OMatG-flash：一种用于可扩展材料发现的强化伴随匹配全原子流图
OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery
AI总结：
OMatG-flash提出一种全原子流图，结合强化伴随匹配，以少一个数量级的推理步骤实现与最先进方法相当的无机材料生成性能，加速材料发现。
AI中文摘要：
新型无机材料的发现推动了计算和能源存储等关键领域的技术突破。生成式人工智能有望加速材料发现流程，但最先进的流模型和扩散模型仍受限于提出候选材料的成本。为此，我们引入了OMatG-flash，一种用于无机晶体结构预测（CSP）和从头生成（DNG）的全原子流图。OMatG-flash是一种帕累托最优的材料推理引擎，与现有流模型和扩散模型相比，它采样候选材料所需的推理步骤和墙钟时间减少了一个数量级，同时在基准测试中表现出与最先进方法相当的性能。为了实现训练后的微调，我们将强化伴随匹配应用于流图，进一步提高了无条件CSP任务上的匹配率和均方根误差（RMSE）。OMatG-flash展示了流图在加速生成高质量候选无机材料方面的潜力，并标志着在数据密集型材料发现工作流所需的样本吞吐量方面迈出了一步。
英文摘要：
The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the materials discovery pipeline, but state-of-the-art flow and diffusion models remain bottlenecked by the cost of proposing candidate materials. To address this, we introduce OMatG-flash, an all-atom flow map for inorganic crystal structure prediction (CSP) and de novo generation (DNG). OMatG-flash is a Pareto-optimal inference engine for materials, sampling candidate materials with an order of magnitude fewer inference steps and less wall-clock time than existing flow and diffusion models while demonstrating benchmark performance on par with the state-of-the-art. To enable post-training fine-tuning we apply Reinforce Adjoint Matching to flow maps, further improving match rates and RMSE on the unconditional CSP task. OMatG-flash showcases the potential of flow maps to accelerate generation of high-quality candidate inorganic materials and demonstrates a step forward in sample throughput necessary for data-hungry materials discovery workflows.
