材料科学
Understanding heat transport at the nanometer scale is critical for semiconductor devices, quantum materials, and thermal management of nanostructures, yet direct local measurements of thermal conductivity and heat capacity remain scarce.…
Radiation-induced segregation (RIS) and chemical redistribution in structural alloys can significantly degrade material performance, ultimately leading to failure. In this study, building on previous work by the authors [1], we investigate…
We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibly employed for both unfilled and filled natural rubber (NR).…
Emerging altermagnets with zero net magnetic moment and moment-dependent spin splitting offer a promising avenue for antiferromagnetic spintronic devices, yet their integration into magnetic tunnel junctions has been hindered by reliance on…
Electronic structure is ubiquitously obtained via density functional theory (DFT), where the charge density plays a central role. This work presents EdenGNN (Equivariant Density Graph Neural Network), a machine learning (ML) charge density…
Altermagnetism represents a recently established class of collinear magnetism that combines zero net magnetization with momentum-dependent spin polarization, enabled by symmetry constraints rather than spin-orbit coupling. This distinctive…
We present a comprehensive investigation of the influence of Ru concentration on the lattice parameters, atomic magnetic moments, electronic structure, and magnetic anisotropy energy of the full Heusler L2$_1$-type Mn$_2$Ru$_{1-x_p}$Ga…
Metal components are extensively used as current collectors, anodes, and interlayers in lithium-ion batteries. Integrating these functions into one component enhances the cell energy density and simplifies its design. However, this…
Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential…
We report the electronic structure of the thermoelectric semimetal Ta$_2$PdSe$_6$ with a large thermoelectric power factor and giant Peltier conductivity by means of angle-resolved photoemission spectroscopy (ARPES). The ARPES spectra…
We investigate the ultrafast magnetization dynamics of semiconducting antiferromagnetic CrSBr using real-time time-dependent density functional theory. In zero magnetic field, laser excitation modifies only the magnetization along the easy…
Orbital effects, despite their fundamental significance and potential to engender novel physical phenomena and enable new applications, have long been underexplored compared to their spin counterparts. Recently, surging interest in the…
Spin splitting in emerging altermagnets is non-relativistic and momentum-dependent, yet energy-independent, and localized in momentum space, posing challenges for practical applications. Here, we propose an intercalation-driven paradigm for…
Traditional approaches to achieve targeted epitaxial growth involves exploring a vast parameter space of thermodynamical and kinetic drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach…
Structural modulation is a key ingredient behind the extraordinary (magneto)elastic response of Ni-Mn-Ga martensite, yet its link to fine twinned microstructure and twin-boundary supermobility remains unresolved. Here we analyse martensitic…
The intercalation of molecular species between the layers of van der Waals (vdW) crystals is a powerful approach to combine the remarkable physical properties of vdW materials with the chemical versatility of organic molecules. However, the…
Chiral charge density waves (CDWs) have attracted intense interest due to their exotic quantum properties, yet the microscopic origin of structural chirality emerging from correlated charge order remains elusive. Here, we reveal that the…
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which…
This study proposes a modification to the yield condition that addresses the mathematical constraints inherent in the Directional Distortional Hardening models developed by Feigenbaum and Dafalias. The modified model resolves both the…
The shock initiation of energetic materials is mediated by the localization of mechanical energy into hotspots. These originate through the interaction of the shock and material microstructure; the most potent hotspots are formed by the…