jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Wu | E Zhou | An Huang | Hongbin Zhang | Ming Hu | Guangzhao Qin
Source: Research Square (preprint, 2022-01-25)
Field: Materials Science — Materials Chemistry

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates Abstract High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m −1 K −1 ) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized [ Nature Electronics 4 , 416-423 (2021)]. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m −2 K −1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 µm. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems. Gallium arsenide ; Materials science ; Boron nitride ; Gallium nitride ; Boron ; Optoelectronics ; Nanotechnology ; Physics ; Nuclear physics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Advanced Thermoelectric Materials and Devices
Authors: Hang Du | Toshiyuki Yokosuka | Yuko Iwabuchi | Makoto Suzuki | Yasuko Aoki
Source: Metrology, Inspection, and Process Control XXXVI (article, 2022-04-22)
Field: Materials Science — Surfaces, Coatings and Films

Fast generation of SEM images including charging effect by neural network In scanning electron microscope (SEM) image simulation, it is necessary to consider the charging of electron beam irradiation, which can be computationally intensive. Therefore, we have developed a neural network-based algorithm to generate SEM images by inputting the shape and material properties of semiconductor devices, after which various preprocesses are applied to the physical parameters to improve the accuracy. The contrast and visibility of the generated images are then compared with simulation results that are not included in the training dataset. As a preliminary result, we found that the physical parameters that affect charging, such as the relative permittivity and electron mobility of the material, can be predicted. The effect of acceptance is also considered in the training process to reproduce the changes in image quality depending on the type and arrangement of detectors. Visibility ; Scanning electron microscope ; Artificial neural network ; Computer science ; Permittivity ; Process (computing) ; Detector ; Contrast (vision) ; Artificial intelligence ; Cathode ray ; Image (mathematics) ; Materials science ; Electron ; Computer vision ; Optics ; Optoelectronics ; Dielectric ; Physics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Surface Roughness and Optical Measurements
Authors: James L. McDonagh | Stamatia Zavitsanou | Alexander Harrison | Dimitry Zubarev | Benjamin H. Wunsch | Theordore van Kessel | Flaviu Cipcigan
Source: ChemRxiv (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

CHEMICAL SPACE ANALYSIS AND PROPERTY PREDICTION FOR CARBON CAPTURE AMINE MOLECULES Carbon capture and storage is part of the roadmap towards net zero for many countries around the world, since emissions from existing infrastructure are close to estimated carbon budgets. To address this problem, currently 87 carbon capture projects are proposed worldwide in the next 10 years. A major class of commercial carbon capture technology involves capture systems using solvents. Commonly carbon capture solvents feature blends of amines and water. Whilst these blends have proved valuable there is an increasing need to identify new candidate molecules which are more efficient and improve performance. Systematic approaches to improve on the current technology are now needed with increasing urgency to expedite the introduction of cutting edge carbon capture methods. Here, we present a chemical space analysis of amines and carbon capture usage. We proceed to show a framework for computational screening relevant to carbon capture solvents. We demonstrate the use of cloud computing, novel molecular representations and machine learning to screen potential candidates. We show the utility of machine learning in this field for high throughput virtual screening with an exemplar application to absorption capacity classification. Additionally, we highlight the need for improved data awareness and accessibility to enable this field to advance at a pace commensurate to its global importance. Our research brings together multiple methods and domains of expertise to accelerate the discovery of carbon capture solvents. Carbon capture and storage (timeline) ; Chemical space ; Pace ; Carbon fibers ; Computer science ; Greenhouse gas ; Space (punctuation) ; Field (mathematics) ; Data science ; Chemistry ; Climate change ; Drug discovery ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Gucciardi Arnaud | Umberto Michelucci | Francesca Venturini | Michela Sperti | Marco A. Deriu
Source:  (article, 2022-03-30)
Field: Chemistry — Analytical Chemistry

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond-CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics. Olive oil ; Artificial neural network ; Convolutional neural network ; Organoleptic ; Computer science ; Process engineering ; Quality (philosophy) ; Artificial intelligence ; Food quality ; Environmental science ; Pattern recognition (psychology) ; Biological system ; Engineering ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Meng Li | Dong Ding
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Proton-Conducting Perovskites through Density Functional Theory and Machine Learning Hydrogen is an important energy carrier resource in response to limiting greenhouse gas emissions. Proton-conducting perovskite oxide is one of the key materials for highly efficient carbon-neutral hydrogen technologies, such as hydrogen production, CO 2 hydrogenation, and ammonia synthesis. Many attempts have been made based on doped perovskites made of well-tested materials, such as BaZrO 3 , BaCeO 3 , BaHfO 3 , BaTiO 3 , and SrZrO 3 . However, the resulting perovskites have often suffered stability and conductivity problems. Furthermore, complex phenomena occurring during hydration present challenges for expanding the materials library. Herein, we demonstrate accelerated discovery of proton-conducting perovskites with high conductivity using machine learning (ML) predictions. We constructed consistent training data using density functional theory (DFT) which enable high accuracy of ML model. DFT computations were performed on &gt; 1000 doped perovskite compositions to get their properties of lattice parameters, point defects (e.g., O vacancies, H interstitials), density of states, hydration energy, and proton migration energy. Several ML algorithms including Linear Regression, Bayesian Ridge Regression, Random Forest Regression, Neural networks, and k-Nearest Neighbor were tested for minimum errors and coefficient of determination. The multidimensional relationships between a set of &gt;50 features and conductivity were mapped out using the optimized ML model. We screened a large material space of A-site and B-site doped perovskites to predict potential proton-conducting materials for various energy applications. The outcomes are promising for accelerating the design and applications of proton-conducting perovskite oxides in hydrogen technologies. Perovskite (structure) ; Density functional theory ; Proton ; Materials science ; Graphene ; Conductivity ; Machine learning ; Computer science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: Behzad Behnia | Romana Sarkar Lopa
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Use of Spiral Cracking Patterns in Fracture Characterization of Soft Adhesive Materials Cracking ; Spiral (railway) ; Materials science ; Adhesive ; Characterization (materials science) ; Fracture (geology) ; Composite material ; Forensic engineering ; Engineering ; Nanotechnology ; Mechanical engineering Material Properties and Failure Mechanisms ; High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites
Authors: Beibei An | Guanyao Yu | Jiayang Li | Yuanyuan Li | Li Wang | Jinglai Zhang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Influence of Uncoordinated N Content on Ni-N-C Electrocatalyst for Co2 Reduction: Combining First Principle With Machine Learning Electrocatalyst ; Content (measure theory) ; Reduction (mathematics) ; Psychology ; Computer science ; Chemistry ; Business ; Mathematics ; Electrochemistry ; Physical chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Lei Fang | Junren Li | Ming Zhao | Tan Li | Jian–Guang Lou
Source: arXiv (Cornell University) (preprint, 2022-04-12)
Field: Materials Science — Materials Chemistry

Leveraging Reaction-aware Substructures for Retrosynthesis Analysis Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generation or machine translation models in natural language processing were frequently utilized approaches. The token-by-token decoding approach is not intuitive from a chemistry perspective because some substructures are relatively stable and remain unchanged during reactions. In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach. Our approach achieved improvement over previously reported models, and we find that the performance can be further boosted if the accuracy of substructure extraction is improved. The substructures extracted by our approach can provide users with better insights for decision-making compared to existing methods. We hope this work will generate interest in this fast growing and highly interdisciplinary area on retrosynthesis prediction and other related topics. Security token ; Retrosynthetic analysis ; Computer science ; Task (project management) ; Machine translation ; Decoding methods ; Substructure ; Generative grammar ; Artificial intelligence ; Perspective (graphical) ; Natural language processing ; Machine learning ; Engineering ; Algorithm ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Marcin Abram | Keith Burghardt | Greg Ver Steeg | Aram Galstyan | Rémi Dingreville
Source: arXiv (Cornell University) (preprint, 2022-03-19)
Field: Materials Science — Materials Chemistry

Inferring topological transitions in pattern-forming processes with self-supervised learning The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains. Unfortunately, relevant microstructure transitions may depend on process parameters in subtle and complex ways that are not captured by the classic theory of phase transition. While supervised machine learning methods may be useful for identifying transition regimes, they need labels which require prior knowledge of order parameters or relevant structures describing these transitions. Motivated by the universality principle for dynamical systems, we instead use a self-supervised approach to solve the inverse problem of predicting process parameters from observed microstructures using neural networks. This approach does not require predefined, labeled data about the different classes of microstructural patterns or about the target task of predicting microstructure transitions. We show that the difficulty of performing the inverse-problem prediction task is related to the goal of discovering microstructure regimes, because qualitative changes in microstructural patterns correspond to changes in uncertainty predictions for our self-supervised problem. We demonstrate the value of our approach by automatically discovering transitions in microstructural regimes in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of concentration modulations of binary alloys during physical vapor deposition of thin films. This approach opens a promising path forward for discovering and understanding unseen or hard-to-discern transition regimes, and ultimately for controlling complex pattern-forming processes. Artificial intelligence ; Phase transition ; Computer science ; Microstructure ; Universality (dynamical systems) ; Process (computing) ; Spinodal decomposition ; Task (project management) ; Statistical physics ; Materials science ; Machine learning ; Physics ; Phase (matter) ; Engineering ; Condensed matter physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Microstructure and Mechanical Properties of Steels
Authors: Vandermause , Jonathan
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Active Learning of Bayesian Force Fields Simulating matter at the atomistic scale can accelerate drug design and materials discovery, but the most accurate atomistic simulation methods are prohibitively expensive. In the past decade, machine learning (ML) has emerged as a powerful tool for combining the computational efficiency of classical force fields with the accuracy of quantum-mechanical methods such as density functional theory (DFT). However, most modern ML force fields are difficult to train, requiring thousands of expensive DFT calculations and detailed prior knowledge of the material of interest. In this thesis, I present a closed-loop Bayesian inference method that automates the training of many-body ML force fields using structures drawn "on the fly" from molecular dynamics simulations. Our online active learning algorithm uses the internal uncertainty of a Gaussian process (GP) regression model to decide whether to accept the model prediction or to perform a DFT calculation that updates the training set of the model. To enable large-scale molecular dynamics simulations with the resulting force fields, mean predictions of the GP are mapped onto much faster spline-based and parametric models. I discuss applications to superionic diffusion in silver iodide, hydrogen chemisorption on platinum, and martensitic phase transitions in the shape-memory alloy nickel titanium. The method is made available in an open-source software package called FLARE (Fast Learning of Atomistic Rare Events). Force field (fiction) ; Molecular dynamics ; Gaussian process ; Kriging ; Bayesian optimization ; Active learning (machine learning) ; Field (mathematics) ; Bayesian probability ; Bayesian inference Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: J. Moriguchi | K. Saitoh | K. Nishimura | T. Sato | M. Takuma | Y. Takahashi
Source: 15th World Congress on Computational Mechanics (WCCM-XV) and 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Constructing Machine-learned Interatomic Potentials for Covalent Bonding Materials and MD Analyses of Dislocation and Surface As machine learning potentials for molecular dynamics (MD) simulations, Spectral Neighbor Analysis Potential (SNAP) and quadratic SNAP (qSNAP) were constructed for silicon (Si) and silicon carbide (SiC). The reproducibility of the basic material properties about perfect crystal, free surface and dislocation cores in Si and 3C-SiC was investigated. The coefficients of SNAP and qSNAP were optimized using liner regression to present energy and force obtained by DFT. In addition, hyperparameters (cutoff length and weights for optimization, here) were determined using genetic algorithm to reproduce elastic moduli obtained by DFT. Lattice constant and elastic moduli of Si crystal by MD using our SNAP or qSNAP agree well with the values of DFT, and they have higher accuracy than those by any empirical potential. Additionally, melting point and specific heat at constant pressure were calculated by MD correctly. Especially in qSNAP of Si, the surface energy of {100} and {111} planes and the reconstructed {100} surface structure were almost reproduced. For 3C-SiC, SNAP reproduces lattice constant and elastic moduli of DFT. Furthermore, edge dislocation cores were generated successfully. However, the potentials we constructed have insufficient reproducibility in the plastic region, so it is necessary to continue development. Materials science ; Lattice constant ; Interatomic potential ; Silicon ; Condensed matter physics ; Dislocation ; Molecular dynamics ; Molecular physics ; Computational chemistry ; Composite material ; Chemistry ; Physics ; Optics ; Diffraction Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Semiconductor materials and interfaces
Authors: Zheren Wang | Kevin Cruse | Yuxing Fei | Ann Chia | Yan Zeng | Haoyan Huo | Tanjin He | Bowen Deng | Olga Kononova | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2022-01-23)
Field: Materials Science — Materials Chemistry

ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols Applying AI power to predict syntheses of novel materials requires high-quality, large-scale datasets. Extraction of synthesis information from scientific publications is still challenging, especially for extracting synthesis actions, because of the lack of a comprehensive labeled dataset using a solid, robust, and well-established ontology for describing synthesis procedures. In this work, we propose the first Unified Language of Synthesis Actions (ULSA) for describing ceramics synthesis procedures. We created a dataset of 3,040 synthesis procedures annotated by domain experts according to the proposed ULSA scheme. To demonstrate the capabilities of ULSA, we built a neural network-based model to map arbitrary ceramics synthesis paragraphs into ULSA and used it to construct synthesis flowcharts for synthesis procedures. Analysis for the flowcharts showed that (a) ULSA covers essential vocabulary used by researchers when describing synthesis procedures and (b) it can capture important features of synthesis protocols. This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis. Computer science ; Vocabulary ; Construct (python library) ; Ontology ; Representation (politics) ; Domain (mathematical analysis) ; Program synthesis ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Data Quality and Management
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Zhishang Luo | Francesco Paesani
Source: ChemRxiv (preprint, 2022-11-28)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the physics of many-body interactions? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Artificial neural network ; Process (computing) ; Scale (ratio) ; Artificial intelligence ; Range (aeronautics) ; State (computer science) ; Liquid water ; Statistical physics ; Physics ; Algorithm ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum, superfluid, helium dynamics