jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryan Noraas | Vasisht Venkatesh | Luke Rettberg | Nagendra Somanath
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Recognition and Quantification of Dual Phase Titanium Alloy Microstructures Using Convolutional Neural Networks Recent advances in machine learning and image recognition tools/methods are being used to address fundamental challenges in materials engineering, such as the automated extraction of statistical information from dual phase titanium alloy microstructure images to support rapid engineering decision making. Initially, this work was performed by extracting dense layer outputs from a pretrained convolutional neural network (CNN), running the high dimensional image vectors through a principal component analysis, and fitting a logistic regression model for image classification. Kfold cross validation results reported a mean validation accuracy of 83% over 19 different material pedigrees. Furthermore, it was shown that fine-tuning the pre-trained network was able to improve image classification accuracy by nearly 10% over the baseline. These image classification models were then used to determine and justify statistically equivalent representative volume elements (SERVE). Lastly, a convolutional neural network was trained and validated to make quantitative predictions from a synthetic and real, two-phase image datasets. This paper explores the application of convolutional neural networks for microstructure analysis in the context of aerospace engineering and material quality. Convolutional neural network ; Artificial intelligence ; Computer science ; Context (archaeology) ; Pattern recognition (psychology) ; Artificial neural network ; Principal component analysis ; Feature extraction ; Contextual image classification ; Machine learning ; Image (mathematics) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Kobi Felton | Jan G. Rittig | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2020-09-11)
Field: Materials Science — Materials Chemistry

Summit: Benchmarking Machine Learning Methods for Reaction Optimisation In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail to find optimal solutions. Benchmarking ; Computer science ; Summit ; Intuition ; Machine learning ; Bayesian optimization ; Artificial intelligence ; Bayesian probability Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kobi Felton | Jan G. Rittig | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2020-12-01)
Field: Materials Science — Materials Chemistry

Summit: Benchmarking Machine Learning Methods for Reaction Optimisation In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail to find optimal solutions. Benchmarking ; Computer science ; Summit ; Intuition ; Machine learning ; Bayesian probability ; Bayesian optimization ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Anh Tran | Julien Tranchida | Timothy Wildey | Aidan P. Thompson
Source: The Journal of Chemical Physics (preprint, 2020-08-19)
Field: Materials Science — Materials Chemistry

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials' design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum-niobium-titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations. Ternary operation ; Bayesian optimization ; Fidelity ; Density functional theory ; Computer science ; Uncertainty quantification ; Gaussian ; Bulk modulus ; Interatomic potential ; Gaussian process ; Bayesian probability ; Algorithm ; Materials science ; Machine learning ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Dean Sumner | Jiazhen He | Amol Thakkar | Ola Engkvist | Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2020-06-28)
Field: Materials Science — Materials Chemistry

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as attentional gain – an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs. Levenshtein distance ; Computer science ; Artificial intelligence ; Similarity (geometry) ; Sequence (biology) ; Machine learning ; Chemistry Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Topic Modeling
Authors: Aditi S. Krishnapriyan | Joseph Montoya | Jens S. Hummelshøj | Dmitriy Morozov
Source: arXiv (Cornell University) (preprint, 2020-10-01)
Field: Materials Science — Materials Chemistry

Persistent homology advances interpretable machine learning for nanoporous materials. Machine learning for nanoporous materials design and discovery has emerged as a promising alternative to more time-consuming experiments and simulations. The challenge with this approach is the selection of features that enable universal and interpretable materials representations across multiple prediction tasks. We use persistent homology to construct holistic representations of the materials structure. We show that these representations can also be augmented with other generic features such as word embeddings from natural language processing to capture chemical information. We demonstrate our approach on multiple metal-organic framework datasets by predicting a variety of gas adsorption targets. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from commonly used manually curated features. Persistent homology features allow us to locate the pores that correlate best to adsorption at different pressures, contributing to understanding atomic level structure-property relationships for materials design. Nanoporous ; Persistent homology ; Computer science ; Transferability ; Artificial intelligence ; Machine learning ; Homology (biology) ; Variety (cybernetics) ; Natural language processing ; Theoretical computer science ; Nanotechnology ; Biology ; Materials science ; Algorithm Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: K. R. Jayaram | G. Prakash | Vikram Jayaram
Source: International Journal of Advanced Computer Science and Applications (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Automatic Extraction of Rarely Explored Materials and Methods Sections from Research Journals using Machine Learning Techniques The scientific community is expanding by leaps and bounds every day owing to pioneering and path breaking scientific literature published in journals around the globe. Viewing as well as retrieving this data is a challenging task in today’s fast paced world. The essence and importance of scientific research papers for the expert lies in their experimental and theoretical results along with the sanctioned research projects from the organizations. Since scant work has been done in this direction, the alternative option is to explore text mining by machine learning techniques. Myriad journals are available on material research which throws light on a gamut of materials, synthesis methods, and characterization methods used to study properties of the materials. Application of materials has many diversified areas, hence selected papers from “Journal of Material Science” where “Materials and Methods” sections contains names of the method, characterization techniques (instrumental methods), algorithms, images, etc. used in research work. The “Acknowledgment” section conveys information about authors’ proximity, collaborations with organizations that are again not explored for the citation network. In the present articulated work, our attempt is to derive a means to automatically extract methods or terminologies used in characterization techniques, author, organization data from “Materials and Methods” and “Acknowledgment” sections, using machine learning techniques. Another goal of this research is to provide a data set for characterization terms, classification and an extended version of the existing citation network for material research. The complete dataset will help new researchers to select research work, find new domains and techniques to solve advanced scientific research problems. Computer science ; LEAPS ; Citation ; Data science ; Characterization (materials science) ; Task (project management) ; Artificial intelligence ; World Wide Web Machine Learning in Materials Science ; Statistical and Computational Modeling
Authors: Anthony Wang | Ryan Murdock | Steven Kauwe | Anton O. Oliynyk | Aleksander Gurlo | Jakoah Brgoch | Kristin A. Persson | Taylor D. Sparks
Source: ChemRxiv (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices &lt;div&gt;This Editorial is intended for materials scientists interested in performing machine learning-centered research.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking datasets, model and architecture sharing, and finally publication.&lt;/div&gt;&lt;div&gt;In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.&lt;/div&gt; Benchmarking ; Best practice ; Workflow ; Python (programming language) ; Computer science ; Artificial intelligence ; Architecture ; Machine learning ; Programming language ; Database ; Management Machine Learning in Materials Science
Authors: М. М. Кантор | V. V. Sudin | К. А. Солнцев
Source: Inorganic Materials Applied Research (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Analysis of the Relationship between the Load-Displacement Curve and Characteristics of Fracture of Low-Alloy Steel by Neural Networks The relationship between the parameters of the load-displacement curves and shear fracture area (SFA) of samples during impact bending tests has been studied. Using neural networks, a correspondence has been established between the form of the load-displacement curve and the fracture structure in a series of different microstructures. The applicability of the trained neural network to low-alloy steel with a microstructure that is different from the microstructures of the samples used in training has been tested. The minimal set of parameters of the load-displacement curve describing the shear fracture area of the sample is established. The nonlinearity of the relationship between the parameters of the dynamic curve and the shear fracture area of the sample is shown. Materials science ; Fracture (geology) ; Microstructure ; Shear (geology) ; Displacement (psychology) ; Artificial neural network ; Structural engineering ; Composite material ; Bending ; Metallurgy ; Computer science ; Engineering ; Artificial intelligence Material Properties and Failure Mechanisms ; High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming
Authors: Lihao Chen | Ben Xu | Jia Chen | Ke Bi | Changjiao Li | Shengyu Lu | Guo-Sheng Hu | Yuanhua Lin
Source: Journal of Materials Chemistry C (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Correction: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials Correction for ‘Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials’ by Lihao Chen <italic>et al.</italic>, <italic>J. Mater. Chem. C</italic>, 2020, DOI: 10.1039/d0tc02855j. Materials science ; Ensemble learning ; Thermoelectric effect ; Machine learning ; Condensed matter physics ; Artificial intelligence ; Computer science ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Machine Learning and ELM
Authors: Marcin Mińkowski | Kerstin Hummer | Christoph Dellago
Source: Journal of Physics Condensed Matter (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulations Using a recently developed approach to represent ab initio based force fields by a neural network potential, we perform molecular dynamics simulations of lead telluride and cadmium telluride crystals. In particular, we study the diffusion of a single cation interstitial in these two systems. Our simulations indicate that the interstitials migrate via two distinct mechanisms: through hops between interstitial sites and through exchanges with lattice atoms. We extract activation energies for both of these mechanisms and show how the temperature dependence of the total diffusion coefficient deviates from Arrhenius behaviour. The accuracy of the neural network approach is estimated by comparing the results for three different independently trained potentials. Molecular dynamics ; Cadmium telluride photovoltaics ; Diffusion ; Lead telluride ; Effective diffusion coefficient ; Arrhenius equation ; Telluride ; Materials science ; Chemical physics ; Chemistry ; Computational chemistry ; Activation energy ; Thermodynamics ; Physical chemistry ; Nanotechnology ; Physics ; Doping ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Kazuaki Toyoura | Kansei Kanayama
Source: Physical review. B./Physical review. B (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal An efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in a crystal. In the proposed method, an effective initial point for local optimization is sampled at each iteration from a given feasible set in the search space. The effective initial point is here defined as the grid point that most likely converges to a new local energy minimum by local optimization and/or is located in the vicinity of the boundaries between energy basins. Specifically, every grid point in the feasible set is classified by the predicted label indicating the local energy minimum that the grid point converges to. The classifier is created and updated at every iteration using the already-known information on the local optimizations at the earlier iterations, which is based on the support vector machine (SVM). The SVM classifier uses our original kernel function designed as reflecting the symmetries of both host crystal and interstitial species. The most distant unobserved point on the classification boundaries from the observed points is sampled as the next initial point for local optimization. The proposed method is applied to three model cases, i.e., the six-hump camelback function, a proton in strontium zirconate with the orthorhombic perovskite structure, and a water molecule in lanthanum sulfate with the monoclinic structure, to demonstrate the high performance of the proposed method. Maxima and minima ; Hyperparameter optimization ; Local optimum ; Orthorhombic crystal system ; Grid ; Support vector machine ; Computer science ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Crystal structure ; Chemistry ; Mathematical analysis ; Crystallography ; Geometry Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Kazuaki Toyoura | Takeo Fujii | Kenta Kanamori | Ichiro Takeuchi
Source: Physical review. B./Physical review. B (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Sampling strategy in efficient potential energy surface mapping for predicting atomic diffusivity in crystals by machine learning We propose a machine-learning-based method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier is partially evaluated by first-principles calculations. To preferentially evaluate the region of interest governing the atomic diffusivity, a statistical PES model based on a Gaussian process (GP-PES) is constructed and updated iteratively from known information on already-computed potential energies. In the proposed method, all local energy minima (stable and metastable sites) and elementary processes of atomic diffusion (atomic jumps) are explored on the predictive mean of the GP-PES. The uncertainty of jump frequency in each elementary process is then estimated on the basis of the variance of the GP-PES. The acquisition function determining the next grid point to be computed is designed to reflect the impacts of the uncertainties of jump frequencies on the uncertainty of the macroscopic atomic diffusivity. A numerical solution of the master equation is here employed to readily estimate the atomic diffusivity, which enables us to design the acquisition function reflecting the centrality of each elementary process. Thermal diffusivity ; Statistical physics ; Maxima and minima ; Jump process ; Gaussian process ; Metastability ; Jump ; Atomic radius ; Algorithm ; Computer science ; Physics ; Chemistry ; Gaussian ; Mathematics ; Thermodynamics ; Computational chemistry ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Bastien Casier | Maurício Chagas da Silva | Michaël Badawi | Fabien Pascale | Tomáš Bučko | Sébastien Lebègue∥ | Dario Rocca
Source: arXiv (Cornell University) (article, 2020-11-14)
Field: Materials Science — Materials Chemistry

Hybrid localized graph kernel for machine learning energy-related\n properties of molecules and solids Nowadays, the coupling of electronic structure and machine learning\ntechniques serves as a powerful tool to predict chemical and physical\nproperties of a broad range of systems. With the aim of improving the accuracy\nof predictions, a large number of representations for molecules and solids for\nmachine learning applications has been developed. In this work we propose a\nnovel descriptor based on the notion of molecular graph. While graphs are\nlargely employed in classification problems in cheminformatics or\nbioinformatics, they are not often used in regression problem, especially of\nenergy-related properties. Our method is based on a local decomposition of\natomic environments and on the hybridization of two kernel functions: a graph\nkernel contribution that describes the chemical pattern and a Coulomb label\ncontribution that 1encodes finer details of the local geometry. The accuracy of\nthis new kernel method in energy predictions of molecular and condensed phase\nsystems is demonstrated by considering the popular QM7 and BA10 datasets. These\nexamples show that the hybrid localized graph kernel outperforms traditional\napproaches such as, for example, the smooth overlap of atomic positions (SOAP)\nand the Coulomb matrices.\n Graph kernel ; Cheminformatics ; Molecular graph ; Kernel (algebra) ; Computer science ; Graph ; Coulomb ; Range (aeronautics) ; String kernel ; Polynomial kernel ; Artificial intelligence ; Kernel method ; Machine learning ; Theoretical computer science ; Computational chemistry ; Chemistry ; Mathematics ; Materials science ; Support vector machine ; Physics ; Discrete mathematics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions