Elements: FLARE infrastructure for reproducible active learning of Bayesian force fields for ex-machina exascale molecular dynamics

能源储存和转换技术急需的进步依赖于我们设计和理解这些系统核心的下一代关键功能材料的能力。控制电池、催化剂和燃料电池功能的基本物理效应起源于原子水平。分子动力学模拟是材料研究中不可或缺的工具，具有广泛的适用性，因为它们能够探测原子运动的微观细节，并预测许多材料的热力学，反应动力学和离子扩散系数。机器学习方法正在改变复杂材料的模拟方式;因此需要软件工具来使这种过渡更快，更顺利。我们的主要目标是推进机器学习方法，并创建用于构建准确快速仿真模型的软件，这些模型包含其预测的原则性不确定性，这是原子建模之外的许多数据科学领域非常理想的目标。原则上的不确定性量化对于非平衡动力学的预测尤其重要，其中罕见的重要事件，例如键的断裂或原子迁移，决定了材料的性能，但涉及不太可能在天真无偏训练集中的原子配置。我们旨在开发的工具将加速和自动化催化，电池，热涂层，软结构和功能材料以及致动器等领域的计算研究工作。目前基于机器学习的力场工具基于非参数方法，只能提供没有不确定性的估计，需要大量的训练数据，并且对于不同种类的大量原子的评估是缓慢的。我们的目标是开发社区软件基础设施，以实现模拟复杂材料非平衡动力学的新范式，其中ML模型自动训练并显着加速从头算模拟，以最小的准确性损失保持精确的物理对称性。我们将创建并免费传播FLARE（原子罕见事件的快速学习），这是一个并行化的数据库驱动的自动化框架，将ML模型训练与高保真DFT计算紧密耦合，使用严格的模型不确定性通过闭环主动学习来指导数据采集。专门设计的多体多物种内核和系统超参数优化工具将允许模型映射到快速列表贝叶斯力场，在广泛使用的MD软件中实现，旨在提高exascale计算性能。其结果将是能够以接近DFT的精度和预测的不确定性对数百万原子的材料系统进行MD模拟。所提出的基础设施的独特优点是（1）需要最少量DFT数据的自动化训练，（2）包含原则贝叶斯不确定性的预测，（3）比从头算分子动力学快至少5个数量级的可扩展性能，以及（4）能够记录训练和预测工作流程的完整来源和再现性信息。该奖项由NSF高级网络基础设施办公室颁发该奖项由NSF数学和物理科学理事会的材料研究部和化学部以及NSF工程理事会的化学、生物工程、环境和运输系统部共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Much needed progress in technologies for energy storage and conversion relies on our ability to design and understand next-generation key functional materials at the core of these systems. The fundamental physical effects that govern the functions of batteries, catalysts and fuel cells originate at the atomic level. Molecular dynamics simulations are indispensable tools with broad applicability for materials research due to their ability to probe microscopic details of atomic motion and predict thermodynamics, reaction kinetics and ionic diffusivities of many materials. Machine learning approaches are transforming how simulations of complex materials are performed; hence software tools are needed to make this transition faster and smoother. Our main goal is to advance machine learning methods and create software for constructing accurate fast simulation models that contain principled uncertainty of their predictions, which is a highly desirable target in many data science areas beyond atomistic modeling. Principled uncertainty quantification is especially critical for prediction of non-equilibrium dynamics, where rare important events, such as the breaking of bonds or atomic migration, determine the material’s performance but involve atomic configurations that are unlikely to be in a naive unbiased training set. Tools that we aim to develop will accelerate and automate computational research efforts in the fields of catalysis, batteries, thermal coatings, soft structural and functional materials, and actuators, to name a few.Currently available tools for machine learning based force fields are based on non-parametric methods that only provide estimates without uncertainties, require large amounts of training data, and are slow to evaluate for large numbers of atoms of different species. Our goal is to develop community software infrastructure to enable a new paradigm of simulating non-equilibrium dynamics of complex materials, where ML models are automatically trained and dramatically accelerate ab-initio simulations on-the-fly, preserving exact physical symmetries with minimal accuracy loss. We will create and freely disseminate FLARE (Fast Learning of Atomistic Rare Events), a parallelized database-driven automation framework tightly coupling ML model training with high-fidelity DFT computations, using rigorous model uncertainty to guide data acquisition via closed-loop active learning. Specially designed many-body multi-species kernels and tools for systematic hyperparameter optimization will allow the models to be mapped to fast tabulated Bayesian force fields, implemented in the widely used MD software aimed at exascale computing performance. The result will be the ability to perform MD simulations of materials systems of millions of atoms at near-DFT accuracy and with predictive uncertainty. The unique advantages of the proposed infrastructure are (1) the automated training requiring minimal amounts of DFT data, (2) predictions containing principled Bayesian uncertainty, (3) scalable performance of at least 5 orders of magnitude faster than ab-initio molecular dynamics, and (4) ability to record full provenance and reproducibility information of training and prediction workflows.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences and the Division of Chemical, Bioengineering, Environmental and Transport Systems within the NSF Directorate of Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.