相关论文: Monolayer Two-dimensional Materials Database (ML2D…
Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure-property relationships from experimental…
Based on recent advancements in using machine learning for classical density functional theory for systems with one-dimensional, planar inhomogeneities, we propose a machine learning model for application in two dimensions (2D) akin to…
Two-dimensional (2D) magnets have great potentials for applications in next-generation information devices. Since the recent experimental discovery of intrinsic 2D magnetism in monolayer CrI$_3$ and few-layer Cr$_2$Ge$_2$Te$_6$, intensive…
It is highly desirable to search for promising two-dimensional (2D) monolayer materials for deep insight of 2D materials and applications. We use first-principles method to investigate tetragonal perovskite oxide monolayers as 2D materials.…
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials…
The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across different length scales. Unlike conventional data-driven…
Starting from a single layer of NbS$_2$ grown on graphene by molecular beam epitaxy, the single unit cell thick 2D materials Nb$_{5/3}$S$_3$-2D and Nb$_2$S$_3$-2D are created using two different pathways. Either annealing under…
Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate.…
Two-dimensional (2D) materials with large band gaps and strong and tunable second-harmonic generation (SHG) coefficients play an important role in the miniaturization of deep-ultraviolet (DUV) nonlinear optical (NLO) devices. Despite the…
Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would enable high-throughout, high-accuracy…
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a…
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…
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing density functional theory (DFT) databases, primarily due to…
We compute the electronic structure of two-dimensional (2D) materials decorated with self-assembled organic monolayers using density functional theory. We find that 2D materials are strongly impacted by near-field electrostatic effects…
Recent progress in the synthesis and assembly of two-dimensional (2D) materials has laid the foundation for various applications of atomically thin layer films. These 2D materials possess rich and diverse properties such as layer-dependent…
2D magnets have emerged as a class of materials highly promising for studies of quantum phenomena and applications in ultra-compact spintronics. Current research aims at design of 2D magnets with particular functional properties. A…
Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we…
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional…
Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory…
The discovery of two-dimensional (2D) materials possessing switchable spontaneous polarization with atomic thickness opens up exciting opportunities to realize ultrathin, high-density electronic devices with potential applications ranging…