Forecasting the outcome of spintronic experiments with neural ordinary differential equations
Deep learning has an increasing impact to assist research, allowing, for example, the
discovery of novel materials. Until now, however, these artificial intelligence techniques …
discovery of novel materials. Until now, however, these artificial intelligence techniques …
Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells
W Hussain, S Sawar, M Sultan - RSC advances, 2023 - pubs.rsc.org
Perovskite solar cells offer great potential for smart energy applications due to their flexibility
and solution processability. However, the use of solution-based techniques has resulted in …
and solution processability. However, the use of solution-based techniques has resulted in …
Machine learning magnetism classifiers from atomic coordinates
The determination of magnetic structure poses a long-standing challenge in condensed
matter physics and materials science. Experimental techniques such as neutron diffraction …
matter physics and materials science. Experimental techniques such as neutron diffraction …
Quantifying the efficacy of magnetic nanoparticles for MRI and hyperthermia applications via machine learning methods
Magnetic nanoparticles are a prospective class of materials for use in biomedicine as agents
for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of …
for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of …
Accelerating materials discovery using integrated deep machine learning approaches
W Xia, L Tang, H Sun, C Zhang, KM Ho… - Journal of Materials …, 2023 - pubs.rsc.org
We present an integrated deep machine learning (ML) approach that combines crystal
graph convolutional neural networks (CGCNN) for predicting formation energies and …
graph convolutional neural networks (CGCNN) for predicting formation energies and …
A machine learning approach to predict the structural and magnetic properties of Heusler alloy families
Random forest (RF) regression model is used to predict the lattice constant, magnetic
moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler …
moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler …
Comparison between different models of magnetic hysteresis in the solution of the TEAM 32 problem
The numerical modeling of magnetic materials in simulators is a difficult task, above all in
real devices with specific excitation. The aim of this work is to compare the accuracy of …
real devices with specific excitation. The aim of this work is to compare the accuracy of …
Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback
W Xia, M Sakurai, B Balasubramanian… - Proceedings of the …, 2022 - National Acad Sciences
Magnetic materials are essential for energy generation and information devices, and they
play an important role in advanced technologies and green energy economies. Currently …
play an important role in advanced technologies and green energy economies. Currently …
[HTML][HTML] Specific loss power of magnetic nanoparticles: A machine learning approach
A machine learning approach has been applied to the prediction of magnetic hysteresis
properties (coercive field, magnetic remanence, and hysteresis loop area) of magnetic …
properties (coercive field, magnetic remanence, and hysteresis loop area) of magnetic …
Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots
Z Bian, T Bao, X Sun, N Wang, Q Mu… - International Journal …, 2024 - Taylor & Francis
Introduction The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused
by the excessive use of antibiotics and the development of biofilms have been a growing …
by the excessive use of antibiotics and the development of biofilms have been a growing …