AIMC Topic: Alloys

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A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.

Scientific reports
We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative rel...

Control Aspects of Shape Memory Alloys in Robotics Applications: A Review over the Last Decade.

Sensors (Basel, Switzerland)
This paper mainly focuses on various types of robots driven or actuated by shape memory alloy (SMA) element in the last decade which has created the potential functionality of SMA in robotics technology, that is classified and discussed. The wide spe...

Antimony as a Programmable Element in Integrated Nanophotonics.

Nano letters
The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of ma...

An end-to-end computer vision methodology for quantitative metallography.

Scientific reports
Metallography is crucial for a proper assessment of material properties. It mainly involves investigating the spatial distribution of grains and the occurrence and characteristics of inclusions or precipitates. This work presents a holistic few-shot ...

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys t...

Soft Hydrogel Actuator for Fast Machine-Learning-Assisted Bacteria Detection.

ACS applied materials & interfaces
We demonstrate that our bio-electrochemical platform facilitates the reduction of detection time from the 3-day period of the existing tests to 15 min. Machine learning and robotized bioanalytical platforms require the principles such as hydrogel-bas...

Machine Learning Accelerated, High Throughput, Multi-Objective Optimization of Multiprincipal Element Alloys.

Small (Weinheim an der Bergstrasse, Germany)
Multiprincipal element alloys (MPEAs) have gained surging interest due to their exceptional properties unprecedented in traditional alloys. However, identifying an MPEA with desired properties from a huge compositional space via a cost-effective desi...

Alloying conducting channels for reliable neuromorphic computing.

Nature nanotechnology
A memristor has been proposed as an artificial synapse for emerging neuromorphic computing applications. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform. An elec...

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...

A bioinspired optoelectronically engineered artificial neurorobotics device with sensorimotor functionalities.

Nature communications
Development of the next generation of bio- and nano-electronics is inseparably connected to the innovative concept of emulation and reproduction of biological sensorimotor systems and artificial neurobotics. Here, we report for the first time princip...