Publications by authors named "T Smidt"
Metallic alloys often form phases-known as solid solutions-in which chemical elements are spread out on the same crystal lattice in an almost random manner. The tendency of certain chemical motifs to be more common than others is known as chemical short-range order (SRO), and it has received substantial consideration in alloys with multiple chemical elements present in large concentrations due to their extreme configurational complexity (e.g.
View Article and Find Full Text PDFThe determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements.
View Article and Find Full Text PDFPurpose: Lipoedema is a progressive adipose (fat) disorder, and little is known about its psychological effect. This study aimed to determine the experiences of physical and mental health and health care across stages of lipoedema.
Methods: Cross-sectional, secondary data from an anonymous survey (conducted 2014-2015) in Dutch and English in those with self-reported lipoedema were used (N = 1,362, Mdnage = 41-50 years old, 80.
Background: Good physical health and capacity is a requirement for offshore wind service technicians (WTs) who have substantial physical work demands and are exposed to numerous health hazards. Workplace physical exercise has shown promise for improving physical health and work ability among various occupational groups. Therefore, we aimed to assess the feasibility and preliminary efficacy of Intelligent Physical Exercise Training (IPET) among WTs in the offshore wind industry.
View Article and Find Full Text PDFArticle Synopsis
- - NequIP is a new neural network model that uses E(3)-equivariant convolutions to learn interatomic potentials from quantum mechanical calculations, improving how atomic environments are represented.
- - This approach achieves top-tier accuracy across various molecules and materials, showing the ability to work effectively with much less training data than traditional models.
- - By requiring significantly fewer training samples, NequIP challenges the notion that deep learning networks need large datasets, enabling accurate simulations of molecular dynamics over extended time periods.