AI 中文总结
本研究发布生物炭分子模型的社区路线图，梳理现有建模方法，指出第一代模型的优势与不足，明确待解决问题及社区优先事项，强调借鉴其他碳材料方法以推动领域进展。
AI 中文摘要
生物炭是生物质热解产生的无序碳质材料，应用范围涵盖土壤改良、水体修复、碳储存及功能材料。尽管其与煤、干酪根、活性炭等其他无序碳具有结构特征，但针对生物炭模型的问题具有特殊性，没有单一模型能同等出色地回答所有问题。模型的有用性必须相对于特定问题来定义，并通过独立实验可观测量验证。这份源自CECAM研讨会的社区路线图，批判性地梳理了当前的分子方法：实验引导的自顶向下重建、仿生自底向上模拟及混合方法。我们认为，只要第一代模型构建在足够的长度尺度上，并明确控制微孔结构和本体化学性质，其成功程度比通常认可的更高。经典力场越来越能处理结构和平衡界面性质，而动态和反应性行为则需要在多尺度工作流中选择性使用反应性方法。一个并行且基本未解决的缺口涉及生物炭的矿物和灰分成分，以及材料在土壤老化过程中发生的变化。我们确定了当前模型无法可靠回答的七个开放问题，以及五项社区优先事项：力场基准测试、开放模型与数据存储库、共享分类与元数据标准、集成验证，以及可复现实践的培训。在这些方面，与实验学家的持续互动对将模型建立在真实可观测量上并记录其失效之处至关重要。通过借鉴煤、干酪根和黏土-有机质框架的可迁移方法，而非重复试错开发，将加速进展。
英文摘要
Biochars are disordered carbonaceous materials produced by biomass pyrolysis, with applications spanning soil amendment, water remediation, carbon storage, and functional materials. Although they share structural features with other disordered carbons such as coal, kerogen, and activated carbons, the questions posed to biochar models are distinct, and no single model can answer all of them equally well. Model usefulness must be defined relative to a specific question and validated against independent experimental observables. This community roadmap, arising from a CECAM workshop, critically maps current molecular approaches: experimentally guided top-down reconstruction, mimetic bottom-up simulation, and hybrid methods. We argue that first-generation models have been more successful than is often acknowledged, provided they are built at sufficient length scale and with explicit control over microporosity and bulk chemistry. Structural and equilibrium interfacial properties are increasingly tractable with classical force fields, whereas dynamic and reactive behaviours require selective use of reactive methods within multiscale workflows. A parallel, largely unaddressed gap concerns the mineral and ash components of biochar, and the changes the material undergoes during ageing in soil. We identify seven open questions current models cannot yet answer reliably, and five community priorities: force field benchmarking, open model and data repositories, shared classification and metadata standards, ensemble validation, and training in reproducible practice. Across these, sustained interaction with experimentalists is essential to ground models in real observables and document where they fail. Progress will be accelerated by adapting transferable methods from coal, kerogen, and clay-organic matter frameworks rather than repeating trial-and-error development.
