AI 中文总结
本研究开发全白盒神经网络NanoEQL，通过引入适配纳米晶体合成的算子与温度门控注意力池化策略，揭示了纳米晶体尺寸可由含三个可解释标量的线性方程描述，为纳米晶体合成设计及化学反应机制解析提供了可泛化范式。
AI 中文摘要
纳米晶体合成的深度学习模型可通过编码前驱体和反应条件来预测尺寸与形状，但其黑箱特性阻碍了对潜在合成机制的深入理解。本文开发了Nanocrystal Equation Learner（NanoEQL），一种全白盒神经网络，用于揭示纳米晶体合成的尺寸决定机制。该模型基于EQL架构，引入8种算子替代标准激活函数以适配纳米晶体合成中的数学方程；其中3种平滑算子解决了奇异算子在零点的梯度爆炸问题。为评估不同前驱体的权重，本文开发了温度门控注意力池化策略，将浓度驱动和反应性驱动的化学合成机制编码入温度门。NanoEQL模型表明，最终纳米晶体尺寸可由包含三个标量的线性方程描述，这三个标量分别代表纳米晶化能力（-Zp）、生长能力（Zrea）和外部输入势（-Zops）。这些可解释标量不仅推进了纳米晶体合成的理性设计，还建立了可泛化的范式，用于通过白盒机器学习解析化学反应机制。
英文摘要
Deep learning models of nanocrystal synthesis enable the prediction of size and shape by encoding precursors and reaction conditions. However, their black-box nature hinders gaining deep insights into the underlying synthetic mechanisms. Here, we develop the Nanocrystal Equation Learner (NanoEQL), a fully white-box neural network to unravel the size determination mechanisms of nanocrystal synthesis. Building on the EQL architecture, eight operators are introduced to replace standard activation functions to fit the mathematical equations in nanocrystal synthesis. Among these operators, three smoothed operators address the gradient explosion of singular operators at zero. To evaluate the weights of different precursors, we develop a temperature-gated attention pooling strategy that encodes concentration-driven and reactivity-driven chemical synthesis mechanisms into the temperature gate. The NanoEQL model illustrates that the final nanocrystal size can be described by a linear equation composed of three scalars representing nanocrystallization capability (-Zp), growth capability (Zrea), and external input potential (-Zops). These interpretable scalars not only advance the rational design of nanocrystal synthesis but also establish a generalizable paradigm for deciphering chemical reaction mechanisms through white-box machine learning.
