Skyrmion qubits: Challenges for future quantum computing applications
Magnetic nano-skyrmions develop quantized helicity excitations, and the quantum tunneling
between nano-skyrmions possessing distinct helicities is indicative of the quantum nature of …
between nano-skyrmions possessing distinct helicities is indicative of the quantum nature of …
Towards end-to-end structure determination from x-ray diffraction data using deep learning
Powder crystallography is the experimental science of determining the structure of
molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) …
molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) …
Artificial intelligence guided studies of van der Waals magnets
TD Rhone, R Bhattarai, H Gavras… - Advanced Theory …, 2023 - Wiley Online Library
A materials informatics framework to explore a large number of candidate van der Waals
(vdW) materials is developed. In particular, in this study a large space of monolayer …
(vdW) materials is developed. In particular, in this study a large space of monolayer …
Machine learning predictions of high-Curie-temperature materials
Technologies that function at room temperature often require magnets with a high Curie
temperature, TC, and can be improved with better materials. Discovering magnetic …
temperature, TC, and can be improved with better materials. Discovering magnetic …
Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review
Abstract 2D intrinsic magnetic materials possess unique physical properties distinct from
bulk materials, providing an ideal research platform for the development of low‐dimensional …
bulk materials, providing an ideal research platform for the development of low‐dimensional …
Machine Learning Prediction of the Experimental Transition Temperature of Fe (II) Spin-Crossover Complexes
Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in
response to external stimuli, with potential applications in molecular electronics. It is …
response to external stimuli, with potential applications in molecular electronics. It is …
DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides
With the technological advancement in recent years and the widespread use of magnetism
in every sector of the current technology, a search for a low-cost magnetic material has been …
in every sector of the current technology, a search for a low-cost magnetic material has been …
Towards physics-informed explainable machine learning and causal models for materials research
A Ghosh - Computational Materials Science, 2024 - Elsevier
From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …
optimization of process parameters for achieving best performance–all key facets of …
Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning
The revolution in materials in the past century was built on a knowledge of the atomic
arrangements and the structure-property relationship. The sine qua non for obtaining …
arrangements and the structure-property relationship. The sine qua non for obtaining …
Predicting magnetic properties of van der Waals magnets using graph neural networks
We study two-dimensional (2D) magnetic materials using state-of-the-art machine learning
models that use a graph-theory framework. We find that representing materials as graphs …
models that use a graph-theory framework. We find that representing materials as graphs …