jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changhae Andrew Kim | Nathan D. Ricke | Troy Van Voorhis
Source: ChemRxiv (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning Dynamic Correlation in Chemical Kinetics The kinetics of surface reactions are often described using a lattice model. Since it is expensive to propagate the configuration probabilities of the entire lattice, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation (CME). Unfortunately, simple closures, such as the mean-field (MF) and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics, using the lattice Lotka-Volterra model (LLVM) as a model system. We trained feed-forward neural networks (FFNNs) on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the machine learning moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pre-trained closures that would enable researchers to extract useful insight at a fraction of the computational cost. Moment closure ; Lattice (music) ; Moment (physics) ; Monte Carlo method ; Statistical physics ; Closure (psychology) ; Computer science ; Mathematics ; Physics ; Thermodynamics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Authors: Luis M. Antunes | Ricardo Grau‐Crespo | Keith T. Butler
Source: arXiv (Cornell University) (preprint, 2021-07-30)
Field: Materials Science — Materials Chemistry

Distributed Representations of Atoms and Materials for Machine Learning The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, via pooling operations of distributed representations of atoms. These compound representations are evaluated on ten different tasks, such as the prediction of formation energy and band gap, and are found to be competitive with existing benchmarks that make use of structure, and even superior in cases where only composition is available. Finally, we introduce a new approach for learning distributed representations of atoms, named SkipAtom, which makes use of the growing information in materials structure databases. Pooling ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Changhae Andrew Kim | Nathan D. Ricke | Troy Van Voorhis
Source:  (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

Machine Learning Dynamic Correlation in Chemical Kinetics The kinetics of surface reactions are often described using a lattice model. Since it is expensive to propagate the configuration probabilities of the entire lattice, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation (CME). Unfortunately, simple closures, such as the mean-field (MF) and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics, using the lattice Lotka-Volterra model (LLVM) as a model system. We trained feed-forward neural networks (FFNNs) on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the machine learning moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pre-trained closures that would enable researchers to extract useful insight at a fraction of the computational cost. Moment closure ; Lattice (music) ; Statistical physics ; Monte Carlo method ; Moment (physics) ; Computer science ; Closure (psychology) ; Mathematics ; Physics ; Thermodynamics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Authors: Yabin Xu | Shengjie Sun | Zhuang Wu
Source:  (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning In order to speed up the development of new iron-base alloy materials and reduce the consumption of time and resources caused by a large number of experiments, a prediction method for Gibbs free energy of iron-base alloy materials was proposed based on the theory of material genetic engineering. Firstly, the collected data were preprocessed by splicing, filling, normalization and one-hot coding to adapt to the training of the model. Then, based on the DeepFM model, a fusion model based on Factorization Machine (FM), bitwise self-attention mechanism and Bi-directional Long Short-term Memory Network (Bi-LSTM) was proposed to predict the Gibbs free energy of iron-base alloy materials. It can not only extract the low-order and high-order features of the data effectively, but also the weight coefficients of each data feature can be reasonably optimized and the correlation between the data can be fully considered. The comparative experimental results show that the Gibbs free energy prediction method based on deep learning has a good prediction effect. It provides a new method to predict the Gibbs free energy of iron-base alloys. Gibbs free energy ; Computer science ; Artificial intelligence ; Alloy ; Energy consumption ; Machine learning ; Materials science ; Metallurgy ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Handwritten Text Recognition Techniques
Authors: Jiaming Song | Jianmin Han | Heng Bo Jiang
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Magnesium Alloy Corrosion Based on Machine Learning Method Corrosion ; Random forest ; Magnesium ; Support vector machine ; Materials science ; Gradient boosting ; Magnesium alloy ; Metallurgy ; Range (aeronautics) ; Alloy ; Machine learning ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Yi-Ming Tseng | Pei-Te Wang | Nan-Yow Chen | An-Cheng Yang | Nien‐Ti Tsou
Source: Research Square (preprint, 2021-01-26)
Field: Materials Science — Materials Chemistry

Martensite Variant Identification For Shape Memory Alloys By Using Graph Neural Networks Abstract Detailed microstructure evolution in shape memory alloys (SMAs) is typically studied by molecular dynamics (MD) simulations. However, the conventional post-processing tools for atomistic calculations, such as CNA and PTM, fail to identify distinct crystal variants and to reveal twin alignments in SMAs. In the current work, a powerful and efficient post-processing tool based on GraphSAGE neural network is developed, which can identify multiple phases in martensitic transformation, including the orthorhombic, monoclinic and R phases. Where the network was trained by the results of sets of temperatureand stress-induced martensitic transformation MD calculations. The accuracy and generality were also verified by the application to the cases which did not appear in the training dataset, such as unrecoverable nanoindentation process. The proposed method is rapid, accurate, and is ready to be integrated with any visualization tool for MD simulations. The outcome of the current work is expected to accelerate the pace of atomistic studies on SMAs and related materials. Martensite ; Shape-memory alloy ; Monoclinic crystal system ; Artificial neural network ; Orthorhombic crystal system ; Diffusionless transformation ; Molecular dynamics ; Nanoindentation ; Computer science ; Materials science ; Artificial intelligence ; Microstructure ; Crystallography ; Crystal structure ; Chemistry ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Microstructure and Mechanical Properties of Steels
Authors: Lili Li | Jiangwei Qiao | Jian Yao | Jie Li | Li Li
Source: Research Square (preprint, 2021-10-14)
Field: Chemistry — Analytical Chemistry

Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning Rapeseed ; Artificial intelligence ; Computer science ; Deep learning ; Computer vision ; Process engineering ; Agricultural engineering ; Environmental science ; Chemistry ; Engineering ; Food science Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Smart Agriculture and AI
Authors: Debjyoti Bhattacharya | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2021-02-24)
Field: Materials Science — Materials Chemistry

Deep Learning Order Parameter for Polymer Phase Transition We report a deep learning (DL) framework viz. deep autoencoder that autonomously discovers an appropriate order parameter from molecular dynamics (MD) simulation data to characterize the coil to globule phase transition of a polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a polymer in a one-dimensional feature space and, subsequently, decodes the one-dimensional feature to its original 3N dimensional polymer trajectory. The feature space representation of a polymer provides a new order parameter that accurately describes the coil to globule phase transition as a function of temperature. This method is very generic and extensible to identify flexible order parameters to characterize wide range of phase transitions that take place in polymers and other soft materials. Moreover, this MD-DL approach is computational very efficient than a pure MD based characterization of phase transition, and has potential implications in accelerating phase prediction. Autoencoder ; Trajectory ; Phase transition ; Feature (linguistics) ; Parameter space ; Representation (politics) ; Phase (matter) ; Polymer ; Deep learning ; Statistical physics ; Computer science ; Phase space ; Artificial intelligence ; Biological system ; Materials science ; Algorithm ; Physics ; Mathematics ; Condensed matter physics ; Thermodynamics ; Nuclear magnetic resonance ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Rishikesh Magar | Yuyang Wang | Cooper Lorsung | Liang Chen | Hariharan Ramasubramanian | Peiyuan Li | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2021-11-30)
Field: Materials Science — Materials Chemistry

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and Graph Neural Networks(GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem. Python (programming language) ; Computer science ; Artificial neural network ; Training set ; Artificial intelligence ; Machine learning ; Property (philosophy) ; Chemical database ; Cheminformatics ; Graph ; Fingerprint (computing) ; Data mining ; Programming language ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jonas Busk | Peter Bjørn Jørgensen | Arghya Bhowmik | Mikkel N. Schmidt | Ole Winther | Tejs Vegge
Source: arXiv (Cornell University) (preprint, 2021-07-13)
Field: Materials Science — Materials Chemistry

Calibrated Uncertainty for Molecular Property Prediction using Ensembles\n of Message Passing Neural Networks Data-driven methods based on machine learning have the potential to\naccelerate computational analysis of atomic structures. In this context,\nreliable uncertainty estimates are important for assessing confidence in\npredictions and enabling decision making. However, machine learning models can\nproduce badly calibrated uncertainty estimates and it is therefore crucial to\ndetect and handle uncertainty carefully. In this work we extend a message\npassing neural network designed specifically for predicting properties of\nmolecules and materials with a calibrated probabilistic predictive\ndistribution. The method presented in this paper differs from previous work by\nconsidering both aleatoric and epistemic uncertainty in a unified framework,\nand by recalibrating the predictive distribution on unseen data. Through\ncomputer experiments, we show that our approach results in accurate models for\npredicting molecular formation energies with well calibrated uncertainty in and\nout of the training data distribution on two public molecular benchmark\ndatasets, QM9 and PC9. The proposed method provides a general framework for\ntraining and evaluating neural network ensemble models that are able to produce\naccurate predictions of properties of molecules with well calibrated\nuncertainty estimates.\n Uncertainty quantification ; Probabilistic logic ; Benchmark (surveying) ; Artificial neural network ; Computer science ; Machine learning ; Artificial intelligence ; Context (archaeology) ; Property (philosophy) ; Data mining ; Calibration ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: X. Zhang | G. H. Teichert | Zijing Wang | M. Duschenes | Shweta Srivastava | E. Livingston | Holber, J. | Shojaei, M. Faghih | Sundararajan, A. | K. Garikipati
Source: arXiv (Cornell University) (preprint, 2021-12-09)
Field: Materials Science — Materials Chemistry

mechanoChemML: A software library for machine learning in computational materials physics We present mechanoChemML, a machine learning software library for computational materials physics. mechanoChemML is designed to function as an interface between platforms that are widely used for machine learning on one hand, and others for solution of partial differential equations-based models of physics. Of special interest here, and the focus of mechanoChemML, are applications to computational materials physics. These typically feature the coupled solution of material transport, reaction, phase transformation, mechanics, heat transport and electrochemistry. Central to the organization of mechanoChemML are machine learning workflows that arise in the context of data-driven computational materials physics. The mechanoChemML code structure is described, the machine learning workflows are laid out and their application to the solution of several problems in materials physics is outlined. Workflow ; Interface (matter) ; Context (archaeology) ; Software ; Computer science ; Focus (optics) ; Computational mechanics ; Function (biology) ; Artificial intelligence ; Computational science ; Programming language ; Physics ; Finite element method ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Eric Qu | Andrew M. Jimenez | Sanat K. Kumar | Kai Zhang
Source: ChemRxiv (preprint, 2021-01-05)
Field: Materials Science — Materials Chemistry

Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning &lt;p&gt;There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub.&lt;/p&gt; Deep learning ; Scattering ; Convolutional neural network ; Computer science ; Matrix (chemical analysis) ; Context (archaeology) ; Materials science ; Polymer ; Pipeline (software) ; Nanoparticle ; Artificial intelligence ; Nanotechnology ; Biological system ; Optics ; Physics ; Composite material Machine Learning in Materials Science
Authors: Henrik Levämäki | Ferenc Tasnádi | Davide G. Sangiovanni | L.J.S. Johnson | Rickard Armiento | Igor A. Abrikosov
Source: arXiv (Cornell University) (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

Predicting properties of hard-coating alloys using ab-initio and machine learning methods Accelerated design of novel hard coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models against the databases. In this work, we present a development of a heavily automated high-throughput workflow to build a database of industrially relevant hard coating materials, such as binary and ternary nitrides. We use Vienna Ab initio Simulation package as the density functional theory calculator and the high-throughput toolkit to automate the calculation workflow. We calculate and present results, including the elastic constants, one of the key materials parameter that determines mechanical properties of the coatings, for X(1-x)Y(x)N binary and ternary nitrides, where X,Y in {Al, Ti, Zr, Hf} and fraction x = 0, 1/4, 1/2, 3/4, 1. We explore ways for ML techniques to support and complement the designed databases. We find that the crystal graph convolutional neural network model trained on Materials Project data for ordered lattices has sufficient prediction accuracy for the disordered nitrides, suggesting that the existing databases provide important data for predicting mechanical properties of qualitatively different type of material systems, in our case hard coating alloys, not included in the original dataset. Ternary operation ; Workflow ; Ab initio ; Materials science ; Binary number ; Computer science ; Nitride ; Coating ; Computational science ; Database ; Nanotechnology ; Physics ; Mathematics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Lenz Fiedler | Karan Shah | Attila Cangi | Michael Bußmann
Source: RODARE (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Dataset and scripts for A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry This dataset contains additional data for the publication "A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry". Its goal is to enable interested people to reproduce the citation analysis carried out in the aforementioned publication. <strong>Prerequesites</strong> The following software versions were used for the python version of this dataset: Python: 3.8.6 Scholarly: 1.2.0 Pyzotero: 1.4.24 Numpy: 1.20.1 <strong>Contents</strong> results/ : Contains the .csv files that were the results of the citation analysis. Paper groupings follow the ones outlined in the publication. scripts/ : Contains scripts to perform the citation analysis. Zotero.cached.pkl : Contains the cached Zotero library. <strong>Usage</strong> In order to reproduce the results of the citation analysis, you can use citation_analysis.py in conjunction with cached Zotero library. Manual additions can be verified using the check_consistency script.<br> Please note that you will need a Tor key for the citation analysis, and access to our Zotero library if you don't want to use the cached version. If you need this access, simply contact us. Scripting language ; Density functional theory ; Computer science ; Artificial intelligence ; Data science ; Chemistry ; Cognitive science ; Psychology ; Programming language ; Computational chemistry Machine Learning in Materials Science