jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Sutton | Mario Boley | Luca M. Ghiringhelli | Matthias Rupp | Jilles Vreek | Matthias Scheffler
Source: ChemRxiv (preprint, 2019-09-09)
Field: Materials Science — Materials Chemistry

Identifying Domains of Applicability of Machine Learning Models for Materials Science We present an extension to the usual machine learning process that allows for the identification of the domain of applicability of a fitted model, i.e., the region in its domain where it performs most accurately. This approach is applied to several vastly different but commonly used materials representations (namely the n-gram approach, SOAP, and the many body tenor representation), which are practically indistinguishable based on performance using a single error statistic. Moreover, these models appear unsatisfactory for screening applications as they fail to reliably identify the ground state polymorphs. When applying our newly developed analysis for each of the models, we can identify the domain of applicability for each model according to a simple set of interpretable conditions. We show that identification of the domain of applicability in the prediction of the formation energy enables a more accurate ground-state search - a crucial step for the discovery of novel materials. Computer science ; Process (computing) ; Identification (biology) ; Statistic ; Domain (mathematical analysis) ; Representation (politics) ; Simple (philosophy) ; Machine learning ; Set (abstract data type) ; Artificial intelligence ; Extension (predicate logic) ; Algorithm ; Data mining ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Matthew L. Gong | Brandon Miller | Ray R. Unocic | Khallid Hattar | Bryan W. Reed | Dan Masiel | Tolga Taşdizen | Jeffery A. Aguiar
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction Journal Article Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction Get access Matthew L Gong, Matthew L Gong Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, Idaho USAUniversity of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, Utah USA Search for other works by this author on: Oxford Academic Google Scholar Brandon D Miller, Brandon D Miller Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, Idaho USA Search for other works by this author on: Oxford Academic Google Scholar Ray R Unocic, Ray R Unocic Oak Ridge National Laboratory, Center for Nanophase Materials Science, Oak Ridge, Tennessee USA Search for other works by this author on: Oxford Academic Google Scholar Khallid Hattar, Khallid Hattar Sandia National Laboratories, Center for Integrated Nanotechnologies Albuquerque, New Mexico USA Search for other works by this author on: Oxford Academic Google Scholar Bryan Reed, Bryan Reed Integrated Dynamic Electron Solutions, Pleasanton, California USA Search for other works by this author on: Oxford Academic Google Scholar Dan Masiel, Dan Masiel Integrated Dynamic Electron Solutions, Pleasanton, California USA Search for other works by this author on: Oxford Academic Google Scholar Tolga Tasdizen, Tolga Tasdizen University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, Utah USA Search for other works by this author on: Oxford Academic Google Scholar Jeffery A Aguiar Jeffery A Aguiar Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, Idaho USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 168–169, https://doi.org/10.1017/S1431927619001570 Published: 01 August 2019 Salt lake ; Oak Ridge National Laboratory ; Library science ; National laboratory ; Miller ; Engineering ; History ; Art history ; Engineering physics ; Computer science ; Geology ; Physics ; Paleontology ; Nuclear physics Machine Learning in Materials Science
Authors: Patrick Avery | Xiaoyu Wang | Davide Μ. Proserpio | Cormac Toher | Corey Oses | Eric Gossett | Stefano Curtarolo | Eva Zurek
Source: arXiv (Cornell University) (preprint, 2019-06-13)
Field: Materials Science — Materials Chemistry

Predicting Superhard Materials via a Machine Learning Informed\n Evolutionary Structure Search Good agreement was found between experimental Vickers hardnesses,\n$H_\\text{v}$, of a wide range of materials and those calculated by three\nmacroscopic hardness models that employ the shear and/or bulk moduli obtained\nfrom: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and\n(ii) a machine learning (ML) model trained on materials within the AFLOW\nrepository. Because $H_\\text{v}^\\text{ML} $ values can be quickly estimated,\nthey can be used in conjunction with an evolutionary search to predict stable,\nsuperhard materials. This methodology is implemented in the XtalOpt\nevolutionary algorithm. Each crystal is minimized to the nearest local minimum,\nand its Vickers hardness is computed via a linear relationship with the shear\nmodulus discovered by Teter. Both the energy/enthalpy and $H_\\text{v,\nTeter}^{\\text{ML}}$ are employed to determine a structure's fitness. This\nimplementation is applied towards the carbon system, and 43 new superhard\nphases are found. A topological analysis reveals that phases estimated to be\nslightly harder than diamond contain a substantial fraction of diamond and/or\nlonsdaleite.\n Vickers hardness test ; Diamond ; Materials science ; Shear modulus ; Moduli ; Elastic modulus ; Shear (geology) ; Energy (signal processing) ; Machine learning ; Computer science ; Physics ; Mathematics ; Microstructure ; Composite material ; Statistics Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Naoyuki Niitsu | Chizuru Sawabe | Masato Mitani | Hiroyuki Ishii | Nobuhiko Kobayashi | Midori Goto | Kei Hirose | Shigeaki Obata | Naofumi Nakayama | Shun Watanabe | Toshihiro Okamoto | Jun Takeya
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Machine learning assisted crystal structure analysis of organic semiconductors II Materials science ; Organic semiconductor ; Semiconductor ; Nanotechnology ; Crystallography ; Optoelectronics Machine Learning in Materials Science
Authors: Chris Patrick
Source: Scilight (article, 2019-08-22)
Field: Materials Science — Materials Chemistry

A new way to represent atomic species improves molecular machine learning models The creation of elemental modes to represent atomic species enables transfer learning and lowers computational cost, bringing chemical modeling closer to a single machine learning model that can be fine-tuned for different systems. Computer science ; Atomic model ; Molecular machine ; Machine learning ; Artificial intelligence ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yasuharu Okamoto
Source: ChemRxiv (preprint, 2019-03-19)
Field: Materials Science — Materials Chemistry

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials &lt;p&gt;High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au&lt;sub&gt;13&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; and Au&lt;sub&gt;11&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.&lt;/p&gt; Transferability ; Generalization ; Molecular dynamics ; Artificial neural network ; Chemistry ; Force field (fiction) ; Field (mathematics) ; Energy (signal processing) ; Physics ; Computer science ; Biological system ; Mathematics ; Computational chemistry ; Artificial intelligence ; Quantum mechanics ; Machine learning ; Mathematical analysis ; Pure mathematics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Satoshi Minamoto | Takuya Kadohira | Kaita Ito | Makoto Watanabe | Masahiko Demura
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Prediction Models Generation by Machine Learning for Structural Materials Performance by Utilizing the Mi System The Materials Integration (MI) System is a domestically developed system in the “Cross-ministerial Strategic Innovation Promotion Program” to analyze structural materials performance. The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment. One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically. Furthermore, we developed a web application (“MIREA”: MI REgression Analyzer) that enables us to build high versatile prediction models based on machine learning techniques by using the data from workflows. In order to keep affinity with the MI system, unification of data format, connecting MI vocabulary system in order to include metadata required for the models, hyper-parameters tuning in learning process to get proper model, reusability of the models in the MI system, etc. were devised in the MIREA software. Here, we construct prediction models by machine learning techniques with use of workflows consisting of high-precision elemental programs for important issues concerning the performance of structural materials such as fatigue life analysis and creep life analysis. The generated models enable us to reduce time to develop materials having required performance. Computer science ; Workflow ; Process (computing) ; Machine learning ; Software ; Metadata ; Performance prediction ; Construct (python library) ; Artificial intelligence ; Reusability ; Data mining ; Database ; Simulation ; Programming language ; Operating system Material Properties and Failure Mechanisms ; Smart Materials for Construction ; BIM and Construction Integration
Authors: Mohammad Atif Faiz Afzal | Aditya Sonpal | Mojtaba Haghighatlari | Andrew J. Schultz | Johannes Hachmann
Source: ChemRxiv (preprint, 2019-06-04)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density.We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations. Artificial neural network ; Process (computing) ; Computer science ; Work (physics) ; Organic molecules ; Throughput ; Artificial intelligence ; Machine learning ; Statistical physics ; Molecule ; Mechanical engineering ; Physics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Rafael Garcia‐Dias | Carlos Allende Prieto | J. Sánchez Alméida | P. A. Palicio
Source: arXiv (Cornell University) (preprint, 2019-07-30)
Field: Chemistry — Analytical Chemistry

Machine learning in APOGEE: Identification of stellar populations through chemical abundances The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. It is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations. We explore the possibility of using clustering algorithms to separate stellar populations with distinct chemical patterns. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our sample includes 18 stellar clusters with a total of 453 stars. We use statistical tests showing that some pairs of clusters are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances. We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters Cluster analysis ; Star cluster ; Stars ; Cluster (spacecraft) ; Galaxy ; Computer science ; Metric (unit) ; Identification (biology) ; Astrophysics ; Physics ; Pattern recognition (psychology) ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Stellar, planetary, and galactic studies
Authors: Wenyan Bi | Gaurav Kumar | Hendrikje Nienborg | Bei Xiao
Source: Journal of Vision (article, 2019-09-06)
Field: Materials Science — Polymers and Plastics

Understanding Information Processing Mechanisms for Estimating Material Properties of Cloth in Deep Neural Networks Two decades of research in material perception shows that humans can estimate material properties in diverse scenes. However, it still lacks a computational framework that explains these perceptual data. Deep neural network trained on large data has succeeded in object recognition and classification. Will it succeed in estimating material properties of objects in dynamic scenes? One challenge is that it is difficult to obtain clear-cut labels for material properties. In addition, there is no simple relationship between perceived material attributes with the physical measurements. Here we use physics-based cloth simulations as our database and train neural networks with a variety of architectures to understand the computational mechanism of estimating mechanical properties of cloth. We hypothesize that network architectures that matches best with human performance would likely to be adapted by the human visual system. The dataset contain 1764 animations containing moving clothes rendered with 7 bending stiffness values, 6 mass values, 6 textures and in 3 dynamic scenes. First, we finetune a pre-trained ResNET (CNN) to extract features for static frames of maximum deformations with ground-truth parameters as labels. Second, we connect a long-short-term memory network (LSTM) in series with the CNN to train videos with the same labels. Thirdly, we use MDS method to obtain perceptual clusters of these stimuli and used these clusters as labels to train the videos with the same architecture (CNN + LSTM). Finally, we built a two-stage network where the first network performs unsupervised learning to cluster data to get the labels, and the second network uses those labels and learns a discriminative model to classify the videos. We use fisher vectors generated from dense trajectories of videos to represent the data in the first network. We find that the two-stage network outperforms the other architectures and the results are closest to human perception. Computer science ; Artificial intelligence ; Artificial neural network ; Perception ; Deep learning ; Variety (cybernetics) ; Object (grammar) ; Ground truth ; Network architecture ; Pattern recognition (psychology) Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Aesthetic Perception and Analysis
Authors: 敦之 石川 | Atsushi Ishikawa
Source: Institutional Repositories DataBase (IRDB) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents Alkali metal ; Electrolyte ; Group (periodic table) ; Battery (electricity) ; Work (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; X-ray Diffraction in Crystallography
Authors: Amelia Solomon | Thomas L. Beck
Source: Undergraduate Scholarly Showcase (article, 2019-04-16)
Field: Chemistry — Physical and Theoretical Chemistry

Determining Partial Charge Distribution of a Molecule through Machine Learning By Amelia Solomon, Chemistry Advisor: Thomas Beck Presentation ID: AM_D37 Abstract: A partial charge of a molecule is used to determine the electrostatic potential of the force field surrounding a molecule. Partial charges also help determine the reactivity of a molecule and can provide a general understanding of the structure of the molecule. There are several properties that help determine partial charges: Electron density, dipole moments, or relationships between the physical properties. Molecular dynamic simulations are a convenient method for viewing the physical movements of these properties. Even with these methods, there is always a high level of uncertainty. And the same method will provide varying levels of accuracy for different molecules. Machine learning is a unique way to determine partial charges by letting the machine determine the best partial charge distribution for a molecule. The machine itself will provide a less biased method of charge development by deriving distribution directly from trends in quantum calculations. The goal is to use unsupervised machine learning to determine a learning set, a set of parameters, for the supervised machine learning program. Ideally, once this program is written, it will be able to analyze any size of molecule and determine a highly accurate partial charge distribution. Partial charge ; Charge (physics) ; Dipole ; Molecule ; Set (abstract data type) ; Charge density ; Artificial intelligence ; Computer science ; Machine learning ; Statistical physics ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Henry Chan | Badri Narayanan | Mathew J. Cherukara | Fatih G. Sen | Kiran Sasikumar | Stephen K. Gray | Maria K. Y. Chan | Subramanian K. R. S. Sankaranarayanan
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials. Molecular dynamics ; Characterization (materials science) ; Scalability ; Interatomic potential ; Flexibility (engineering) ; Atom (system on chip) ; Statistical physics ; Picosecond ; Chemistry ; Computer science ; Nanotechnology ; Physics ; Computational chemistry ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Hyuck-Mo Lee
Source:  (article, 2019-08-21)
Field: Materials Science — Materials Chemistry

High Throughput Screening of Alloy Catalysts for PEMFC and Machine Learning Prediction of Chemisorption Chemisorption ; Throughput ; Proton exchange membrane fuel cell ; Alloy ; Catalysis ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Metallurgy ; Operating system Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion