据悉，非绝热分子动力学（NA-MD）是模拟远离平衡态过程（如光化学反应和电荷传输）的有力工具。最近，NA-MD在凝聚态领域的应用受到了广泛关注，以期用于下一代能源和光电材料的研发。凝聚态物质的研究使人们能够使用高效的计算工具，如密度泛函理论（DFT）和经典路径近似（CPA）。然而，系统规模和模拟时间尺度仍然受到电子能量、力和非绝热耦合的昂贵从头计算（ab initio calculations）的严重限制。
该研究团队通过开发一种完全基于机器学习（ML）的方法来解决这些限制，该方法使用基于局部描述符的神经网络来获取所有上述属性。机器学习模型将基于密度泛函理论（DFT）和经典路径近似（CPA）实现的非绝热分子动力学（NA-MD）的目标属性与系统结构直接相关联。这些神经网络在小系统上进行训练后，被应用于大系统和长时间尺度，从而将NA-MD的能力提高数个量级。
研究人员以二硫化钼（MoS2）中电荷捕获和复合对缺陷浓度的依赖性为例来展示这种方法。缺陷是导致电荷损失的主要机制，从而导致性能下降。随着缺陷浓度的降低，电荷捕获速度减慢；然而，复合表现出复杂的依赖性，取决于其发生在自由电荷或捕获电荷之间，以及载流子和缺陷的相对浓度。随着温度的升高，非局域的浅陷阱可能变得局域化，从而改变捕获和复合行为。这种方法完全基于机器学习，填补了理论模型与实际实验条件之间的空白，使非绝热分子动力学能够在千原子系统和数纳秒的时间尺度上进行模拟。
附：英文原文
Title: Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning
Author: Liu, Dongyu, Wang, Bipeng, Wu, Yifan, Vasenko, Andrey S., Prezhdo, Oleg V.
Issue&Volume: 2024-8-30
Abstract: Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS2. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds.
DOI: 10.1073/pnas.2403497121
Source: https://www.pnas.org/doi/abs/10.1073/pnas.2403497121