Scientific Resource Catalog
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Weak-beam imaging in dark-field X-ray microscopy (DFXM) can resolve individual dislocations in bulk crystals, but assigning Burgers vectors from the resulting contrast typically requires manual comparison with forward simulations. Here, we train a physics-informed convolutional neural network (CNN)…
The Seebeck coefficient in liquids often reaches the mV/K range, yet its microscopic origin remains unclear due to the complexity of electrolyte systems. Here we propose a minimal electrostatic theory focusing on solvation entropy. Using a core-shell extension of the Born equation with temperature-…
Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristin…
Understanding phonon-mediated heat transport in structurally complex materials remains a central challenge for next-generation electronic and nanomechanical devices, where grain boundaries and interfacial disorder strongly limit thermal dissipation. Although classical interatomic potentials enable…
The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription for how long-range physical effe…
Rapid progress of artificial neural network applications in recent years has led to the issue of unprecedented energy consumption. It can be solved by the implementation of energy-efficient hardware based on non-von Neumann architectures, which requires the development of electronic components emul…
Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support…
The effective mass approximation is widely used across models of carrier transport, optical response, and excitons in semiconductors and insulators, but its validity hinges on the assumption that the band dispersion $E_n(\mathbf{k})$ at the relevant extremum is analytic. We prove that analyticity h…
Periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD) has shown promise for quantitative calculations of band structures in solids. However, its steep computational scaling has limited calculations to relatively coarse $k$-point meshes, leading to sizable…
Recent experiments and simulations provide compelling evidence for the emergence of a non-phononic flat band in the dynamical structure factor of two- and three-dimensional amorphous solids. This feature has been suggested to be connected to the excess in the reduced vibrational density of states o…
The modification and enhancement of antiferromagnetic two-dimensional semiconductor is considered crucial for realizing novel electronic properties and facilitating promising applications. For this purpose, we investigate six antiferromagnetic 2D bilayer Janus YIBr structures with different stackin…
Atomically thin mechanical resonators have been realized predominantly in crystalline two-dimensional (2D) materials, such as graphene, where long-range crystalline order sets their elastic properties and defines their nonlinear resonant behavior. Extending these concepts to the amorphous 2D limit…
One-dimensional periodic rod networks are the structures that are obtained by periodically assembling its microstructural unit, a network of rods itself, in just one direction. In this work, we present a scheme for obtaining the nonlinear constitutive response of such structures when homogenized ma…
Abstract Crystalline solid solutions incorporate guest atoms by substituting host lattice sites up to a solubility limit dictated by solute–host similarity. Solid solutions enable tuning of various material properties, such as the optoelectronic behavior of halide perovskites. In bulk, the incompat…
We investigate the evolution of topological defects in polar fluids driven by discrete inversion symmetry-breaking acrossthe nematic-to-ferroelectric nematic phase transition. Using photopatterned surface alignment to prescribe well-defined initial defect configurations in the nematic phase, we tra…
The Dzyaloshinskii–Moriya interaction (DMI) in magnetic multilayers plays a pivotal role in spintronic applications by enabling the stabilization and manipulation of chiral spin textures, including chiral domain walls and skyrmions. While interfacial DMI in asymmetric multilayers is well establishe…
Welcome to the second edition of AISE, the International Workshop on AI Systems for the Environment. AISE brings together researchers working at the intersection of artificial intelligence and environmental sustainability, with the aim of exploring how AI methods can support more effective and resp…
We present a brief overview of the current state of radioactive (or nuclear) waste (RW) management (RWM), focusing on its end point and international advice provided by the International Atomic Energy Agency (IAEA). RW, defined as waste that contains, or is contaminated with, radionuclides at activ…
DTX is a throughput-first training accelerator for diffusion and transformer models. Any summation serialized through a single FP32 adder is a loop-carried dependence that pins a machine near 2 FLOP/cycle regardless of physical design; DTX is built so no such chain exists anywhere -- every reductio…
Reproducible bicrystal high-temperature superconducting quantum interference devices remain limited by local variability along the grain boundaries that form the Josephson junctions. Here, we develop a site-selective fabrication workflow in which atomic force microscopy maps the intended junction r…