Related papers: LLM-driven discovery for carbon allotropes with bo…
Carbon can exist as isolated dumbbell, one-dimensional (1D) chain, 2D plane, and 3D network in carbon solids or carbon-based compounds, which attributes to its rich chemical binding way, including sp-, sp2-, and sp3-hybridized bonds. Sp2…
Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM-driven framework for…
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating and predicting inorganic crystal structures is important for…
High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal elements. Material design and property tailoring possibilities emerge from…
Modeling high-dimensional, nonlinear dynamic structural systems under natural hazards presents formidable computational challenges, especially when simultaneously accounting for uncertainties in external loads and structural parameters.…
Search of materials with C-N composition hold a great promise in creating materials which would rival diamond in hardness due to the very strong and relatively low-ionic C-N bond. Early experimental and theoretical works on C-N compounds…
A sp2-sp3 hybrid carbon allotrope named HSH-carbon is proposed by the first-principles calculations. The structure of HSH-carbon can be regarded as a template polymerization of [1.1.1]propellane molecules in a hexagonal lattice, as well as,…
The accelerating adoption of Large Language Models (LLMs) in software engineering (SE) has brought with it a silent crisis: unsustainable computational cost. While these models demonstrate remarkable capabilities in different SE tasks, they…
This paper presents an innovative end-to-end workflow for mineral exploration, integrating ambient noise tomography (ANT) and artificial intelligence (AI) to enhance the discovery and delineation of mineral resources essential for the…
A structurally stable crystalline carbon allotrope is predicted by means of the first-principles calculations. This allotrope can be derived by substituting each atom in diamond with a carbon tetrahedron, and possesses the same space group…
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…
The lon topology inherent to lonsdaleite C4 hexagonal diamond is shown to characterize three original carbon allotropes C8 in the orthorhombic system with irregular orientations of the C4 tetrahedra. The octacarbon stoichiometries were…
Enhancing the fracture toughness of diamond while preserving its hardness is a significant challenge. Traditional toughening strategies have primarily focused on modulating the internal microstructural units of diamonds, including…
Silicon is the undisputed cornerstone of modern technology, with applications ranging from micro- and opto-electronics to quantum technologies. Recently, the exploration of its allotropes has emerged as a pivotal frontier for engineering…
Super-hard C5 with lon topology (lon: Lonsdaleite hexagonal diamond) and characterized by the presence of sp3 and sp2 -like carbon sites is devised from crystal chemistry and used as template matrix structure for identifying original…
We present a comprehensive first-principles investigation of a novel carbon allotrope characterized by quasi-tetragonal atomic motifs and quasi-two-dimensional structural behavior. Structural analysis reveals an open framework composed of…
High entropy alloys (HEA) show promise as a new type of high-performance structural material. Their vast degrees of freedom provide for extensive opportunities to design alloys with tailored properties. However, the compositional…
We present an advanced scanning probe microscopy system enhanced with artificial intelligence (AI-SPM) designed for self-driving atomic-scale measurements. This system expertly identifies and manipulates atomic positions with high…
First-principles prediction of lattice thermal conductivity $\kappa_L$ of strongly anharmonic crystals is a long-standing challenge in solid state physics. Making use of recent advances in information science, we propose a systematic and…
First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for…