Autonomous intelligent agents for accelerated materials discovery,Chemical Science

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Autonomous intelligent agents for accelerated materials discovery
Chemical Science ( IF 7.6 ) Pub Date : 2020-07-30 , DOI: 10.1039/d0sc01101k
Joseph H Montoya ₁ , Kirsten T Winther ₂ , Raul A Flores ₂ , Thomas Bligaard _{2,

3} , Jens S Hummelshøj ₁ , Muratahan Aykol ₁

Affiliation

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration–exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

中文翻译：

用于加速材料发现的自主智能代理

我们提出了一个用于自主材料发现的端到端计算系统。该系统旨在通过采用基于代理的顺序方法来决定要进行哪些实验，从而在大型高维材料搜索空间中实现成本效益的优化。在选择下一个实验时，智能体可以利用过去的知识、替代模型、逻辑、热力学或其他物理结构、启发式规则和不同的探索-利用策略。我们展示了一系列示例：（i）如何模拟寻找满足相对稳定性目标的材料的发现活动来设计新的代理，以及（ii）如何在实际的发现活动中部署这些代理来控制外部运行的实验，例如本工作中基于云的密度泛函理论模拟。在涵盖金属氧化物、磷化物、硫化物和合金等一系列二元和三元化学物质的 16 个活动样本中，该自主平台在没有研究人员干预的情况下发现了 383 种新的稳定或接近稳定的材料。

更新日期：2020-08-20

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