TL;DR
This work addresses a critical gap in data-driven modeling of low-dimensional quantum materials by curating a dataset of 254 functionalized and doped graphene and silicon carbide quantum dots with DFT-computed HOMO and LUMO energies, establishing a foundation for high-throughput screening and inverse design of quantum dots.
ABSTRACT Frontier orbital energies govern charge transfer, energy‐level alignment, and optoelectronic performance in organic molecules, yet their prediction across vast chemical spaces remains challenging because they arise from coupled effects of functionality, conjugation, and topology. In this study, we develop an i...
Here, reported ML-assisted studies on MXene prediction and design are organized by target property and workflow, and ML–DFT screening, graph neural networks, uncertainty quantification, active learning, and interpretability are examined.
This review critically surveys the integration of DFT and ML across the energy-materials landscape and concludes that the DFT - ML framework has matured from a proof of concept into an important engine of energy materials discovery.
Machine-learning interatomic potentials (MLIPs) enable nanosecond-scale atomistic simulations of inorganic semiconductor nanocrystals, but low errors on held-out configurations do not necessarily guarantee stable molecular dynamics. We benchmark five graph-neural-network MLIPs, SchNet, PaiNN, NequIP, Allegro and MACE,...
This work analyzes the effect of hydrostatic pressure on the predicting excitonic optical response of a 2D Ultra-thin GaAs-system. The data are generated from a numerical model incorporating quantum confinement and excitonic effects, capturing the system's response to incident photon energy and applied pressure. To red...
An efficient and cost-effective approach for evaluating electronic excitations in functional materials is presented, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques.
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