Related papers: ConfPred: A layered intergrowth structure predicti…
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,…
Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self-supervised foundation model converting surface meshes into dense per-token embeddings.…
Superhard materials with good fracture toughness have found wide industrial applications, which necessitates the development of accurate hardness and fracture toughness models for efficient materials design. Although several macroscopic…
Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of…
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train…
We systematically explore the self-assembly of semi-flexible polymers in deformable spherical confinement across a wide regime of chain stiffness, contour lengths and packing fractions by means of coarse-grained molecular dynamics…
Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusion principles can…
Intercalation (ic) of metal atoms into the van der Waals (vdW) gap of layered materials constitutes a facile strategy to create new materials whose properties can be tuned via the concentration of the intercalated atoms. Here we perform…
Electronic and magnetic structures of iron selenide compounds Ce2O2FeSe2 (2212\ast) and BaFe2Se3(123\ast) are studied by the first-principles calculations. We find that while all these compounds are composed of one-dimensional (1D) Fe chain…
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a…
Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved studies and the property correlations are usually deciphered by…
For a newly discovered iron-based high T_c superconducting parent material KFe2Se2, we present an effective three-dimensional five-orbital tight-binding model by fitting the band structures. The three t2g-symmetry orbitals of the five Fe 3d…
Extensive studies have been performed on layered compounds, ranging from layered cuprates to van der Waals materials with critical issues of intergrowths and stacking faults. However, such structures have been studied less because of…
Crystal structure design is important for the discovery of new highly functional materials because crystal structure strongly influences material properties. Crystal structures are composed of space-filling polyhedra, which affect material…
Correlated electron systems that host multiple electronic orders offer routes to multifunctional quantum materials, but strong competition between these orders often prevents their coexistence. Here we show that nanoscale, metastable…
Composites with high strength and high fracture resistance are desirable for structural and protective applications. Most composites, however, suffer from poor damage tolerance and are prone to unpredictable fractures. Understanding the…
Single crystal neutron diffraction is combined with synchrotron x-ray scattering to identify the different superlattice phases present in $Cs_{0.8}Fe_{1.6}Se_2$. A combination of single crystal refinements and first principles modelling are…
A new iron-based superconductor, Lix(C6H16N2)yFe2-zSe2, with Tc = 38 K has successfully been synthesized via the intercalation of lithium and hexamethylenediamine into FeSe. The superconducting transition has been confirmed not only by…
The discovery of graphene has spurred vigorous investigation of 2D materials, revealing a wide range of extraordinary properties and functionalities. 2D heterostructural materials have recently been fabricated by assembling isolated planes…
Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to…