jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ekaterina Gracheva | G. Lambard | Sadaki Samitsu | Keitaro Sodeyama | Ayako Nakata
Source: Science and Technology of Advanced Materials Methods (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Prediction of the coefficient of linear thermal expansion for the amorphous homopolymers based on chemical structure using machine learning The coefficient of thermal expansion (CTE) is an industrially crucial macroscopic property of polymers. Yet, there is no structure-based model expressing it with sufficient accuracy. In this work, we present two data-driven predictive models for the linear CTE of amorphous homopolymers in the glassy state based solely on chemical structure, showing consistent predictions. The first model is built with the SMILES-X software and is based on the simplified molecular-input line-entry system (SMILES) of polymer’s repeating unit as input. The second model is built with a random forest trained on extended-connectivity fingerprints of repeating units. Both models are trained on 106 experimental data samples taken from the PoLyInfo database. The out-of-sample prediction shows a root-mean-square error of 2.65 ± 0.09 × 10<sup>–5</sup> K<sup>–1</sup> (2.58 ± 0.09 × 10<sup>–5</sup> K<sup>–1</sup>), a mean absolute error of 1.71 ± 0.06 × 10<sup>–5</sup> K<sup>–1</sup> (1.61 ± 0.06 × 10<sup>–5</sup> K<sup>–1</sup>) and a coefficient of determination of 0.62 ± 0.03 (0.64 ± 0.03) for SMILES-X (random forest). Additionally, the models are validated experimentally using a lab-prepared sample with good agreement (<i>p</i>-value≫for both models). The attention mechanism, incorporated into SMILES-X, points out salient SMILES substructures, and the resulting maps suggest that the model takes decisions on a chemically interpretable basis. <b>Abbreviations:</b> SMILES; CTE; CLTE; CVTE Thermal expansion ; Mean squared error ; Root mean square ; Amorphous solid ; Coefficient of determination ; Random forest ; Computer science ; Materials science ; Statistical physics ; Algorithm ; Mathematics ; Artificial intelligence ; Thermodynamics ; Chemistry ; Statistics ; Physics ; Machine learning ; Crystallography Polymer crystallization and properties ; Injection Molding Process and Properties
Authors: Tian Xie | Victor Bapst | Alexander L. Gaunt | Annette Obika | Trevor Back | Demis Hassabis | Pushmeet Kohli | James Kirkpatrick
Source: arXiv (Cornell University) (preprint, 2021-03-25)
Field: Materials Science — Materials Chemistry

Atomistic graph networks for experimental materials property prediction Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Materials Science is that experimental datasets of material properties tend to be small. In this work we show how material descriptors can be learned from the structures present in large scale datasets of material simulations; and how these descriptors can be used to improve the prediction of an experimental property, the energy of formation of a solid. The material descriptors are learned by training a Graph Neural Network to regress simulated formation energies from a material's atomistic structure. Using these learned features for experimental property predictions outperforms existing methods that are based solely on chemical composition. Moreover, we find that the advantage of our approach increases as the generalization requirements of the task are made more stringent, for example when limiting the amount of training data or when generalizing to unseen chemical spaces. Property (philosophy) ; Computer science ; Limiting ; Generalization ; Graph ; Artificial neural network ; Machine learning ; Artificial intelligence ; Material properties ; Task (project management) ; Theoretical computer science ; Materials science ; Mathematics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xiaoyong Cao | Pu Tian
Source: Preprints.org (preprint, 2021-01-05)
Field: Materials Science — Materials Chemistry

"Dividing and Conquering" and "Caching" in Molecular Modeling Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems. Computer science ; Granularity ; Metadynamics ; Discretization ; Theoretical computer science ; Divide and conquer algorithms ; Software portability ; Markov chain ; Computational science ; Algorithm ; Molecular dynamics ; Computational chemistry ; Mathematics ; Chemistry ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Gordon Koerner | Ramakrishna Surya | Kannappan Palaniappan | Prasad Calyam | Filiz Bunyak | Matthew R. Maschmann
Source: Volume 2B: Advanced Manufacturing (article, 2021-11-01)
Field: Materials Science — Surfaces, Coatings and Films

In-Situ Scanning Electron Microscope Chemical Vapor Deposition as a Platform for Nanomanufacturing Insights Abstract While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and ensemble properties remain poorly understood. Few experimental techniques exist to characterize and observe the growth and self-assembly processes in situ. Here we introduce the use of in-situ scanning electron microscope (SEM) synthesis based on chemical vapor deposition (CVD) processing. In this preliminary report, we share best practices for in-situ SEM CVD processing and initial CNT forest synthesis results. Image analysis techniques are developed to identify and track the movement of catalyst nanoparticles during synthesis conditions. Finally, a perspective is provided in which in-situ SEM observations represent one component of a larger system in which numerical simulation, machine learning, and digital control of experiments reduces the role of humans and human error in the exploration of CNT forest process-structure-property relationships. Nanomanufacturing ; Materials science ; Scanning electron microscope ; Carbon nanotube ; Chemical vapor deposition ; Nanotechnology ; Characterization (materials science) ; In situ ; Deposition (geology) ; Composite material ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Anodic Oxide Films and Nanostructures
Authors: Jonggul Lee | Jungho Shin | Tae-Wook Ko | Seunghee Lee | Hyunju Chang | YunKyong Hyon
Source: Materials Research Express (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Descriptors of atoms and structure information for predicting properties of crystalline materials Abstract Machine learning (ML) has increasingly been of interest in the design of new materials. However, it is still challenging to exploit an ML model in this field because its performance highly depends on the representation of materials, its properties, and the amount of data. In this study, for the cases of prediction of properties of crystalline materials, we explore a systematic comparison of two state-of-the-art frameworks: Crystal Graph Convolutional Neural Networks (CGCNNs) and the Sure Independence Screening and Sparsifying Operator (SISSO). The common key advantage of these two models is the fact that painstakingly handcrafted descriptors from simple material properties are not required. The main differences between the two models are (1) the use of structure information in the arbitrary size of compounds (CGCNN) and (2) limited interpretability (CGCNN) but simple and analytic relations between descriptor-property (SISSO). Using these two ML algorithms we evaluate the prediction performance on the target properties, which are band gap, formation energy, and elasticity of crystalline compounds in the database of Materials Project (MP). Moreover, to improve prediction of the properties of the materials without human bias in the selection of initial atomic features for the CGCNNs, we use Atom2Vec that provides atom representation obtained in an unsupervised manner from the materials. We also perform the predictions with the different sizes of training set to investigate the data-size dependency of the predictive models. According to the amount of dataset, the use of structural information, and the ability to identify the best descriptor with its interpretability, these algorithms showed different prediction performances. This result will enable researchers in materials discovery to gain appropriate choices and insights in various attempts to improve the prediction performance of crystalline materials’ properties. Interpretability ; Computer science ; Representation (politics) ; Exploit ; Graph ; Artificial intelligence ; Crystal structure prediction ; Machine learning ; Field (mathematics) ; Convolutional neural network ; Set (abstract data type) ; Data mining ; Theoretical computer science ; Crystal structure ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xu Zhao | Youzhi Luo | Xuan Zhang | Xinyi Xu | Yaochen Xie | Meng Liu | Kaleb Dickerson | Cheng Deng | Maho Nakata | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies show that when 3D molecular geometries, such as bond lengths and angles, are available, molecular property prediction tasks can be made more accurate. However, computing of 3D molecular geometries requires quantum calculations that are computationally prohibitive. For example, accurate calculation of 3D geometries of a small molecule requires hours of computing time using density functional theory (DFT). Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods. To make this feasible, we develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules derived from DFT. We also provide a set of software tools for data processing, splitting, training, and evaluation, etc. Specifically, we propose to assess the error and validity of predicted geometries using four metrics. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Experimental results show that, compared with generating 3D geometries with RDKit, our method can achieve comparable prediction accuracy but with much smaller computational costs. Our Molecule3D is available as a module of the MoleculeX software library (https://github.com/divelab/MoleculeX). Benchmark (surveying) ; Pairwise comparison ; Computer science ; Software ; Algorithm ; Set (abstract data type) ; Molecular graph ; Computational science ; Theoretical computer science ; Graph ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michael Hutchinson | Charline Le Lan | Sheheryar Zaidi | Emilien Dupont | Yee Whye Teh | Hyunjik Kim
Source: International Conference on Machine Learning (article, 2021-07-18)
Field: Materials Science — Materials Chemistry

LieTransformer: Equivariant Self-Attention for Lie Groups Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing. Such works have mostly focused on group equivariant convolutions, building on the result that group equivariant linear maps are necessarily convolutions. In this work, we extend the scope of the literature to self-attention, that is emerging as a prominent building block of deep learning models. We propose the LieTransformer, an architecture composed of LieSelfAttention layers that are equivariant to arbitrary Lie groups and their discrete subgroups. We demonstrate the generality of our approach by showing experimental results that are competitive to baseline methods on a wide range of tasks: shape counting on point clouds, molecular property regression and modelling particle trajectories under Hamiltonian dynamics. Equivariant map ; Lie group ; Generality ; Computer science ; Group (periodic table) ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Pure mathematics ; Algebra over a field ; Psychology ; Physics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Ayu Irie | Shogo Fukushima | Akihide Koura | Kohei Shimamura | Fuyuki Shimojo
Source: Journal of the Physical Society of Japan (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Reproduction of Melting and Crystallization of Sodium by Machine-Learning Interatomic Potential Based on Artificial Neural Networks The training requirements for machine-learning interatomic potential based on artificial neural networks (ANN) are investigated to reproduce melting and crystallization of sodium. Only when the virial stress tensor, as well as the potential energy and atomic forces, is considered in the training, the constructed ANN potential precisely mimics the temperature dependence of the phase behavior obtained by first-principles molecular dynamics simulations. The melting temperature is estimated from the Helmholtz free energy, which is calculated by thermodynamic integration using the ANN potential. This study also discusses the dependence of the obtained melting temperature on the system size and the number of sampling k points. Helmholtz free energy ; Crystallization ; Artificial neural network ; Thermodynamics ; Materials science ; Interatomic potential ; Molecular dynamics ; Virial theorem ; Melting temperature ; Thermodynamic integration ; Chemical physics ; Statistical physics ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Neural Networks and Applications
Authors: Jimy Oblitas | Jezreel Mejía | Miguel De‐la‐Torre | Himer Ávila-George | Lucía Seguí | Luis Mayor López | Albert Ibarz | Wilson Castro
Source: Applied Sciences (article, 2021-02-10)
Field: Chemistry — Analytical Chemistry

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods. Convolutional neural network ; Artificial intelligence ; Cucurbita pepo ; Pattern recognition (psychology) ; Classifier (UML) ; Computer science ; Artificial neural network ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: John A. Bacus
Source: 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) (article, 2021-08-27)
Field: Chemistry — Analytical Chemistry

Identification of Pork Meat Freshness Using Neural Networks Pork meat freshness level is a vital factor in determining its quality for consumption. Sensory testing, physical and chemical testing, microbiological testing, and instrument analysis are all examples of traditional detection procedures. The goal of this work is to use image analysis and deep learning to classify pork meat quality. The datasets were provided, and it consists of extracted features from the photos of various pork meats. The parameters are then classified using Neural Network model and Keras Classifier. Pork meat quality can be categorized as fresh, half-fresh and adulterated. After testing and conducting a statistical analysis, the system achieved an overall accuracy of 87.93 %. Artificial neural network ; Classifier (UML) ; Computer science ; Artificial intelligence ; Identification (biology) ; Quality (philosophy) ; Pattern recognition (psychology) ; Quality assessment ; Food science ; Engineering ; Evaluation methods ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Yan Yan | Xuelong Shi | Tao Zhou | Bowen Xu | Chen Li | Wei Yuan | Ying Gao | Bingyang Pan | Xuling Diao | Shoumian Chen | Yuhang Zhao
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2021-09-29)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning virtual SEM metrology and SEM-based OPC model methodology Background: E-beam metrologies, both critical dimension scanning electron microscope metrology and defect scan metrology, have been playing a very critical role in gating patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analyses. However, the low e-beam metrology tool throughput makes it impossible to obtain SEM images for larger area. Monte Carlo-based SEM image simulations or other SEM image simulations require postlithography or postetch pattern three-dimensional structures as prerequisite, and the simulation speed is not sufficiently fast for full chip implementation. Aim: We aim to develop machine learning SEM models with sufficient accuracy and speed for full chip application in semiconductor manufacturing environment. Approach: We have proposed a virtual SEM metrology solution based on U-Net neural network with physics-based feature maps as model input. With information in aerial image space encoded properly, SEM images of both postlithography and postetch can be predicted accurately enough for practical applications using our proposed virtual SEM metrology models. Equipped with GPUs, the machine learning-based SEM image models are fast enough to make it possible to realize full chip SEM generation from post-OPC data. Results: Our machine learning SEM image models can predict SEM images with normalized cross correlation around 0.95 in reference to ground truth SEM images, each SEM image (512 × 512 in size) takes about 800 ms using single CPU, and the speed can be accelerated to about 10 ms with single GPU. Conclusions: Using U-Net structure and physics-based feature maps as model inputs, machine learning-based SEM image models can be developed. The models are sufficiently accurate and fast to find their applications in semiconductor manufacturing, and they can be used as independent model for OPC data verification or generate SEM images as reference for SEM defect scan metrology. Metrology ; Feature (linguistics) ; Computer science ; Scanning electron microscope ; Artificial intelligence ; Critical dimension ; Dimensional metrology ; Chip ; Monte Carlo method ; Throughput ; Optics ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection