Related papers: Expanding the search space of high entropy oxides …
We introduce the proper orthogonal descriptors for efficient and accurate interatomic potentials of multi-element chemical systems. The potential energy surface of a multi-element system is represented as a many-body expansion of…
This article reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve…
Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain…
On the premise that amorphous-HEA composites could demonstrate high toughness and resistance to embrittlement akin to the phase separating glassy-solid solution composites, we develop a thermodynamics based approach to identify chemical…
Retrosynthesis is essential for designing synthetic pathways for complex molecules and can be revolutionized by AI to automate and accelerate chemical synthesis planning for drug discovery and materials science. Here, we propose a…
Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen…
A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded…
High entropy alloys (HEAs) are promising magnetocaloric materials with tunable operating temperature conditions using compositional modifications. Here, we combine experiments and first principles based spin modelling to engineer…
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional…
We present an evaluation of CSP-MACE-{\AA}, a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into separate intramolecular and intermolecular components.…
Room temperature superconductivity remains elusive, and hydrogen-base compounds despite remarkable transition temperatures(Tc) typically require extreme pressures that hinder application. To accelerate discovery under moderate pressures, an…
We have developed a multi-objective optimization (MOO) procedure to construct modified-embedded-atom-method (MEAM) potentials with minimal manual fitting. This procedure has been applied successfully to develop a new MEAM potential for…
High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's…
Rare-earth oxides (REOs) are an important class of materials owing to their unique properties, including high ionic conductivities, large dielectric constants, and elevated melting temperatures, making them relevant to several technological…
We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including magnetic effects. The potential is based on the…
Density functional theory (DFT) is a fundamental method for simulating quantum chemical properties, but it remains expensive due to the iterative self-consistent field (SCF) process required to solve the Kohn-Sham equations. Recently, deep…
Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as charge neutrality, balanced electronegativity, synthesizability,…
Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are…
High throughput screening of materials for technologically relevant areas, like identification of better catalysts, electronic materials, ceramics for high temperature applications and drug discovery, is an emerging topic of research. To…
High-entropy alloys (HEAs) composed of multiple principal elements have been shown to offer improved radiation resistance over their elemental or dilute-solution counterparts. Using NiCoFeCrMn HEA as a model, here we introduce carbon and…