9 research outputs found
Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing
The area-normalized change of mass (m/A) with time during the
oxidation of metallic alloys is commonly used to assess oxidation resistance.
Analyses of such data can also aid in evaluating underlying oxidation
mechanisms. We performed an exhaustive literature search and digitized
normalized mass change vs. time data for 407 alloys. To maximize the impact of
these and future mass uptake data, we developed and published an open, online,
computational workflow that fits the data to various models of oxidation
kinetics, uses Bayesian statistics for model selection, and makes the raw data
and model parameters available via a queryable database. The tool, Refractory
Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls
to make the workflow and data (including metadata) findable, accessible,
interoperable, and reusable (FAIR). We find that the models selected by the
original authors do not match the most likely one according to the Bayesian
information criterion (BIC) in 71% of the cases. Further, in 56% of the cases,
the published model was not even in the top 3 models according to the BIC.
These numbers were obtained assuming an experimental noise of 2.5% of the mass
gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is
open access and researchers can add their own raw data (those to be included in
future publications, as well as negative results) for analysis and to share
their work with the community. Such consistent and systematic analysis of open,
community generated data can significantly accelerate the development of
machine-learning models for oxidation behavior and assist in the understanding
and improvement of oxidation resistance
Prediction of Atomization Energy Using Graph Kernel and Active Learning
Data-driven prediction of molecular properties presents unique challenges to
the design of machine learning methods concerning data
structure/dimensionality, symmetry adaption, and confidence management. In this
paper, we present a kernel-based pipeline that can learn and predict the
atomization energy of molecules with high accuracy. The framework employs
Gaussian process regression to perform predictions based on the similarity
between molecules, which is computed using the marginalized graph kernel. To
apply the marginalized graph kernel, a spatial adjacency rule is first employed
to convert molecules into graphs whose vertices and edges are labeled by
elements and interatomic distances, respectively. We then derive formulas for
the efficient evaluation of the kernel. Specific functional components for the
marginalized graph kernel are proposed, while the effect of the associated
hyperparameters on accuracy and predictive confidence are examined. We show
that the graph kernel is particularly suitable for predicting extensive
properties because its convolutional structure coincides with that of the
covariance formula between sums of random variables. Using an active learning
procedure, we demonstrate that the proposed method can achieve a mean absolute
error of 0.62 +- 0.01 kcal/mol using as few as 2000 training samples on the QM7
data set
Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences
Fueled by breakthrough technology developments, the biological, biomedical,
and behavioral sciences are now collecting more data than ever before. There is
a critical need for time- and cost-efficient strategies to analyze and
interpret these data to advance human health. The recent rise of machine
learning as a powerful technique to integrate multimodality, multifidelity
data, and reveal correlations between intertwined phenomena presents a special
opportunity in this regard. However, classical machine learning techniques
often ignore the fundamental laws of physics and result in ill-posed problems
or non-physical solutions. Multiscale modeling is a successful strategy to
integrate multiscale, multiphysics data and uncover mechanisms that explain the
emergence of function. However, multiscale modeling alone often fails to
efficiently combine large data sets from different sources and different levels
of resolution. We show how machine learning and multiscale modeling can
complement each other to create robust predictive models that integrate the
underlying physics to manage ill-posed problems and explore massive design
spaces. We critically review the current literature, highlight applications and
opportunities, address open questions, and discuss potential challenges and
limitations in four overarching topical areas: ordinary differential equations,
partial differential equations, data-driven approaches, and theory-driven
approaches. Towards these goals, we leverage expertise in applied mathematics,
computer science, computational biology, biophysics, biomechanics, engineering
mechanics, experimentation, and medicine. Our multidisciplinary perspective
suggests that integrating machine learning and multiscale modeling can provide
new insights into disease mechanisms, help identify new targets and treatment
strategies, and inform decision making for the benefit of human health
Learning the constitutive relation of polymeric flows with memory
We develop a learning strategy to infer the constitutive relation for the
stress of polymeric flows with memory. We make no assumptions regarding the
functional form of the constitutive relations, except that they should be
expressible in differential form as a function of the local stress- and
strain-rate tensors. In particular, we use a Gaussian Process regression to
infer the constitutive relations from stress trajectories generated from
small-scale (fixed strain-rate) microscopic polymer simulations. For
simplicity, a Hookean dumbbell representation is used as a microscopic model,
but the method itself can be generalized to incorporate more realistic
descriptions. The learned constitutive relation is then used to perform
macroscopic flow simulations, allowing us to update the stress distribution in
the fluid in a manner that accounts for the microscopic polymer dynamics. The
results using the learned constitutive relation are in excellent agreement with
full Multi-Scale Simulations, which directly couple micro/macro degrees of
freedom, as well as the exact analytical solution given by the Maxwell
constitutive relation. We are able to fully capture the history dependence of
the flow, as well as the elastic effects in the fluid. We expect the proposed
learning/simulation approach to be used not only to study the dynamics of
entangled polymer flows, but also for the complex dynamics of other Soft Matter
systems, which possess a similar hierarchy of length- and time-scales.Comment: 19 pages, 9 figure