jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paulo Matheus Vinhas | Ricardo Pires | Eduardo Bock | Tarcísio Leão
Source: The Academic Society Journal (article, 2021-06-01)
Field: Chemistry — Analytical Chemistry

Sistema inteligente de processamento e análise de vibrações em máquinas rotativas para Manutenção Preditiva Avançada em indústria 4.0 The arrival of 5G provides an evolution of predictive maintenance with a focus on multiple sensors on the machines to measure dozens of parameters, ensuring that data is always collected reliable. For this purpose, an adaptive machine learning method is proposed in this work to deal with composite signals. The challenge of dealing with the varying size of these signals is overcome by exploring the parameters of a support vector machine classifier. Maximum assertiveness in the classification is achieved, guaranteeing an adaptive and effective solution, built on a computationally consolidated method, making it easy to implement. Computer science ; Classifier (UML) ; Support vector machine ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Paulo Matheus Vinhas | Ricardo Pires | Eduardo Bock | Tarcísio Leão
Source: The Academic Society Journal (article, 2021-06-01)
Field: Chemistry — Analytical Chemistry

Sistema inteligente de processamento e análise de vibrações em máquinas rotativas para Manutenção Preditiva Avançada em indústria 4.0 The arrival of 5G provides an evolution of predictive maintenance with a focus on multiple sensors on the machines to measure dozens of parameters, ensuring that data is always collected reliable. For this purpose, an adaptive machine learning method is proposed in this work to deal with composite signals. The challenge of dealing with the varying size of these signals is overcome by exploring the parameters of a support vector machine classifier. Maximum assertiveness in the classification is achieved, guaranteeing an adaptive and effective solution, built on a computationally consolidated method, making it easy to implement. Computer science ; Classifier (UML) ; Support vector machine ; Measure (data warehouse) ; Artificial intelligence ; Machine learning ; Data mining Spectroscopy and Chemometric Analyses
Authors: Truong Son-Hy | Risi Kondor
Source: arXiv (Cornell University) (preprint, 2021-06-02)
Field: Materials Science — Materials Chemistry

Multiresolution Graph Variational Autoencoder. In this paper, we propose Multiresolution Graph Networks (MGN) and Multiresolution Graph Variational Autoencoders (MGVAE) to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGN employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution. MGVAE constructs a hierarchical generative model based on MGN to variationally autoencode the hierarchy of coarsened graphs. Our proposed framework is end-to-end permutation equivariant with respect to node ordering. Our methods have been successful with several generative tasks including link prediction on citation graphs, unsupervised molecular representation learning to predict molecular properties, molecular generation, general graph generation and graph-based image generation. Autoencoder ; Equivariant map ; Theoretical computer science ; Computer science ; Generative grammar ; Graph ; Generative model ; Artificial intelligence ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Anugrah Azhar
Source: Jurnal Fisika dan Aplikasinya (article, 2021-06-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-Orbit Effect on Thermal Properties of Half-Heusler Alloy FeVSb FeVSb, a family of Half-Heusler alloy, is categorized as an n-type semiconductor which has a high figure of merit that makes this material to be one of the promising candidates for a thermoelectric device. In FeVSb, Fe and V atoms which have d -orbitals can be considered to have spin-orbit interaction that can affect the electronic structure and thermal properties of this material. In order to investigate how strong the spin-orbit interaction affect this material, we do the first-principles study by implementing the spin-orbit interaction to investigate the change of the band structure and thermal properties of FeVSb. Our results show that the spin-orbit interaction affects the band structure of the material indicated by the energy splitting in the electronic structure which increase the Seebeck coefficient, electrical conductivity, and thermal conductivity but slightly reduce the figure of merit values of FeVSb. Figure of merit ; Condensed matter physics ; Materials science ; Thermoelectric effect ; Spin–orbit interaction ; Thermal conductivity ; Spin (aerodynamics) ; Semiconductor ; Seebeck coefficient ; Thermoelectric materials ; Electronic band structure ; Alloy ; Orbit (dynamics) ; Atomic orbital ; Optoelectronics ; Physics ; Electron ; Composite material ; Thermodynamics Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films
Authors: Nick A. Eaves | Qingan Zhanga | Fengshan Liu | Hongsheng Guo Guo | Seth B. Dworkin | Murray J. Thomson
Source:  (preprint, 2021-07-28)
Field: Materials Science — Materials Chemistry

CoFlame: A refined and validated numerical algorithm for modeling sooting laminar coflow diffusion flames Mitigation of soot emissions from combustion devices is a global concern. For example, recent EURO 6 regulations for vehicles have placed stringent limits on soot emissions. In order to allow design engineers to achieve the goal of reduced soot emissions, they must have the tools to so. Due to the complex nature of soot formation, which includes growth and oxidation, detailed numerical models are required to gain fundamental insights into the mechanisms of soot formation. A detailed description of the CoFlame FORTRAN code which models sooting laminar coflow diffusion flames is given. The code solves axial and radial velocity, temperature, species conservation, and soot aggregate and primary particle number density equations. The sectional particle dynamics model includes nucleation, PAH condensation and HACA surface growth, surface oxidation, coagulation, fragmentation, particle diffusion, and thermophoresis. The code utilizes a distributed memory parallelization scheme with strip-domain decomposition. The public release of the CoFlame code, which has been refined in terms of coding structure, to the research community accompanies this paper. CoFlame is validated against experimental data for reattachment length in an axi-symmetric pipe with a sudden expansion, and ethylene–air and methane–air diffusion flames for multiple soot morphological parameters and gas-phase species. Finally, the parallel performance and computational costs of the code is investigated. Soot ; Diffusion flame ; Laminar flow ; Numerical diffusion ; Nucleation ; Diffusion ; Domain decomposition methods ; Thermophoresis ; Combustion ; Mechanics ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Combustor ; Heat transfer ; Physical chemistry ; Finite element method Catalytic Processes in Materials Science ; Atmospheric chemistry and aerosols ; Advanced Combustion Engine Technologies
Authors: Dai Hai Nguyen | Koji Tsuda
Source: arXiv (Cornell University) (preprint, 2021-06-07)
Field: Materials Science — Materials Chemistry

A generative model for molecule generation based on chemical reaction trees Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However, these models are limited in recommending synthetic routes for the generated molecules in practice. We propose a generative model to generate molecules via multi-step chemical reaction trees. Specifically, our model first propose a chemical reaction tree with predicted reaction templates and commercially available molecules (starting molecules), and then perform forward synthetic steps to obtain product molecules. Experiments show that our model can generate chemical reactions whose product molecules are with desired chemical properties. Also, the complete synthetic routes for these product molecules are provided. Molecule ; Template ; Generative model ; Tree (set theory) ; Computer science ; Chemical reaction ; Chemical space ; Product (mathematics) ; Generative grammar ; Biological system ; Chemistry ; Artificial intelligence ; Mathematics ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Avadhut Sardeshmukh | Sreedhar Reddy | B. P. Gautham | Pushpak Bhattacharyya
Source: National Conference on Artificial Intelligence (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

TextureVAE: Learning Interpretable Representations of Material Microstructures Using Variational Autoencoders. Artificial intelligence ; Computer science ; Microstructure ; Pattern recognition (psychology) ; Machine learning ; Natural language processing ; Materials science ; Composite material Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Image Processing and 3D Reconstruction
Authors: Mani Valleti | Sergei V. Kalinin | Christopher T. Nelson | Jonathan J. P. Peters | Wen Dong | Richard Beanland | Xiaohang Zhang | Ichiro Takeuchi | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2021-01-18)
Field: Materials Science — Materials Chemistry

Unsupervised learning of ferroic variants from atomically resolved STEM images An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and are shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in the manuscript is available at https://github.com/saimani5/ferroelectric_domains_rVAE. Autoencoder ; Unsupervised learning ; Cluster analysis ; Dimensionality reduction ; Curse of dimensionality ; Invariant (physics) ; Artificial intelligence ; Lattice (music) ; Degrees of freedom (physics and chemistry) ; Microscope ; Computer science ; Physics ; Pattern recognition (psychology) ; Optics ; Deep learning Electronic and Structural Properties of Oxides ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Rocío Mercado | Esben Jannik Bjerrum | Ola Engkvist
Source:  (preprint, 2021-09-20)
Field: Materials Science — Materials Chemistry

Exploring graph traversal algorithms in graph-based molecular generation Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical Graph traversal ; Tree traversal ; Computer science ; Graph ; Algorithm ; Theoretical computer science ; Depth-first search ; Generative model ; Generative grammar ; Artificial intelligence ; Search algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rocío Mercado | Esben Jannik Bjerrum | Ola Engkvist
Source:  (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Exploring graph traversal algorithms in graph-based molecular generation Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical Graph traversal ; Tree traversal ; Computer science ; Graph ; Algorithm ; Depth-first search ; Breadth-first search ; Generative grammar ; Theoretical computer science ; Generative model ; Node (physics) ; Artificial intelligence ; Search algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sheng Cheng | Yang Jiao | Yi Ren
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations Correlation ; Point (geometry) ; Microstructure ; Computer science ; Artificial intelligence ; Statistical physics ; Mathematics ; Psychology ; Materials science ; Physics ; Geometry ; Metallurgy Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Electron and X-Ray Spectroscopy Techniques
Authors: Rafał Kiełbik | Krzysztof Hałagan | Kamil Rudnicki | Piotr Polanowski | Grzegorz Jabłoński | Jarosław Jung
Source:  (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

Molecular Diffusion Simulation on ARUZ – Massively-parallel FPGA-based Machine In this paper, the implementation of a molecular diffusion model named Dynamic Lattice Liquid (DLL) is discussed. Two algorithms, sequential and parallel, implementing this model are examined. The presented theoretical considerations indicate some limitations of the sequential approach. The proposed parallel algorithm overcomes these limitations, but it requires dedicated hardware to function. An example of such hardware, the FPGA-based HPC system named ARUZ, is presented. Massively parallel ; Computer science ; Field-programmable gate array ; Parallel computing ; Computational science ; Function (biology) ; Computer hardware Lanthanide and Transition Metal Complexes ; Microfluidic and Capillary Electrophoresis Applications ; Nonlinear Dynamics and Pattern Formation
Authors: Emma Benjaminson | R. E. Taylor | Matthew Travers
Source: arXiv (Cornell University) (preprint, 2021-01-29)
Field: Materials Science — Materials Chemistry

Predicting Nanorobot Shapes via Generative Models The field of DNA nanotechnology has made it possible to assemble, with high yields, different structures that have actionable properties. For example, researchers have created components that can be actuated. An exciting next step is to combine these components into multifunctional nanorobots that could, potentially, perform complex tasks like swimming to a target location in the human body, detect an adverse reaction and then release a drug load to stop it. However, as we start to assemble more complex nanorobots, the yield of the desired nanorobot begins to decrease as the number of possible component combinations increases. Therefore, the ultimate goal of this work is to develop a predictive model to maximize yield. However, training predictive models typically requires a large dataset. For the nanorobots we are interested in assembling, this will be difficult to collect. This is because high-fidelity data, which allows us to characterize the shape and size of individual structures, is very time-consuming to collect, whereas low-fidelity data is readily available but only captures bulk statistics for different processes. Therefore, this work combines low- and high-fidelity data to train a generative model using a two-step process. We first use a relatively small, high-fidelity dataset to train a generative model. At run time, the model takes low-fidelity data and uses it to approximate the high-fidelity content. We do this by biasing the model towards samples with specific properties as measured by low-fidelity data. In this work we bias our distribution towards a desired node degree of a graphical model that we take as a surrogate representation of the nanorobots that this work will ultimately focus on. We have not yet accumulated a high-fidelity dataset of nanorobots, so we leverage the MolGAN architecture [1] and the QM9 small molecule dataset [2-3] to demonstrate our approach. Generative grammar ; Generative model ; Artificial intelligence ; Nanorobotics ; Computer science Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Nicholas Stiles Wilkins | James A. Sawada | Arvind Rajendran
Source: ChemRxiv (article, 2021-05-20)
Field: Chemistry — Inorganic Chemistry

Diffusion of CH4 and N2 in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that is able to separate CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from limiting vacuum to 1.2 bar were measured at 30, 40, and 50° C for CH4 and at 30, 50, and 70° C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50° C and N2 -20, -10, and 0° C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken's relationship, the barrier resistance did not. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behaviour methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated CH4/N2 kinetic selectivity of Ba-RPZ was shown to be significantly larger than the current benchmark material for CH4/N2 separation. Microporous material ; Diffusion ; Zeolite ; Molecular sieve ; Methane ; Chemistry ; Analytical Chemistry (journal) ; Adsorption ; Thermodynamics ; Materials science ; Volume (thermodynamics) ; Barium ; Physical chemistry ; Inorganic chemistry ; Chromatography ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Nicholas Stiles Wilkins | James A. Sawada | Arvind Rajendran
Source: ChemRxiv (preprint, 2021-05-04)
Field: Chemistry — Inorganic Chemistry

Diffusion of CH4 and N2 in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that is able to separate CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from limiting vacuum to 1.2 bar were measured at 30, 40, and 50° C for CH4 and at 30, 50, and 70° C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50° C and N2 -20, -10, and 0° C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken's relationship, the barrier resistance did not. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behaviour methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated CH4/N2 kinetic selectivity of Ba-RPZ was shown to be significantly larger than the current benchmark material for CH4/N2 separation. Microporous material ; Diffusion ; Zeolite ; Methane ; Molecular sieve ; Materials science ; Analytical Chemistry (journal) ; Adsorption ; Chemistry ; Thermodynamics ; Volume (thermodynamics) ; Physical chemistry ; Chromatography ; Composite material ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis