arXiv subjects

Yizhi Zhu

Publications and source records attributed to Yizhi Zhu.

5 recordsLinked to original sources

arXiv2026

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs

Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale for high-throughput defect databases. However, these methods are computationally expensive, making machine-learning force fields (MLFFs) an attractive alternative for accelerating structural relaxations. Most existing MLFFs are based on graph neural networks (GNNs), which can suffer from oversmoothing, oversquashing, and poor representation of long-range interactions. Both of these issues are especially of concern when modeling point defects. To address these challenges, we introduce the \textit{Accelerated Deep Atomic Potential Transformer} (ADAPT), an MLFF that replaces graph representations with a direct coordinates-in-space formulation and explicitly considers all pairwise atomic interactions. Atoms are treated as tokens, with a Transformer encoder modeling their interactions. Applied to a dataset of silicon point defects, ADAPT achieves a roughly 22% reduction in force and a roughly 40 percent reduction in energy prediction error relative to a state-of-the-art GNN-based model, while requiring only a fraction of the computational cost.

cs.LG↗

arXiv2026

On The Finetuning of MLIPs Through the Lens of Iterated Maps With BPTT

Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic potentials (MLIPs), which strive to faithfully reproduce first-principles computed forces. We propose a fine-tuning method to be used on a pretrained MLIP in which we create a fully-differentiable end-to-end simulation loop that optimizes the predicted final structures directly. Trajectories are unrolled and gradients are tracked through the entire relaxation. We show that this method consistently improves performance across all evaluated pretrained models; resulting in an average of roughly 32% reduction in prediction error. Interestingly, we show the process is robust to substantial variation in the relaxation setup, achieving negligibly different results across varied hyperparameter and procedural modifications.

cond-mat.mtrl-sci↗

arXiv2025

Identifying high performance spectrally-stable quantum defects in diamond

Point defects in semiconductors are becoming central to quantum technologies. They can be used as spin qubits interfacing with photons, which are fundamental for building quantum networks. Currently, the most prominent quantum defect in diamond is the nitrogen-vacancy (NV) center. However, it suffers from spectral diffusion that negatively impacts optical coherence and is due to the coupling of the emission energy with uncontrolled electric fields. The group IV vacancy complexes on the other hand have shown to be significantly more spectrally-stable as they are centrosymmetric and thus immune to the linear Stark shift. They however suffer from several issues ranging from low operation temperature to low optical efficiency due to dark states and difficulty in stabilizing the right defect charge state. Here we search for alternative to the group IV vacancy complex in diamond by systematically evaluating all possible vacancy complex using high-throughput first-principles computational screening. We identify the defects that combine centrosymmetry, emission in the visible range, as well as favorable and achievable electronic structure promoting higher operation temperature and defect levels well within the band gap. We find Zn$V^{-2}$ to be especially appealing.

cond-mat.mtrl-sci↗

arXiv2023

Quantum spin probe of single charge dynamics

Electronic defects in semiconductors form the basis for many emerging quantum technologies. Understanding defect spin and charge dynamics in solid state platforms is crucial to developing these building blocks, but many defect centers are difficult to access at the single-particle level due to the lack of sensitive readout techniques. A method for probing optically inactive spin defects would reveal semiconductor physics at the atomic scale and advance the study of new quantum systems. We exploit the intrinsic correlation between the charge and spin states of defect centers to measure defect charge populations and dynamics through the steady-state spin population, read-out at the single-defect level with a nearby optically active qubit. We directly measure ionization and charge relaxation of single dark defects in diamond, effects we do not have access to with traditional coherence-based quantum sensing. These spin resonance-based methods generalize to other solid state defect systems in relevant materials.

quant-ph↗

arXiv2021

Five-second coherence of a single spin with single-shot readout in silicon carbide

An outstanding hurdle for defect spin qubits in silicon carbide (SiC) is single-shot readout - a deterministic measurement of the quantum state. Here, we demonstrate single-shot readout of single defects in SiC via spin-to-charge conversion, whereby the defect's spin state is mapped onto a long-lived charge state. With this technique, we achieve over 80% readout fidelity without pre- or post-selection, resulting in a high signal-to-noise ratio (SNR) that enables us to measure long spin coherence times. Combined with pulsed dynamical decoupling sequences in an isotopically purified host material, we report single spin T2 > 5s, over two orders of magnitude greater than previously reported in this system. The mapping of these coherent spin states onto single charges unlocks both single-shot readout for scalable quantum nodes and opportunities for electrical readout via integration with semiconductor devices.

quant-ph↗