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Results of high resolution x-ray diffraction experiments are presented for single crystals of the spin gap compound BaCuSi$_2$O$_6$ in the temperature range from 16 to 300 K. The data show clear evidence of a transition from the room…
Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (T_m) and glass transition temperature (T_g)…
The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ melts and glasses. Here, we extend the model compositional range, which now allows…
Thermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and…
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use…
Around a glass transition, the dynamics of a supercooled liquid dramatically slow down, exhibited by caging of particles, while the structural changes remain subtle. In alternative to recent machine learning studies searching for structural…
Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to alloy design, catalysis, and the study of phase transitions.…
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the…
This work aimed to mathematically model the glass transition temperature (Tg), one of the most important parameters regarding the behavior of slag, responsible for the sudden change in thermomechanical properties of non-crystalline…
We explore the possibilities and limitations of using a coherent second phase to engineer the thermo-mechanical properties of a martensitic alloy by modifying the underlying free energy landscape that controls the transformation. We use…
Ceramic samples, produced by conventional sintering method in ambient air, 6H-SrMnO$_3$(6H-SMO), 15R-BaMnO$_3$(15R-BMO), 4H-Ba$_{0.5}$Sr$_{0.5}$MnO$_3$(4H-BSMO) were studied. In the XRD measurements for SMO the new anomalies of the lattice…
Time reversal symmetry and magnetoelastic correlations are probed by means of high-resolution volume dilatometry in URu2Si2 at cryogenic temperatures and magnetic fields more than enough to suppress the hidden order state at H_HO(T = 0.66…
We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time…
Understanding the physics of phase transitions in two-dimensional (2D) systems underpins the research in diverse fields including statistical mechanics, quantum systems, nanomagnetism, and soft condensed matter. However, many fundamental…
We consider a recently introduced 2-D square-to-rectangle martensite model that explains several unusual features of martensites to study ferromagnetic martensites. The strain order parameter is coupled to the magnetic order parameter…
Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML…
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to…
For machine learning of interatomic potentials a scalable sparse Gaussian process regression formalism is introduced with a data-efficient on-the-fly adaptive sampling algorithm. With this approach, the computational cost is effectively…
The current work reports on the elastic shear moduli, internal friction, and the specific heat of the B2 cubic ordered alloy AuZn as a function of temperature. Measurements were made on single-crystal and polycrystalline samples using…
The high-fidelity characterization of soft, tissue-like materials under ultra-high-strain-rate conditions is critical in engineering and medicine. Still, it remains challenging due to limited optical access, sensitivity to initial…