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Ruohan Chen

Publications and source records attributed to Ruohan Chen.

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Unveiling the impact of anti-site defects in magnetic transitions of few-layer MnBi2Te4 by operando heating

As the first experimentally discovered intrinsic magnetic topological insulator, MnBi2Te4 has attracted widespread attentions, providing a unique platform for the exploration of topological quantum phases, such as quantum anomalous Hall effect and axion insulator state. Despite the increasing number of potential factors affecting samples being identified, obtaining the high-quality device performance with desired topological quantum phases remains a challenge. In this work, by comparing the reflective magnetic circular dichroism (RMCD) of crystals with different defect densities that are characterized by atomically resolved scanning tunneling microscopy, we demonstrate that anti-site defects play an essential role in achieving ideal magnetic states. By measuring RMCD hysteresis loops with operando heating, we find that MnBi2Te4 few-layer samples are highly susceptible to thermal impact, even at temperature as low as 45{\deg}C. The magnetic behavior of heating-treated samples is akin to that of samples fabricated into devices, revealing the thermal impact on devices as well. Starting from few-layers with ideal layer-dependent magnetic order, thermal heating leads to the convergence of magnetization and transition fields between odd- and even-layers. The observed heating-induced magnetic evolution can serve as a valuable reference for assessing the sample quality or the density of anti-site defects. Our findings not only point out the long-standing hidden factor that arose controversies in MnBi2Te4, but also pave the way for controllably engineering the topological quantum phenomena.

cond-mat.mtrl-sci

Sign reversal of Berry curvature triple driven by magnetic phase transition in a ferromagnetic polar metal

While the time-reversal-even (T-even) nonlinear Hall effect has been extensively discussed in nonmagnetic materials, the impact of magnetic phase transition on it remains largely overlooked. Here, we report an abrupt enhancement of the T-even nonlinear Hall effect in non-centrosymmetric SrRuO3(111) thin films during the paramagnetic-ferromagnetic transition. Scaling analysis reveals a sign reversal of both the skew scattering and side jump contributions upon the emergence of magnetism, which we attribute to the sign change of Berry curvature triple on the Fermi surface. Density functional theory calculations support this interpretation, ascribing this behavior to distinctive origins of Berry curvature hot spots in paramagnetic and ferromagnetic phases. Our findings unveil the exchange-induced dramatic nonperturbative change of nonlinear Hall effect, and establish SrRuO3(111) thin films as a promising platform for exploring magnetically tunable nonlinear transport effects.

cond-mat.mtrl-sci

Ensemble Learning For Mega Man Level Generation

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating \emph{Mega Man} levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.

cs.LG