The effective charge of an element is a parameter characterizing the electromgration effect, which can determine the reliability of interconnection in electronic technologies. In this work, machine learning approaches were employed to model the effective charge (z*) as a linear function of physically meaningful elemental properties. Average 5-fold (leave-out-alloy-group) cross-validation yielded root-meansquare-error divided by whole data set standard deviation (RMSE/σ) values of 0.37 ± 0.01 (0.22 ± 0.18), respectively, and R 2 values of 0.86. Extrapolation to z* of totally 2 new alloys showed limited but potentially useful predictive ability. The model was used in predicting z* for technologically relevant host-impurity pairs.
Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.
We
use a random forest (RF) model to predict the critical cooling
rate (R
C) for glass formation of various
alloys from features of their constituent elements. The RF model was
trained on a database that integrates multiple sources of direct and
indirect R
C data for metallic glasses
to expand the directly measured R
C database
of less than 100 values to a training set of over 2000 values. The
model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude
in K/s. The error on leave-out-one-group CV on alloy system groups
is 0.59 log units in K/s when the target alloy constituents appear
more than 500 times in training data. Using this model, we make predictions
for the set of compositions with melt-spun glasses in the database
and for the full set of quaternary alloys that have constituents which
appear more than 500 times in training data. These predictions identify
a number of potential new bulk metallic glass systems for future study,
but the model is most useful for the identification of alloy systems
likely to contain good glass formers rather than detailed discovery
of bulk glass composition regions within known glassy systems.
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