Wood is a multiscale material exhibiting a complex viscoplastic response. We study avalanches in small wood samples in compression. “Woodquakes” measured by acoustic emission are surprisingly similar to earthquakes and crackling noise in rocks and laboratory tests on brittle materials. Both the distributions of event energies and of waiting (silent) times follow power laws. The stress-strain response exhibits clear signatures of localization of deformation to “weak spots” or softwood layers, as identified using digital image correlation. Even though material structure-dependent localization takes place, the avalanche behavior remains scale-free.

Portevin–Le Chatelier deformation bands obey predictions of a simple mean-field model of critical avalanche dynamics.

The search for better compositions in high entropy alloys is a formidable challenge in materials science. Here, we demonstrate a systematic Bayesian optimization method to enhance the mechanical properties of the paradigmatic five-element Cantor alloy in silico. This method utilizes an automated loop with an online database, a Bayesian optimization algorithm, thermodynamic modeling, and molecular dynamics simulations. Starting from the equiatomic Cantor composition, our approach optimizes the relative fractions of its constituent elements, searching for better compositions while maintaining the thermodynamic phase stability. With 24 steps, we find Fe21Cr20Mn5Co20Ni34 with a yield stress improvement of 58%, and with 72 steps, we find Fe6Cr22Mn5Co32Ni35 where the yield stress has improved by 74%. These optimized compositions correspond to Ni-rich medium entropy alloys with enhanced mechanical properties and superior face-centered-cubic phase stability compared to the traditional equiatomic Cantor alloy. The automatic approach devised here paves the way for designing high entropy alloys with tailored properties, opening avenues for numerous potential applications.

Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore. Here, we tackle this challenge by finding optimal compositions for target mechanical properties. We apply Bayesian exploration for the CuZrAl composition, a paradigmatic metallic glass known for its good glass forming ability. We exploit an automated loop with an online database, a Bayesian optimization algorithm, and molecular dynamics simulations. From the ubiquitous 50/50 CuZr starting point, we map the composition landscape, changing the ratio of elements and adding aluminum, to characterize the yield stress and the shear modulus. This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration c _Al = 15% and zirconium concentration c _Zr = 30%. We also explore several cooling rates (“process parameters”) and find that the best mechanical properties for a composition result from being most affected by the cooling procedure. Our Bayesian approach paves the novel way for the design of metallic glasses with “small data”, with an eye toward both future in silico design and experimental applications exploiting this toolbox.

Our framework enables the use of fast measurements in an auxiliary space (the liquid state) to predict the target material properties (mechanical strength of dry material), instead of slow sample manufacture (foam forming and mechanical tests).