Related papers: LLM-driven discovery for carbon allotropes with bo…

Based on structure prediction method, the machine learning method is used instead of the density function theory (DFT) method to predict the material properties, thereby accelerating the material search process. In this paper, we…

Based on C8, carbon 4C, with cfc topology, two hybrid carbon allotropes generated by inserting C(sp2) and C(sp1) carbon atoms into C8 diamond-like lattice were identified and labeled ene-C10 containing C(sp2) and ene-yne-C14 containing…

The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…

Even though thermodynamic energy-based crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through…

A metallic covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an $sp^3$ carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene…

We present a systematic search for low-energy metastable superhard carbon allotropes by using the recently developed evolutionary metadynamics technique. It is known that cold compression of graphite produces an allotrope at 15-20 GPa. Here…

Diamondynes, a new class of diamond-like carbon allotropes composed of carbon with sp$^2$/sp$^3$-hybridized carbon networks, exhibit unique structural motifs that have not been previously reported in carbon materials. These architectures…

Based on crystal chemistry considerations and quantum density functional theory ground state calculations, rhombohedral rh-C3 and hexagonal h-C6 carbon allotropes are proposed and energetically calculated as new stable ultra-hard phases…

A novel carbon allotrope, hexagonal C12, is proposed from crystal chemistry and quantum density functional theory DFT calculations of ground state and physical properties. The structure exhibits corner sharing distorted tetrahedra with the…

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response.…

Novel ultra-hard carbon allotrope is proposed with mechanical, dynamic, and thermal properties like diamond. Based on energy criteria from computations within density functional theory DFT, tetragonal C8 stoichiometry is identified as a…

High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as…

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although…

Carbon enjoys a vast number of allotropic forms, each possessing unique properties determined by the lattice structures and bonding characters. Here, based on first-principles calculations, we propose a new three-dimensional carbon…

Crystal structure prediction (CSP) has proven to be a highly effective route for discovering new materials. Substantial advancements have been made in CSP of inorganic and molecular crystals, while hybrid materials, including metal-organic…

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,…

Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…

Recent advances in machine learning have enabled large-scale atomic simulations with first-principles accuracy, allowing precise modeling of disordered materials such as glassy carbon (GC). However, conventional ab initio molecular dynamics…

Based on the third allotropic form of carbon (Fullerenes) through theoretical study have been predicted structures described as non-classical fullerenes. We have studied novel allotropic carbon structures with a closed cage configuration…

Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials…