Multiscale and multimodal
imaging of material structures and properties
provides solid ground on which materials theory and design can flourish.
Recently, KAIST announced 10 flagship research fields, which include
KAIST Materials Revolution: Materials and Molecular Modeling, Imaging,
Informatics and Integration (M3I3). The M3I3 initiative aims to reduce
the time for the discovery, design and development of materials based
on elucidating multiscale processing–structure–property
relationship and materials hierarchy, which are to be quantified and
understood through a combination of machine learning and scientific
insights. In this review, we begin by introducing recent progress
on related initiatives around the globe, such as the Materials Genome
Initiative (U.S.), Materials Informatics (U.S.), the Materials Project
(U.S.), the Open Quantum Materials Database (U.S.), Materials Research
by Information Integration Initiative (Japan), Novel Materials Discovery
(E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing
Network (China), Vom Materials Zur Innovation (Germany), and Creative
Materials Discovery (Korea), and discuss the role of multiscale materials
and molecular imaging combined with machine learning in realizing
the vision of M3I3. Specifically, microscopies using photons, electrons,
and physical probes will be revisited with a focus on the multiscale
structural hierarchy, as well as structure–property relationships.
Additionally, data mining from the literature combined with machine
learning will be shown to be more efficient in finding the future
direction of materials structures with improved properties than the
classical approach. Examples of materials for applications in energy
and information will be reviewed and discussed. A case study on the
development of a Ni–Co–Mn cathode materials illustrates
M3I3’s approach to creating libraries of multiscale structure–property–processing
relationships. We end with a future outlook toward recent developments
in the field of M3I3.
In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naïve Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. The proposed method has been applied to the construction of a nomogram for a real clinical data set.
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