材料科学
Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks remain limited to forward property prediction, failing to systematically evaluate inverse…
Operando microscopy has unveiled key mechanistic insights in battery materials during early cycling, but long-term characterization to unveil material evolution, degradation, and failure remain limited. To address this gap, we develop a…
Antimicrobial photodynamic therapy (aPDT) is a promising modality for inactivation of antibiotic resistant bacteria, relying on the activation of a photosensitizer (PS) by light of a specific wavelength. This process results in the…
While negative capacitance has been demonstrated in ferroelectric-dielectric heterostructures in the form of capacitance enhancement, all experimental evidence, to date, suggests the existence of domains therein. Here, we address the…
Twisted moire superlattices in van-der-Waals heterostructures provide a powerful platform for engineering correlated states through moire-band reconstruction. However, whether globally coherent electronic orders can be continuously…
The discovery of altermagnetism has shown that crystal symmetry can generate momentum-dependent internal polarization without net magnetization. Whether an analogous form of symmetry-organized momentum-space order can exist for spinless…
Phononic crystals enable precise manipulation of elastic wave propagation through engineered bandgaps; however, designing defect states within these bandgaps for frequency-selective applications remains a significant challenge. Existing…
Previous density functional theory (DFT) calculations for random solid solution (RSS) CoCrNi predict negative intrinsic stacking-fault energy (ISFE) at 0 K, contrary to experimental observations of finite stacking-fault widths. Two…
The development of radiation-tolerant materials capable of maintaining structural, electrical, and thermal stability in extreme, radiation-rich environments remains a critical challenge in materials science. In this work, the effects of 60…
Compound-refractive lenses (CRL) are a type of x-ray optics that find widespread applications as focusing and imaging lenses. The choice of material is one of the most defining properties of these lenses. In this work, we present a CRL made…
Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when…
EuCd2As2 materials have two magnetic ordering states: antiferromagnetic (AFM) and ferromagnetic (FM) when their chemical tunability is utilized. While AFM-EuCd2As2 has a nonzero magnetoelectric response due to its symmetry breaking with…
Polymer-derived ceramics combine the thermal stability of ceramics with the versatile properties of carbon domains, but modeling their atomic-scale evolution during processing remains elusive due to the limitations of traditional…
Altermagnets provide a promising platform for a wide spectrum of applications integrating advantages of conventional ferromagnets and antiferromagnets. In this work, we implement a medium-throughput first-principles workflow and evaluate…
Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Materials Acceleration Platform for Electrochemistry (MAP-E)…
The application of molecular dynamics (MD) simulations to quasistatic loading is severely limited by the large separation between atomic vibration timescales and experimentally relevant deformation rates. In this work we employ the…
Twistronics is an emerging and captivating field in condensed matter physics and material science. However, accurately and efficiently calculating the electronic structures of twisted systems remains a significant challenge. To address…
Autoregressive learning of time-stepping operators provides an effective approach to data-driven partial differential equation (PDE) simulation, yet for conservation laws, they face a fundamental challenge: learned updates may violate…
Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the possibility to simulate much larger length and time scales.…
We present an open-source software package IRSSG for investigating magnetic systems with spin space groups (SSGs). The package works within the density functional theory (DFT) framework and requires wavefunctions from DFT codes, such as…