Discovering
and understanding new materials with desired properties
are at the heart of materials science research, and machine learning
(ML) has recently offered special shortcuts to the ultimate goal.
Thanks to the nourishment of computer hardware and computational chemistry,
the development of calculated scientific data repositories could fuel
the ML models to investigate the vast materials space. At this moment,
understanding this revolutionary paradigm is urgent, and this Review
aims to deliver comprehensive information about the collaboration
of ML with materials science. This Review summarizes recent achievements
in catalysts design, which can be benefitted from ML because of the
complex nature of catalytic reactions and vast candidate materials
space. ML models for catalyst design could be transferred to applications
in other domains and vice versa. The basic concepts of ML algorithms
and practical guides to materials scientists are also demonstrated.
Moreover, challenges and strategies of applying ML are discussed,
which should be addressed collaboratively between materials scientists
and ML communities. Ultimate integrations of ML in materials science
are expected to accelerate the design, discovery, optimization, and
interpretation of materials in both industry and academia, and this
Review hopes to be the informative base camp for that journey.
Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog synaptic device, researchers focus on improving device characteristics. Among various characteristics, the most challenging one is linearity and symmetry of synaptic weight update that is required for on‐chip training. In this regard, this review paper discusses recent synaptic device improvements focusing on novel schemes tailored for each NVM device to improve the linearity and symmetry. In addition to device‐level studies, recent research achievements are reviewed expanded up to chip‐level studies because in realizing neuromorphic hardware systems beyond a single synaptic device, several considerations and requirements are needed to confirm for high‐level design, and accordingly, cooptimize among synaptic devices, synapse arrays, electrical circuits, neural networks, algorithms, and implementation. Also, this review paper introduces various circuit and algorithmic approaches to compensate for the non‐ideality of the analog synaptic device.
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