jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joshua A. Schiller | Ricardo J. Toro | Aagam Shah | Mitisha Surana | Kaihao Zhang | Matthew J. Robertson | Kristina M. Miller | Kevin Cruse | Kevin Liu | Bomsaerah Seong | Seol Chae | Ian Foster | Ben Blaiszik | Ben Galewsky | Darren Adams | Daniel S. Katz | Placid M. Ferreira | Elif Ertekin | Sameh Tawfick
Source:  (preprint, 2021-08-31)
Field: Materials Science — Materials Chemistry

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally-friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials, and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, “Graphene – Recipes for synthesis of high quality material” (Gr-ResQ: pronounced graphene rescue), that includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures ∼300 parameters ranging from synthesis conditions like catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility (MDF), which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control, and open opportunities for advancing not only graphene, but also many other 1D and 2D materials. Graphene ; Nanotechnology ; Chemical vapor deposition ; Materials science ; Raman spectroscopy ; Process engineering ; Computer science ; Engineering ; Physics Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Kasra Asnaashari | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2021-07-10)
Field: Materials Science — Materials Chemistry

Gradient domain machine learning with complex kernels: improving the accuracy of PES and force fields for large molecules The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be generally improved either by increasing the number of training points or by improving the models. In order to build accurate models based on expensive high-level ab initio calculations, much of recent work has focused on the latter. In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for high-dimensional molecular systems with a small number of ab initio calculations. The present work extends GDML to models with complex kernels built to maximize inference from a small number of molecular geometries. We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric. We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors. The results are presented for ethanol, uracil, malonaldehyde and aspirin. For aspirin, the model with complex kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57-dimensional PES with the mean absolute accuracy 0.177 kcal/mol (61.9 cm$^{-1}$) and FFs with the mean absolute error 0.457 kcal/mol A$^{-1}$. Force field (fiction) ; Ab initio ; Computer science ; Generalization ; Metric (unit) ; Algorithm ; Kernel (algebra) ; Artificial intelligence ; Machine learning ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kōji Shimizu | Satoshi Watanabe
Source: The Brain & Neural Networks (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Applications of Interatomic Potentials Using Neural Network in Materials Science ビッグデータや機械学習の活用が注目を浴びているという点について，著者らが専門とする物性物理学や材料科学の分野も例外ではない．世界各所でデータベース作成の取り組みが加速しているが，その背景には，密度汎関数理論に基づく第一原理計算によりミクロな物理量を精度良く予測可能になったことがある．他方，材料中の欠陥やイオンの挙動，表面や界面で起こる現象，非晶質材料など，多くの興味ある系での第一原理計算には非常に高い計算コストが必要となる．この計算コストと精度との両立という問題の解決にも機械学習手法の活用が注目されている．著者らは，ニューラルネットワークを用いた機械学習によって原子間の相互作用を第一原理計算と同等の精度で予測できると期待される，高次元ニューラルネットワークポテンシャルという手法を用いた材料研究を進めてきた．本稿では，著者らの応用事例を中心に，このニューラルネットワークポテンシャルを用いた材料科学研究について紹介する． Computer science ; Artificial neural network ; Interatomic potential ; Computational science ; Artificial intelligence ; Computational chemistry ; Molecular dynamics ; Chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Niek Buurma
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: DNA: coding blocks for biocompatible assembly &amp; disassembly This talk forms part of the ML4MC (Machine Learning for Materials and Chemicals Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Directed Assembly Network. This series ran over summer 2021 and covers topics that encompass our overlapping Network interests of AI, Machine Learning, Artificial Photosynthesis, Biomimetic Materials, Crystal Design &amp; Engineering, Materials, Molecules, Photochemistry, Photocatalysis and Supramolecular Chemistry. This video was the second talk in the ML4MC series and formed part of the session "Research Talks". Biocompatible material ; Artificial photosynthesis ; Session (web analytics) ; Coding (social sciences) ; Artificial light ; Computer science ; Nanotechnology ; Joint venture ; Supramolecular chemistry ; Photocatalysis ; Engineering ; Chemistry ; Materials science ; Molecule ; World Wide Web Machine Learning in Materials Science
Authors: Lyndon Emsley
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Chemistry — Spectroscopy

Structure determination of amorphous molecular solids by NMR crystallography Structure elucidation of amorphous molecular solids presents one of the key challenges in chemistry today.Knowledge of the atomiclevel structure in such materials would be of great value for example to direct the optimisation of pharmaceutical formulations.However, the lack of long-range structural order prevents atomic-level characterization of these materials using methods like single crystal X-Ray diffraction.Solid state NMR on the other hand yields atomic-level information on amorphous materials and molecular dynamics (MD) can generate candidate sets of possible disordered structures.Directly relating these two techniques has been out of reach so far, due to the prohibitive cost of computing chemical shifts on large ensembles of large MD structures using DFT.Recently, a method based on machine learning, dubbed ShiftML (1), has emerged as a quick and accurate way to predict the chemical shifts of organic solids, even for large systems.Here, using a machine learning model of chemical shifts, we determine the complete atomic-level structure of the amorphous form of a drug molecule by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems (2).From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular interaction energies. Amorphous solid ; Intermolecular force ; Solid-state nuclear magnetic resonance ; Chemical shift ; Molecule ; Molecular dynamics ; Molecular solid ; Chemical physics ; Crystal structure prediction ; Characterization (materials science) ; Crystal structure ; Chemistry ; Materials science ; Crystallography ; Computational chemistry ; Nanotechnology ; Physics ; Physical chemistry ; Organic chemistry ; Nuclear magnetic resonance Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Keith T. Butler
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Calibrated deep representations and entropy based active learning for materials property prediction This talk forms part of the ML4MC (Machine Learning for Materials and Chemicals Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Directed Assembly Network. This series ran over summer 2021 and covers topics that encompass our overlapping Network interests of AI, Machine Learning, Artificial Photosynthesis, Biomimetic Materials, Crystal Design &amp; Engineering, Materials, Molecules, Photochemistry, Photocatalysis and Supramolecular Chemistry. This video was the ninth talk in the ML4MC series and formed part of the session "Mentor Talks". Ninth ; Artificial intelligence ; Computer science ; Deep learning ; Series (stratigraphy) ; Supramolecular chemistry ; Chemistry ; Crystal structure ; Geology ; Paleontology Machine Learning in Materials Science
Authors: Takuma Wakiya | Yoshinobu Kamakura | Hiroki Shibahara | Kazuyoshi Ogasawara | Akinori Saeki | Ryosuke Nishikubo | Akihiro Inokuchi | Hirofumi Yoshikawa | Daisuke Tanaka
Source: Angewandte Chemie (article, 2021-10-11)
Field: Materials Science — Materials Chemistry

Frontispiz: Machine‐Learning‐Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons Kristall-Engineering Die durch maschinelles Lernen unterstützte Synthese von halbleitenden Silberthiolat-Koordinationspolymeren mit segregierten Transportpfaden für Löcher und Elektronen wird von Daisuke Tanaka et al. im Forschungsartikel auf S. 23405 vorgestellt. Polymer ; Electron ; Materials science ; Coordination polymer ; Nanotechnology ; Physics ; Composite material Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Julio C. Aguilar | Laura F. Sandoval | Arturo Martínez Rodríguez | Sanjay Shantha-Kumar | Jose Terrazas | Richard Adansi | Vinod Kumar | Arturo Bronson
Source:  (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed Abstract In seeking predictability of characterizing materials for ultra-high temperature materials for hypersonic vehicles, the use of the convolutional neural network for characterizing the behavior of liquid Al-Sm-X (Hf, Zr, Ti) alloys within a B4C packed to determine the reaction products for which they are usually done with the scanning electron microscope (SEM) or X-ray diffraction (XRD) at ultra-high temperatures (&amp;gt; 1600°C). Our goal is to predict ultimately the products as liquid Al-Sm-X (Hf, Zr, Ti) alloys infiltrate into a B4C packed bed. Material characterization determines the processing path and final species from the reacting infusion consisting of fluid flow through porous channels, consumption of elemental components, and reaction forming boride and carbide precipitates. Since characterization is time-consuming, an expert in this field is required; our approach is to characterize and track these species using a Convolutional Neural Network (CNN) to facilitate and automate analysis of images. Although Deep Learning seems to provide an automated prediction approach, some of these challenges faced under this research are difficult to overcome. These challenges include data required, accuracy, training time, and computational cost requirements for a CNN. Our approach was to perform experiments on high-temperature metal infusion under B4C Packed Bed infiltration in a parametric matrix of cases. We characterized images using SEM and XRD images and run/optimize our CNN, which yields an innovative method for characterization via Deep Learning compared to traditional practices. Convolutional neural network ; Characterization (materials science) ; Materials science ; Scanning electron microscope ; Boron carbide ; Deep learning ; Porosity ; Artificial intelligence ; Artificial neural network ; Parametric statistics ; Computer science ; Boride ; Process engineering ; Mechanical engineering ; Composite material ; Nanotechnology ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Advanced materials and composites
Authors: K. Tsugawa
Source: Atomic Energy Society of Japan (article, 2021-07-16)
Field: Materials Science — Materials Chemistry

Behaviors of Vacancy Clusters in Zr by Machine Learning Molecular Dynamics Vacancy defect ; Molecular dynamics ; Materials science ; Chemical physics ; Dynamics (music) ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Condensed matter physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Chunhui Li | Benjamin Gilbert | Steven Farrell | Piotr Zarzycki
Source: Goldschmidt2021 abstracts (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep neural network-based surrogates for Surface Complexation Models of oxide/electrolyte interfaces Electrolyte ; Artificial neural network ; Computer science ; Oxide ; Artificial intelligence ; Materials science ; Chemistry ; Electrode ; Physical chemistry ; Metallurgy Machine Learning in Materials Science
Authors: A. Ghaderi | Vahid Morovati | Amir Bahrololoumi | Roozbeh Dargazany
Source:  (preprint, 2021-07-16)
Field: Materials Science — Polymers and Plastics

A Phyics-Informed Neural Network Constitutive Model for Cross-Linked Polymers Artificial neural network ; Constitutive equation ; Computer science ; Artificial intelligence ; Engineering ; Finite element method ; Structural engineering Polymer crystallization and properties ; Elasticity and Material Modeling ; Rheology and Fluid Dynamics Studies
Authors: Meredith Fore
Source: Scilight (article, 2021-02-02)
Field: Materials Science — Materials Chemistry

Machine learning techniques provide insight into materials with competing order parameters Atomically disordered systems are extremely complicated to model, but a new probabilistic algorithm allows them to be quantitatively analyzed. This could lead to the discovery of new physics. Probabilistic logic ; Order (exchange) ; Computer science ; Lead (geology) ; Statistical physics ; Machine learning ; Artificial intelligence ; Physics ; Geology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Huziel E. Sauceda | Luis E. Gálvez-González | Stefan Chmiela | Lauro Oliver Paz‐Borbón | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

BIGDML: Efficient Gradient-Domain Machine Learning Force Fields for Materials Domain (mathematical analysis) ; Computer science ; Mathematical analysis ; Mathematics Machine Learning in Materials Science
Authors: Mingda Li
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Machine learning augmented neutron and x-ray scattering for quantum materials Neutron scattering ; Small-angle neutron scattering ; Scattering ; Neutron ; X-ray ; Quantum ; Materials science ; Nuclear physics ; Physics ; Nuclear engineering ; Optics ; Quantum mechanics ; Engineering Machine Learning in Materials Science
Authors: Nikita Yu. Serov | Vladimir V. Vinogradov
Source: ChemRxiv (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection Nanomaterials of various morphologies and chemistry have an extensive use &lt;a&gt;as photonic devices, advanced catalysts, sorbents for water purification, agrochemicals, platforms for drug delivery&lt;/a&gt; as well as imaging systems to name a few. However, search for synthesis routes giving custom nanomaterials for particular needs with the desired structure, shape, and size remains a challenge and is often implemented by manual research articles screening. Here, we develop for the first time scanning and transmission electron microscopy (SEM/TEM) reverse image search and hand drawing-based search &lt;i&gt;via&lt;/i&gt; transfer learning (TL), namely, VGG16 convolutional neural network (CNN) repurposing for image features extraction and subsequent image similarity determination. Moreover, we demonstrate case use of this platform on calcium carbonate system, where sufficient amount of data was acquired by random high throughput multiparametric synthesis, as well as on Au nanoparticles (NPs) data extracted from the articles. This approach can be not only used for advanced nanomaterials search and synthesis procedure verification, but also can be further combined with machine learning (ML) solutions to provide data-driven novel nanomaterials discovery. Nanomaterials ; Convolutional neural network ; Computer science ; Nanotechnology ; Nanoparticle ; Artificial intelligence ; Materials science Machine Learning in Materials Science
Authors: Hossein Mirhosseini | Hossein Tahmasbi | Sai Ram Kuchana | S. Alireza Ghasemi | Thomas D. Kühne
Source: Computational Materials Science (preprint, 2021-05-16)
Field: Materials Science — Materials Chemistry

An automated approach for developing neural network interatomic potentials with FLAME Artificial neural network ; Interatomic potential ; Reliability (semiconductor) ; Computer science ; Process (computing) ; Training set ; Quality (philosophy) ; Artificial intelligence ; Work (physics) ; Ab initio ; Machine learning ; Molecular dynamics ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Yiqun Wang | Xiaojie Zhang | Fei Xia | Elsa Olivetti | Ram Seshadri | James M. Rondinelli
Source: arXiv (Cornell University) (preprint, 2021-01-05)
Field: Materials Science — Materials Chemistry

Learning the Crystal Structure Genome for Property Classifications. Materials property predictions have improved from advances in machine-learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely on featurization of materials composition, however, whether the exclusive use of structural knowledge in such models has the capacity to make comparable predictions remains unknown. Here we employ a deep neural network (DNN) model, deepKNet, to learn structure-property relationships in crystalline materials without explicit chemical compositions, focusing on classification of crystal systems, mechanical elasticity, electrical behavior, and phase stability. The deepKNet model utilizes a three-dimensional (3D) momentum space representation of structure from elastic X-ray scattering theory and simultaneously exhibits rotation and permutation invariance. We find that the spatial symmetry of the 3D point cloud, which reflects crystalline symmetry operations, is more important than the point intensities contained within, which correspond to various planar electron densities, for making a successful metal-insulator classification. In contrast, the intensities are more important for predicting bulk moduli. Phase stability, however, relies more upon chemical composition information, where our structure-based model exhibits limited predictability. We find learning the materials structural genome in the form of a chemistry-agnostic DNN demonstrates that some crystal structures inherently host high propensities for optimal materials properties, which enables the decoupling of structure and composition for future co-design of multifunctionality. Crystal structure ; Computer science ; Artificial intelligence ; Statistical physics ; Stability (learning theory) ; Materials science ; Algorithm ; Biological system ; Physics ; Machine learning ; Crystallography ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hossein Mirhosseini | Hossein Tahmasbi | Sai Ram Kuchana | S. Alireza Ghasemi | Thomas D. Kühne
Source: arXiv (Cornell University) (preprint, 2021-02-08)
Field: Materials Science — Materials Chemistry

An automated approach for developing neural network interatomic\n potentials with FLAME The performance of machine learning interatomic potentials relies on the\nquality of the training dataset. In this work, we present an approach for\ngenerating diverse and representative training data points which initiates with\n\\it{ab initio} calculations for bulk structures. The data generation and\npotential construction further proceed side-by-side in a cyclic process of\ntraining the neural network and crystal structure prediction based on the\ndeveloped interatomic potentials. All steps of the data generation and\npotential development are performed with minimal human intervention. We show\nthe reliability of our approach by assessing the performance of neural network\npotentials developed for two inorganic systems.\n Artificial neural network ; Interatomic potential ; Reliability (semiconductor) ; Computer science ; Process (computing) ; Training set ; Artificial intelligence ; Quality (philosophy) ; Work (physics) ; Ab initio ; Machine learning ; Computational chemistry ; Molecular dynamics ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science