Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning

Gomberg, Joshua A.; Medford, Andrew J.; Kalidindi, Surya R.

doi:10.1016/j.actamat.2017.05.009

Cited by 52 publications

(30 citation statements)

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“…Orme et al discovered that twinning in Mg was correlated to a combination of factors including grain size, bulk dislocation density and grain boundary misorientation by applying machine learning to orientation image maps and dislocation density measurements. Others have extracted the common atomic structures of low energy or specific behavior‐bearing grain boundaries from large, simulated datasets …”

Section: Methods For Grain Boundary Quantificationmentioning

confidence: 99%

Review of grain boundary complexion engineering: Know your boundaries

et al. 2018

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Grain boundary structure-property relationships influence bulk performance and, therefore, are an important criterion in materials design. Materials scientists can generate different grain boundary structures by changes in temperature, pressure, and chemical potential because interfaces attain their own equilibrium states, known as complexions. Complexions undergo first-order transitions by changes in thermodynamic variables, which results in discontinuous changes in properties.Grain boundary complexion engineering is introduced in this paper as a method for controlling complexion transitions to improve material performance. This International Conference on Sintering 2017 lecture describes the tools for grain boundary complexion engineering: complexion equilibrium and time-temperaturetransformation (TTT) diagrams. These tools can be implemented in processing design to tailor grain boundary properties, including grain boundary mobility.While impactful, these diagrams are often limited in scope because they are currently empirically derived. This article discusses how measurement techniques can be combined with data analytical methods to build mechanistically derived complexion equilibrium and TTT diagrams.

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Section: Methods For Grain Boundary Quantificationmentioning

confidence: 99%

Review of grain boundary complexion engineering: Know your boundaries

et al. 2018

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“…Numerous research results have been published on microstructural quantification (Altschuh et al, 2017;Voyles, 2017;Gobert et al, 2018), classification (DeCost and Holm, 2015;Chowdhury et al, 2016;DeCost et al, 2017), evolution (Gomberg et al, 2017) and reconstruction (Sundararaghavan and Zabaras, 2005;Bostanabad et al, 2016). Bridging length-scales around the microstructure can be pursued via either bottom-up approaches, e.g., through homogenization or via top-down approaches, e.g., through localization.…”

Section: Microstructurementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

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Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

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“…9 Across all of these applications, a training database of simulated or experimentally-measured materials properties serves as input to a ML algorithm that predictively maps features (i.e., materials descriptors) to target materials properties. Ideally, the result of training such models would be the experimental realization of new materials with promising properties.…”

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confidence: 99%

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

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Antono

Church

et al. 2018

Mol. Syst. Des. Eng.

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Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-onecluster-out cross-validation (LOCO CV) and (2) a simple nearestneighbor benchmark to show that model performance in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering breakthrough materials like high-T c superconductors with ML.Materials informatics (MI), or the application of data-driven algorithms to materials problems, has grown quickly as a field in recent years. 9 Across all of these applications, a training database of simulated or experimentally-measured materials properties serves as input to a ML algorithm that predictively maps features (i.e., materials descriptors) to target materials properties. Ideally, the result of training such models would be the experimental realization of new materials with promising properties. The MI community has produced several such success stories, including thermoelectric compounds, 10,11 shapememory alloys, 12 superalloys, 13 and 3d-printable high-strength aluminum alloys. 14 However, in many cases, a model is itself the output of a study, and the question becomes: to what extent could the model be used to drive materials discovery? Typically, the performance of ML models of materials properties is quantified via cross-validation (CV). CV can be performed either in a single division of the available data into a training set (to build the model) and a test set (to evaluate its performance), or as an ensemble process known as k-fold CV wherein the data are partitioned into k nonoverlapping subsets of nearly equal size (folds) and model performance is averaged across each combination of k-1 training folds and one test fold. Leave-one-out crossvalidation (LOOCV) is the limit where k is the number of total examples in the dataset. Table 1 summarizes some examples of model performance statistics as reported in the aforementioned studies (some studies involved testing multiple algorithms across multiple properties).In Table 1, the reported model performance is uniformly excellent across all studies. A tempting conclusion is that any of these models could be used for one-shot high-throughput screening of large numbers of materials for desired properties. However, as we discuss below, traditional CV has critical shortcomings in terms of quantifying ML model performance for materials discovery. Issues with traditional crossvalidation for materials discoveryMany ML benchmark problems consist of data classification into discrete bins, i.e., pattern matching. For example, the Design, System, ApplicationMachine learning (ML) has become a widely-adopted predictive tool for materials design and discovery. Random k-fold cross-validation (CV), the traditional gold-standard approach for evaluating the quality of ML models, is fundamentally mismatched to the nature of materials discovery, and leads to ...

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Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning

Cited by 52 publications

References 51 publications

Review of grain boundary complexion engineering: Know your boundaries

Review of grain boundary complexion engineering: Know your boundaries

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

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