in water analysis. Data processing strategies and workflow are described.Chapter 4: Steroids hormones analysis and cortisol quantification. It highlights the benefit of IMS to enhance the S/N ratio in low concentration standards.Chapter 5: A CCS prediction tool was developed by machine learning. The molecular fingerprint was first time used to describe chemical structure. Two modeling strategies are compared to evaluate the impact of chemical classes to prediction accuracy.Chapter 6: Finally, the milestones and limitations of presence research are emphasized. Ion-mobility spectrometry ; Mass spectrometry ; Ion-mobility spectrometry–mass spectrometry ; High resolution ; Contamination ; Resolution (logic) ; Chemistry ; Environmental chemistry ; Analytical Chemistry (journal) ; Chromatography ; Selected reaction monitoring ; Tandem mass spectrometry ; Computer science ; Remote sensing ; Geology Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development