AI 中文总结
研究人员开发JANUS多模态神经采样器，用于无序材料的热力学采样，其以远少于传统方法的能量评估次数重现平衡性质，可用于合金逆设计及半导体缺陷探索，为相关研究提供了基础。
AI 中文摘要
无序材料中的诸多问题需要超越固定成分与体积的采样，其中原子种类与结构的耦合变化会产生离散-连续采样问题，其计算成本高得难以承受。本文提出JANUS，一种多模态神经采样器，它通过等变图神经网络耦合连续与掩码离散扩散，该网络直接从能量评估中训练，无需预先生成的平衡数据。在基准伊辛（Ising）和等压ΔμNPT合金系统中，JANUS重现了参考蒙特卡洛（Monte Carlo）平衡可观测量，且以超过三个数量级更少的能量评估次数恢复了自由能与相行为。在多组分合金中，JANUS支持向规定的化学短程有序及增强的体积模量进行条件引导，当与大语言模型进化智能体耦合时，可实现平衡光学与力学性能的高效逆设计。在硅、金刚石等半导体中，JANUS在巨正则μVT系综中探索涵盖15种元素的空位与掺杂剂，恢复了包括硅E中心在内的已确立缺陷，并识别出量子工程用的新型候选缺陷对与三重组，包括硅中的S-Ti及金刚石中的B-O-O，其深隙态经杂化泛函密度泛函理论验证。通过统一离散位点种类与连续结构及体积弛豫，JANUS为化学无序材料的热力学采样、表征及逆设计提供了基础。
英文摘要
Many problems in disordered materials require sampling beyond fixed composition and volume, where coupled changes in atomic identities and structure create a prohibitively expensive discrete-continuous sampling problem. Here we introduce JANUS, a multimodal neural sampler that couples continuous and masked discrete diffusion through an equivariant graph neural network trained directly from energy evaluations, without pre-generated equilibrium data. In benchmark Ising and isobaric $ΔμNPT$ alloy systems, JANUS reproduces reference Monte Carlo equilibrium observables and recovers free energies and phase behavior with more than three orders of magnitude fewer energy evaluations. In multicomponent alloys, JANUS enables conditional steering toward prescribed chemical short-range order and enhanced bulk modulus and, when coupled to a large language model evolutionary agent, performs efficient inverse design for balanced optical and mechanical properties. In semiconductors like silicon and diamond, JANUS explores vacancies and dopants spanning 15 elements in grand-canonical $μVT$ ensembles, recovers established defects including the silicon $E$ centre, and identifies new candidate defect pairs and triplets for quantum engineering, including S-Ti in silicon and B-O-O in diamond, with deep in-gap states validated by hybrid-functional density functional theory. By unifying discrete site identities with continuous structural and volumetric relaxation, JANUS provides a foundation for thermodynamic sampling, characterization and inverse design of chemically disordered materials.
