Determination of γ/γ' interface free energy for solid state precipitation in Ni-Al alloys from molecular dynamics simulation.

Interface free energy is a fundamental material parameter needed to predict the nucleation and growth of new phases. The high cost of experimentally determining this parameter makes it an ideal target for calculation through a physically informed simulation. Direct determination of interface free energy has many challenges, especially for solid-solid transformations.

Hybrid magnon-phonon localization enhances function near ferroic glassy states.

Ferroic materials on the verge of forming ferroic glasses exhibit heightened functionality that is often attributed to competing long- and short-range correlations. However, the physics underlying these enhancements is not well understood. The NiCoMnIn Heusler alloy is on the edge of forming both spin and strain glasses and exhibits magnetic field-induced shape memory and large magnetocaloric effects, making it a candidate for multicaloric cooling applications.

True random number generation using the spin crossover in LaCoO.

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g.

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.

Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.

Allosteric modulation of a human odorant receptor.

Odor perception is first determined by how the myriad of environmental volatiles are detected at the periphery of the olfactory system. The combinatorial activation of dedicated odorant receptors generates enough encoding power for the discrimination of tens of thousands of odorants. Recent studies have revealed that odorant receptors undergo widespread inhibitory modulation of their activity when presented with mixtures of odorants, a property likely required to maintain discrimination and ensure sparsity of the code for complex mixtures.