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Abstract The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence. 

Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects. 

One-dimensional (1D) van der Waals (vdW) materials display electronic and magnetic transport properties that make them uniquely suited as interconnect materials and for low-dimensional optoelectronic applications. However, there are only around 700 1D vdW structures in general materials databases, making database curation approaches ineffective for 1D discovery. Here, we utilize machine-learning techniques to discover 1D vdW compositions that have not yet been synthesized. Our techniques go beyond discovery efforts involving elemental substitutions and instead start with a composition space of 4741 binary and 392,342 ternary formulas. We predict up to 3000 binary and 10,000 ternary 1D compounds and further classify them by expected magnetic and electronic properties. Our model identifies MoI3, a material we experimentally confirm to exist with wire-like subcomponents and exotic magnetic properties. More broadly, we find several chalcogen-, halogen-, and pnictogen-containing compounds expected to be synthesizable using chemical vapor deposition and chemical vapor transport. 

We report the polarization-dependent Raman spectra of exfoliated MoI3, a van der Waals material with a “true one-dimensional” crystal structure that can be exfoliated to individual atomic chains. The temperature evolution of several Raman features reveals an anomalous behavior suggesting a phase transition of magnetic origin. Theoretical considerations indicate that MoI3 is an easy-plane antiferromagnet with alternating spins along the dimerized chains and with inter-chain helical spin ordering. The calculated frequencies of phonons and magnons are consistent with the interpretation of the experimental Raman data. The obtained results shed light on the specifics of the phononic and magnonic states in MoI3 and provide a strong motivation for further study of this unique material with potential for future spintronic applications.