Related papers: Discovering High-Entropy Oxides with a Machine-Lea…
High-entropy alloys and ceramics containing at least five principal elements have recently received high attention for various mechanical and functional applications. The application of severe plastic deformation (SPD), particularly the…
Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder…
Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab…
A generic method to estimate the relative feasibility of formation of high entropy compounds in a single phase, directly from first principles, is developed. As a first step, the relative formation abilities of 56 multi-component, AO,…
Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of…
Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However,…
Interest in high-entropy inorganic compounds originates from their ability to stabilize cations and anions in local environments that rarely occur at standard temperature and pressure. This leads to new crystalline phases in many-cation…
High-entropy intermetallic compounds (HEICs) were fabricated by mechanical alloying and spark plasma sintering to fill a knowledge gap between the traditional high-entropy alloys (HEAs) and emerging high-entropy ceramics (HECs). Notably,…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
The vast compositional and structural landscape of high-entropy oxides (HEOs) grants them a wide range of potentially valuable physicochemical properties. However, the elemental immiscibility and crystal complexity limit their controllable…
Perovskite oxides ($AB$O$_3$) have long been central to the advancement of modern condensed matter physics, owing to their rich and tunable electronic and magnetic properties. The quest to understand their various entangled phases has…
Developing fast and accurate methods to discover intermetallic compounds is relevant for alloy design. While density-functional-theory (DFT)-based methods have accelerated design of binary and ternary alloys by providing rapid access to the…
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates…
Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials…
We describe a novel mechanism for the synthesis of a stable high-entropy alloy powder from an otherwise immiscible Mg-Ti rich metallic mixture by employing high-energy mechanical milling. The presented methodology expedites the synthesis of…
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied;…
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust…
Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a novel speedup technique: we change…
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…
Self-organization creates new order and shifts sub-boundaries while reorganizing energy and entropy within a control volume. This article examines pathway selection and tests whether maximizing the entropy generation rate can forecast…