The convergence of proton conduction and multiferroics is generating a compelling opportunity to achieve strong magnetoelectric coupling and magneto-ionics, offering a versatile platform to realize molecular magnetoelectrics. Here we describe machine learning coupled with additive manufacturing to accelerate the design strategy for hydrogen-bonded multiferroic macromolecules accompanied by strong proton dependence of magnetic properties. The proton switching magnetoelectricity occurs in three-dimensional molecular heterogeneous solids. It consists of a molecular magnet network as proton reservoir to modulate ferroelectric polarization, while molecular ferroelectrics charging proton transfer to reversibly manipulate magnetism. The magnetoelectric coupling induces a reversible 29% magnetization control at ferroelectric phase transition with a broad thermal hysteresis width of 160 K (192 K to 352 K), while a room-temperature reversible magnetic modulation is realized at a low electric field stimulus of 1 kV cm⁻¹. The findings of electrostatic proton transfer provide a pathway of proton mediated magnetization control in hierarchical molecular multiferroics.

质子传导和多铁性的融合为实现强磁电耦合和磁离子提供了一个引人注目的机会，为实现分子磁电提供了一个多功能平台。在这里，我们描述了机器学习与增材制造相结合，以加速氢键多铁性大分子的设计策略，伴随着磁性的强质子依赖性。质子转换磁电发生在三维分子异质固体中。它由一个分子磁体网络作为质子储存器组成，以调节铁电极化，而分子铁电体则为质子转移充电以可逆地操纵磁性。^-1。静电质子转移的发现为分级分子多铁性中质子介导的磁化控制提供了途径。