Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments.Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-specific data effectively (e.g., crystal structures), and integrating experimental workflows.To address these challenges, we introduce LLaMP, a hierarchical multi-agent framework designed to emulate the materials science research workflow.The high-level supervisor agent decomposes user requests into sub-tasks and coordinates with specialized assistant agents.These assistant agents handle domain-specific tasks, such as retrieving and processing data from the Materials Project (MP) or conducting simulations as needed.This pipeline facilitates iterative refinement of material property retrieval and enables the simulation of real-world research workflows.To ensure reliability, we propose a novel metric combining uncertainty and confidence estimate to evaluate the selfconsistency of responses from LLaMP and baseline methods.Our experiments demonstrate LLaMP's superior performance in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations using pre-trained interatomic potentials.Unlike prior work focused solely on material property prediction or discovery, LLaMP serves as a foundation for autonomous materials research by combining grounded informatics and enabling iterative experimental processes.Code and live demo are available at https://github.com/chiang-yuan/llamp.

Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references. We introduce MLIP Arena, a benchmark platform that evaluates force field performance based on physics awareness, chemical reactivity, stability under extreme conditions, and predictive capabilities for thermodynamic properties and physical phenomena. By moving beyond static DFT references and revealing the important failure modes of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency. The Python package and online leaderboard are available at https://github.com/atomind-ai/mlip-arena.

Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, or improve the robustness of a policy to an unexpected perturbance. We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Specifically, we design an intrinsic reward based on an information-theoretic diversity objective. Our final objective alternately constraints on the diversity of the strategies and on the extrinsic reward. We solve the constrained optimization problem by casting it as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks. Compared to baseline methods, DGPO achieves comparable rewards, while discovering more diverse strategies, and often with better sample efficiency.