Scientific Resource Catalog
统一发现文献、数据集、软件、Benchmark 与出版物。当前只登记公开元数据、永久标识和原始链接。
MXenes, two-dimensional transition-metal carbides and nitrides, are typically obtained from MAX phases, yet historical reports suggest a broader, largely unexplored chemical space. Here we combine machine-learning-assisted database mining with experiments to uncover overlooked multilayer (ml) MXene…
Many crystals exhibit polymorphism, undergoing atomic rearrangements that result in unit cells belonging to different symmetry groups. These structural changes directly affect lattice vibration modes and consequently influence their mid-infrared optical properties. In this study, we use tungsten tr…
Van der Waals heterostructures (vdW HTSs) incorporating graphene (GE) have been an active area of research, both theoretical and experimental, due to their potential to yield devices with a wide variety of applications. In this paper, first-principles calculations are employed to investigate C$_{6}…
Designing electrolyte molecules for lithium batteries requires balancing electronic stability with appropriate Li+ solvation, yet the structural basis remains unclear across chemically diverse molecules. High-throughput screening expands the searchable space, but ranked candidates alone do not reve…
Equivariant atomistic models commonly use either Clebsch Gordan tensor product(CGTP) or edge aligned SO(2) operationsCGTP support general O(3) representations but become expensive at high angular degree, while existing local SO(2) frame methods are designed for SO(3) or the natural-parity subset of…
Atomistic descriptions of hydrogen diffusion and trapping at defects are essential for understanding hydrogen embrittlement. As the lightest solute in metals, hydrogen exhibits nuclear quantum effects that alter these processes even at room temperature. Explicit treatment of such effects is computa…
Pnictogen chalcohalide (MChX; M=Bi,Sb; Ch=S,Se; X=I,Br) solid solutions combine earth-abundant constituents, tunable band gaps ($1.2$-$2.1$ eV), and strong optical absorption, making them attractive for solar energy conversion. Yet their vast compositional space has so far prevented a systematic as…
The stopping power of charged particles is investigated using time-dependent density functional theory (TDDFT). Such simulations are made possible by recent advances in computational resources and numerical implementations of this first-principles method. In practice, DFT simulations widely employ…
Semi-metallic noble-metal dichalcogenides are promising materials for infrared optoelectronics, yet the origin and tunability of their nonlinear optical responses remain poorly understood. Here, we demonstrate in-situ mechanical control of photon-drag photocurrents in polycrystalline PtSe$_2$ thin…
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…