Equivariant diffusion models have emerged as the prevailing approach for generat- ing novel crystal materials due to their ability to leverage the physical symmetries of periodic material structures. However, current models do not effectively learn the joint distribution of atom types, fractional coordinates, and lattice structure of the crystal material in a cohesive end-to-end diffusion framework. Also, none of these models work under realistic setups, where users specify the desired characteristics that the generated structures must match. In this work, we introduce TGDMat, a novel text-guided diffusion model designed for 3D periodic material generation. Our approach integrates global structural knowledge through textual descriptions at each denoising step while jointly generating atom coordinates, types, and lattice structure using a periodic-E(3)-equivariant graph neural network (GNN). Extensive experiments using popular datasets on benchmark tasks reveal that TGDMat out- performs existing baseline methods by a good margin. Notably, for the structure prediction task, with just one generated sample, TGDMat outperforms all baseline models, highlighting the importance of text-guided diffusion. Further, in the genera- tion task, TGDMat surpasses all baselines and their text-fusion variants, showcasing the effectiveness of the joint diffusion paradigm. Additionally, incorporating textual knowledge reduces overall training and sampling computational overhead while enhancing generative performance when utilizing real-world textual prompts from experts. Code is available at https://github.com/kdmsit/TGDMat

Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language models (LLMs) or equivariant denoising models, each with complementary strengths: LLMs excel at handling discrete atomic types but often struggle with continuous features such as atomic positions and lattice parameters, while denoising models are effective at modeling continuous variables but encounter difficulties in generating accurate atomic compositions. To bridge this gap, we propose CrysLLMGen, a hybrid framework that integrates an LLM with a diffusion model to leverage their complementary strengths for crystal material generation. During sampling, CrysLLMGen first employs a fine-tuned LLM to produce an intermediate representation of atom types, atomic coordinates, and lattice structure. While retaining the predicted atom types, it passes the atomic coordinates and lattice structure to a pre-trained equivariant diffusion model for refinement. Our framework outperforms state-of-the-art generative models across several benchmark tasks and datasets. Specifically, CrysLLMGen not only achieves a balanced performance in terms of structural and compositional validity but also generates more stable and novel materials compared to LLM-based and denoising-based models Furthermore, CrysLLMGen exhibits strong conditional generation capabilities, effectively producing materials that satisfy user-defined constraints. Code is available at \url{https://github.com/kdmsit/crysllmgen}

The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average reward actor-critic algorithms, but average reward off-policy actor-critic is relatively less explored. In this work, we present both on-policy and off-policy deterministic policy gradient theorems for the average reward performance criterion. Using these theorems, we also present an Average Reward Off-Policy Deep Deterministic Policy Gradient (ARO-DDPG) Algorithm. We first show asymptotic convergence analysis using the ODE-based method. Subsequently, we provide a finite time analysis of the resulting stochastic approximation scheme with linear function approximator and obtain an $\epsilon$-optimal stationary policy with a sample complexity of $\Omega(\epsilon^{-2.5})$. We compare the average reward performance of our proposed ARO-DDPG algorithm and observe better empirical performance compared to state-of-the-art on-policy average reward actor-critic algorithms over MuJoCo-based environments.

An adaptive routing helps in evading early network saturation by steering data packets through the less congested area at the oppressive loaded situation. However, performances of adaptive routing are not always promising under all circumstances. Say for, given more freedom in choosing an alternate route on non-minimal paths for a substantially loaded network even may result in worsening network performances due to following longer route under adaptive routing. Here, underlying topology facilitates routing by offering more alternate short-cut routes on minimal or quasi-minimal paths. This work presents a congestion-aware (CA) adaptive routing for one-hop diagonally connected subnet-based mesh (SDmesh) network aiming to facilitate both performances and routing flexibility simultaneously. Our proposed technique on the selected system facilitates packet routing, offering more options in choosing an output link from minimal or quasi-minimal paths and hence helps in lowering packet delay by shortening the length of traversed traffic under the oppressive loaded situation. Furthermore, we have also employed a congestion-aware virtual input crossbar router aiming to split the entire network into two distinct logically separated sub-networks. It facilitates preserving important routing properties like deadlock, live-lock fairness, and other essential routing constraints. Experiments, conducted over two 8×8- and 12×12-sized networks, show an average improvement of 25--87.5% saturated latency and 60--83% throughput improvement under uniform traffic patterns for the proposed CA routing compared to centralized adaptive XY routing. Experimental results on application-specific PARSEC and SPLASH2 benchmark suites show an average of 22--50% latency and 23--30% throughput improvements by the proposed technique compared to centralized XY routing on the baseline mesh network. Moreover, experiments were also carried out to check the performance of the proposed routing method with different newly proposed deadlock-free adaptive routing approaches over the same subnet-based diagonal mesh (SDmesh) network and reported.