arXiv subjects

Ruyi Song

Publications and source records attributed to Ruyi Song.

5 recordsLinked to original sources

arXiv2026

Performance of Tkatchenko-Scheffler Dispersion Method with Updated van der Waals Radii: Importance for Alkali-Containing Systems

The Tkatchenko-Scheffler (TS) pairwise method to calculate dispersion interactions is a widely used approach to incorporate missing long-range van der Waals contributions in semilocal and hybrid density functional calculations. Despite numerous refinements of the approach to include many-body terms, the original formulation still remains highly relevant as an efficient and robust method, especially for organic and/or insulating materials. In 2018, Fedorov et al. reported updated van der Waals radii to the seminal work published in 2009. The present work examines the accuracy of the TS method with updated van der Waals radii (abbreviated as TS_2018), coupled with the semilocal Perdew-Burke-Ernzerhof density functional, for structural predictions of semiconducting and insulating materials in comparison to the non-local many-body dispersion method and the original TS method (TS_2009). Special attention is paid to materials containing alkali elements, for which the TS_2009 method exhibits a large overbinding, associated with potentially large errors in predicted atomic structures. We also consider a more narrow reformulation (TS_alkali) where only the the alkali atoms are corrected, so the method remains otherwise compatible with TS_2009. The binding energy curves of five alkali dimers are used to assess the TS_2009 and the TS_2018 methods in comparison to the random phase approximation. Using 45 inorganic solid compounds with available experimental reference data, as well as three widely studied, Cs-containing halide perovskites, CsPb$X_3$ ($X$ = Cl, Br, I), we then examine the performance of the TS_2018 and TS_alkali approaches compared to TS_2009 and the beyond-pairwise, nonlocal many-body dispersion method; the latter found to give good results as well.

cond-mat.mtrl-sci↗

arXiv2024

Optical activity of chiral excitons

Recent activity in the area of chiroptical phenomena has been focused on the connection between structural asymmetry, electron spin configuration and light matter interactions in chiral semiconductors. In these systems, spin-splitting phenomena emerge due to inversion symmetry breaking and the presence of extended electronic states, yet the connection to chiroptical phenomena is lacking. Here, we develop an analytical effective mass model of chiral excitons, parameterized by density functional theory. The model accounts for parity mixing of the band edge Bloch functions resulting from polar distortions, resulting in magnetic dipole allowed transitions. Through the study of a prototypical chiral 2D hybrid perovskite semiconductor, we show that circular dichroism of the chiral exciton and its interband continuum emerges from spin-splitting via cross coupling of Rashba-like and chiral/helical spin-texture components. As a counterpoint, we apply our model to describe chiroptical properties of excitons in perovskite nanocrystals that occur without chiral lattice distortions.

cond-mat.mes-hall↗

arXiv2024

SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks

StarCraft Multi-Agent Challenge (SMAC) has been one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL), where the specific task is to control a set number of allied units to defeat enemy forces. Traditional MARL algorithms often require interacting with the environment for millions of steps to train a parametric model, of which the resulting policies are typically non-interpretable with weak transferability. In this paper, we introduce SMAC-R1 which is based on the Qwen2.5-7B-Base LLM distilled from DeepSeek-Coder-v2.5-236B. Similar to online reinforcement learning after behavior cloning in offline learning process, in our pipeline, agents leverage the DeepSeek LLM to generate decision tree code by providing task descriptions, and the agents are further self-reflected using feedback from the rewards provided by the environment. Based on that, we augment the generated scripts to fine-tune a small LLM, Qwen2.5-7B-Base, to distill the decision-making ability via Supervised Fine-Tuning (SFT) and enhance the script generation ability by the Group Relative Policy Optimization (GRPO) algorithm. We conduct experiments in the original 23 SMAC tasks and 10 newly-designed tasks to demonstrate that our method can produce high-quality, interpretable decision trees with minimal environmental exploration. Moreover, these scripts exhibit strong transferability, successfully applying to homogeneous SMAC environments without modification. We believe this approach offers a new direction for solving decision-making tasks and domain-specific LLM training pipelines in the future.

cs.AI↗

arXiv2023

Self-Supervised Generative Models for Crystal Structures

Drawing inspiration from the achievements of natural language processing, we adopt self-supervised learning and utilize an equivariant graph neural network to develop a unified platform designed for training generative models capable of generating crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of incorporating large-scale assessment on the reliability of generated structures into the training process, we utilize the generative adversarial network (GAN) with its discriminator being a cost-effective evaluator for the generated structures, resulting in notable improvements in model performance. We demonstrate the utility of our model in finding the optimal crystal structure under predefined conditions. Without reliance on properties acquired experimentally or numerically, our model further displays its capability to comprehend the mechanism of crystal structure formation through its ability to grouping chemically similar elements. Therefore, this paper extends an invitation to explore deeper into the scientific understanding of material structures through generative models, offering a fresh perspective on broadening the scope and efficacy of machine learning in material science.

cond-mat.mtrl-sci↗

arXiv2022

Large scale quantum chemistry with Tensor Processing Units

We demonstrate the use of Google's cloud-based Tensor Processing Units (TPUs) to accelerate and scale up conventional (cubic-scaling) density functional theory (DFT) calculations. Utilizing 512 TPU cores, we accomplish the largest such DFT computation to date, with 247848 orbitals, corresponding to a cluster of 10327 water molecules with 103270 electrons, all treated explicitly. Our work thus paves the way towards accessible and systematic use of conventional DFT, free of any system-specific constraints, at unprecedented scales.

physics.comp-ph↗