jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marco Podda | Davide Bacciu
Source: arXiv (Cornell University) (preprint, 2021-07-18)
Field: Materials Science — Materials Chemistry

GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model. Among this family of approaches, we focus on the GraphGen model. The preprocessing phase of GraphGen transforms graphs into unique edge sequences called Depth-First Search (DFS) codes, such that two isomorphic graphs are assigned the same DFS code. Each element of a DFS code is associated with a graph edge: specifically, it is a quintuple comprising one node identifier for each of the two endpoints, their node labels, and the edge label. GraphGen learns to generate such sequences auto-regressively and models the probability of each component of the quintuple independently. While effective, the independence assumption made by the model is too loose to capture the complex label dependencies of real-world graphs precisely. By introducing a novel graph preprocessing approach, we are able to process the labeling information of both nodes and edges jointly. The corresponding model, which we term GraphGen-Redux, improves upon the generative performances of GraphGen in a wide range of datasets of chemical and social graphs. In addition, it uses approximately 78% fewer parameters than the vanilla variant and requires 50% fewer epochs of training on average. Computer science ; Preprocessor ; Graph ; Theoretical computer science ; ENCODE ; Node (physics) ; Marginal distribution ; Conditional independence ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Software Engineering Research
Authors: P. Villars | Evgeny Blokhin | Shuichi Iwata
Source: arXiv (Cornell University) (preprint, 2021-05-26)
Field: Materials Science — Materials Chemistry

Data-driven relationship of atomic structure and physical properties as the holistic view on the materials science fundamentals The fundamental relationship of the atomic structure (represented by its atomic property parameters, APPs) and its physical properties of a specific inorganic substance can be realized in the bottom-up data-centric and the top-down knowledge physics-centric ways. Nowadays these two approaches compete and enhance one another qualitatively and quantitatively. We present our own holistic method and implementation, based on the PAULING FILE peer-reviewed inorganic substances database, the world largest materials database containing under one shelter crystallographic structures, phase diagrams and large variety of physical properties of single-phase inorganic substances. In addition we present generated machine-learning data, as well as simulated DFT physics-centered data, which are in close connection and comparison with the PAULING FILE peer-reviewed reference data. Physical property ; Variety (cybernetics) ; Property (philosophy) ; Computer science ; Nanotechnology ; Materials science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Moeen Meigooni | Emad Tajkhorshid
Source: Biophysical Journal (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Waypoint Graph Generation with Growing Neural Gases for Pathfinding Applications in Molecular Dynamics Simulations Waypoint ; Autoencoder ; Computer science ; Unsupervised learning ; Artificial intelligence ; Configuration space ; Generative model ; Discretization ; Motion planning ; Graph ; Artificial neural network ; Theoretical computer science ; Machine learning ; Mathematics ; Robot ; Physics ; Generative grammar Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: M. Vimal | Stefan Sandfeld | Aruna Prakash
Source: arXiv (Cornell University) (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of results. Nanocrystalline material ; Grain boundary ; Segmentation ; Orientation (vector space) ; Materials science ; Deformation (meteorology) ; Computer science ; Scale (ratio) ; Visualization ; Artificial intelligence ; Algorithm ; Geometry ; Nanotechnology ; Mathematics ; Physics ; Microstructure Machine Learning in Materials Science ; Microstructure and mechanical properties ; Ion-surface interactions and analysis
Authors: Moein Borghei | Mona Ghassemi
Source:  (article, 2021-09-12)
Field: Materials Science — Materials Chemistry

Deep Learning Models for Multi-Source Corona Discharge Classification Under DC Voltage DC voltage has been targeted for electric transportation where failures can impact the safety of passengers or may jeopardize the success of a mission and thus are unacceptable. Because electrical insulation determines the life of an electrical system, monitoring the aging of insulation systems is essential. In this regard, partial discharge (PD) activity can be used as a primary indicator in monitoring the weakness of insulation materials. In the power corridor of electric transportation, there are many electrical generation, distribution, and storage apparatuses that each are a PD source and the input PD signal measured is a mixture of all PD sources. Separating and recognizing multiple PD sources has become a hot and, of course, challenging research topic in the dielectrics and electrical insulation area; deep learning (DL) is being considered as a promising technique to deal with it. Partial discharge ; Electrical engineering ; Voltage ; Corona discharge ; Computer science ; High voltage ; SIGNAL (programming language) ; Insulation system ; Electric power system ; Automotive engineering ; Power (physics) ; Environmental science ; Reliability engineering ; Materials science ; Engineering ; Physics High voltage insulation and dielectric phenomena ; ECG Monitoring and Analysis ; Anomaly Detection Techniques and Applications
Authors: Junya MORIGUCHI | Ken-ichi SAITOH | Kenji Nishimura | Masanori TAKUMA | Yoshimasa TAKAHASHI | Tomohiro Sato
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Constructing Machine-learned Interatomic Potentials for Covalent Bonding Materials and MD Analyses of Dislocation and Surface In recent years, it has become quite feasible to create interatomic potentials using machine learning (ML) methodology. Such ML potentials are constructed on a general functional form and a large number of parameters, and they will reproduce the results of DFT calculations. The SNAP is one of the ML potentials, and as its advanced type there is the qSNAP. In this research, we actually construct SNAP and qSNAP for silicon (Si) and 3C-silicon carbide (SiC) crystal systems and confirm their reproducibility in MD simulations. Using a genetic algorithm, optimization calculations are performed so that the error of the elastic moduli are minimized. For 3C-SiC, elastic moduli are well reproduced by qSNAP. For Si, the lattice constant, elastic moduli, and melting point are well reproduced. Besides, the dimer structure in the free surface of Si {100},which has not been predicted by empirical potentials, is successfully obtained by qSNAP. Although the dislocation behavior has not sufficiently been reproduced yet, the improvement is expected by adding other data on stacking defects. Interatomic potential ; Materials science ; Silicon ; Stacking ; Covalent bond ; Moduli ; Dimer ; Dislocation ; Crystallography ; Condensed matter physics ; Computational chemistry ; Molecular dynamics ; Chemistry ; Physics ; Composite material ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Chen Qu | Paul L. Houston | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source:  (article, 2021-03-23)
Field: Materials Science — Materials Chemistry

Breaking the CCSD(T) Barrier for Machine Learned Potentials of Large Molecules: Demonstration for Acetylacetone Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density functional theory and second-order Moller-Plesset perturbation theory (MP2). While these are efficient and realistic, they fall short of the accuracy of the gold standard coupled-cluster method [CCSD(T)], especially with respect to reaction and isomerization barriers. We report a major step forward in using a $\Delta$-machine learning method to the challenging case of acetlyacetone, whose MP2 barrier height for H-atom transfer is low by roughly 1.5 kcal/mol relative to the CCSD(T) barrier of 3.5 kcal/mol. From a database of 2151 LCCSD(T) energies, and training with as few as 430 energies, we obtain a new PES with a barrier in agreement with the LCCSD(T) one. Tunneling splittings due to H-atom transfer are calculated using this new PES, providing improved estimates over previous ones obtained using an MP2-based PES. Acetylacetone ; Coupled cluster ; Isomerization ; Perturbation theory (quantum mechanics) ; Density functional theory ; Quantum tunnelling ; Molecule ; Atom (system on chip) ; Chemistry ; Atomic physics ; Computational chemistry ; Physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Andrea Pedrielli | Paolo E. Trevisanutto | Lorenzo Monacelli | Giovanni Garberoglio | Nicola M. Pugno | Simone Taioli
Source: arXiv (Cornell University) (preprint, 2021-11-27)
Field: Materials Science — Materials Chemistry

Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network Magnesium hydride (MgH$_2$) has been widely studied for effective hydrogen storage. However, its bulk desorption temperature (553 K) is deemed too high for practical applications. Besides doping, a strategy to decrease such reaction energy for releasing hydrogen is the use of MgH$_2$-based nanoparticles (NPs). Here, we investigate first the thermodynamic properties of Mg$_n$H$_{2n}$ NPs ($n&lt;10$) from first-principles, in particular by assessing the anharmonic effects on the enthalpy, entropy and thermal expansion by means of the Stochastic Self Consistent Harmonic Approximation (SSCHA). The latter method goes beyond previous approaches, typically based on molecular mechanics and the quasi-harmonic approximation, allowing the ab initio calculation of the fully-anharmonic free energy. We find an almost linear dependence on temperature of the interatomic bond lengths - with a relative variation of few percent over 300K -, alongside with a bond distance decrease of the Mg-H bonds. In order to increase the size of NPs toward experiments of hydrogen desorption from MgH$_2$ we devise a computationally effective Machine Learning model trained to accurately determine the forces and total energies (i.e. the potential energy surfaces), integrating the latter with the SSCHA model to fully include the anharmonic effects. We find a significative decrease of the H-desorption temperature for sub-nanometric clusters Mg$_n$H$_{2n}$ with $n \leq 10$, with a non-negligible, although little effect due to anharmonicities (up to 10%). Anharmonicity ; Ab initio ; Nanoclusters ; Magnesium hydride ; Enthalpy ; Hydrogen bond ; Desorption ; Bond length ; Hydrogen ; Binding energy ; Chemistry ; Ab initio quantum chemistry methods ; Thermodynamics ; Materials science ; Physical chemistry ; Atomic physics ; Physics ; Nanotechnology ; Quantum mechanics ; Adsorption ; Molecule ; Organic chemistry Hydrogen Storage and Materials ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Benyamin Motevalli | Bronwyn Fox | Amanda S. Barnard
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Charge-Dependent Fermi Level of Graphene Oxide Nanoflakes from Machine Learning Graphene ; Charge (physics) ; Oxide ; Materials science ; Nanotechnology ; Fermi level ; Condensed matter physics ; Physics ; Quantum mechanics ; Metallurgy ; Electron Graphene research and applications ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Beibei Wang | Wenhua Jin | Miloš Hašan | Ling‐Qi Yan
Source: arXiv (Cornell University) (preprint, 2021-10-14)
Field: Materials Science — Polymers and Plastics

SpongeCake: A Layered Microflake Surface Appearance Model In this paper, we propose SpongeCake: a layered BSDF model where each layer is a volumetric scattering medium, defined using microflake or other phase functions. We omit any reflecting and refracting interfaces between the layers. The first advantage of this formulation is that an exact and analytic solution for single scattering, regardless of the number of volumetric layers, can be derived. We propose to approximate multiple scattering by an additional single-scattering lobe with modified parameters and a Lambertian lobe. We use a parameter mapping neural network to find the parameters of the newly added lobes to closely approximate the multiple scattering effect. Despite the absence of layer interfaces, we demonstrate that many common material effects can be achieved with layers of SGGX microflake and other volumes with appropriate parameters. A normal mapping effect can also be achieved through mapping of microflake orientations, which avoids artifacts common in standard normal maps. Thanks to the analytical formulation, our model is very fast to evaluate and sample. Through various parameter settings, our model is able to handle many types of materials, like plastics, wood, cloth, etc., opening a number of practical applications. Scattering ; Surface (topology) ; Layer (electronics) ; Optics ; Computer science ; Side lobe ; Algorithm ; Materials science ; Geometry ; Physics ; Mathematics ; Telecommunications ; Composite material Textile materials and evaluations ; Computer Graphics and Visualization Techniques ; Color Science and Applications
Authors: Kyowon Jeong | Maša Babović | Vladimir Gorshkov | Jihyung Kim | Ole N. Jensen | Oliver Kohlbacher
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-11-13)
Field: Chemistry — Spectroscopy

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count Abstract Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in ⅓ of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates. Computer science ; Identification (biology) ; Data acquisition ; Proteomics ; Selection (genetic algorithm) ; Data mining ; Machine learning ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Fabian Berressem | Arash Nikoubashman
Source: The Journal of Chemical Physics (preprint, 2021-03-28)
Field: Materials Science — Materials Chemistry

BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the NVT ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient and the Carnahan-Starling equation of state for hard sphere liquids. Furthermore, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, g(r), a task that is often performed for inverse design and coarse-graining. Providing the NNs with additional information on the forces greatly improves the accuracy of the predictions since more correlations are taken into account; the predicted potentials become smoother, are significantly closer to the target potentials, and are more transferable as a result. Virial coefficient ; Isotropy ; Artificial neural network ; Statistical physics ; Monatomic gas ; Pair potential ; Limit (mathematics) ; Inverse ; Virial theorem ; Computer science ; Radial distribution function ; Equation of state ; Molecular dynamics ; Applied mathematics ; Mathematics ; Physics ; Artificial intelligence ; Thermodynamics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Maxim Ziatdinov | Yongtao Liu | Anna N. Morozovska | Eugene А. Eliseev | Xiaohang Zhang | Ichiro Takeuchi | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2021-12-13)
Field: Materials Science — Materials Chemistry

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here we introduce an active learning approach based on co-navigation of the hypothesis and experimental spaces. This is realized by combining the structured Gaussian Processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. We demonstrate this approach for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using Piezoresponse Force Microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis-generation are available. Workflow ; Reinforcement learning ; Probabilistic logic ; Computer science ; Active learning (machine learning) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Yolla Torina | Tuti Purwaningsih
Source: AIP conference proceedings (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Chili classification automation using deep learning with convolutional neural network method (Case study: Three types of chili with form of curly, cayenne and binocular) Chili can be easily found in markets, minimarkets or large supermarkets, only the ones that are easily found are curly chili, cayenne pepper and binocular chili. Warehouse or storage is a place for storing goods both raw materials which are then processed or manufactured goods that are ready and marketed. CNN is a machine learning method in which the development of Multi-Layer Perceptron (MLP) is designed to process two-dimensional data. The purpose of this study is to classify the image of curly chili, cayenne and binoculars so that it becomes an input for warehousing to apply the CNN method in placing goods. Obtained the results of this study is the use of the best epoch value of 70 with 80%: 20% training testing dataset comparison scenarios and, obtained a classification accuracy of 70%, where, in the classification results of the predicted chili image according to the actual chili image only 10 image for curly chili and 7 image for cayenne image and 4 image for binocular chili image. Convolutional neural network ; Chili pepper ; Computer science ; Artificial intelligence ; Contextual image classification ; Perceptron ; Image (mathematics) ; Artificial neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses
Authors: Subramanian K. R. S. Sankaranarayanan | Sukriti Manna | Troy D. Loeffler | Rohit Batra | Suvo Banik | Henry Chan | Kiran Sasikumar | Michael Sternberg | Tom Peterka | Mathew J. Cherukara | Stephen K. Gray | Bobby G. Sumpter
Source: Research Square (preprint, 2021-03-13)
Field: Materials Science — Materials Chemistry

Learning in Continuous Action Space for Determination of High Dimensional Potential Energy Surfaces Action (physics) ; Space (punctuation) ; Energy (signal processing) ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Electron and X-Ray Spectroscopy Techniques