arXiv subjects

Guanghui Cai

Publications and source records attributed to Guanghui Cai.

3 recordsLinked to original sources

arXiv2026

Response time of lateral predictive coding and benefits of modular structures

Lateral predictive coding (LPC) is a simple theoretical framework to appreciate feature detection in biological neural circuits. Recent theoretical work [Huang et al., Phys.Rev.E 112, 034304 (2025)] has successfully constructed optimal LPC networks capable of extracting non-Gaussian hidden input features by imposing the tradeoff between energetic cost and information robustness, but the resulting dynamical systems of recurrent interactions can be very slow in responding to external inputs. We investigate response-time reduction in the present paper. We find that the characteristic response time of the LPC system can be minimized to closely approaching the lower-bound value without compromising the mean predictive error (energetic cost) and the information robustness of signal transmission. We further demonstrate that optimal LPC networks taking a modular structural organization with extensively reduced number of lateral interactions are equally excellent as all-to-all completely connected networks, in terms of feature detection performance, response time, energetic cost and information robustness.

q-bio.NC↗

arXiv2023

Energy landscape and phase competition of CsV3Sb5-, CsV6Sb6-, and TbMn6Sn6-type Kagome materials

Finding viable Kagome lattices is vital for materializing novel phenomena in quantum materials. In this work, we performed element substitutions on CsV3Sb5 with space group P6/mmm, TbMn6Sn6 with space group P6/mmm, and CsV6Sb6 with space group R-3 m, respectively, as the parent compounds. A total of 4158 materials were obtained through element substitutions, and these materials were then calculated via density function theory in high-throughput mode. Afterward, 48 materials were identified with high thermodynamic stability (E_hull<5meV/atom). Furthermore, we compared the thermodynamic stability of three different phases with the same elemental composition and predicted some competing phases that may arise during material synthesis. Finally, by calculating the electronic structures of these materials, we attempted to identify patterns in the electronic structure variations as the elements change. This work provides guidance for discovering promising AM3X5/AM6X6 Kagome materials from a vast phase space.

cond-mat.supr-con↗

arXiv2023

Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning

DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values.

physics.comp-ph↗