Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling’s rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles.

Interlayer excitons (IXs) in two-dimensional (2D) heterostructures provide an exciting avenue for exploring optoelectronic and valleytronic phenomena. Presently, valleytronic research is limited to transition metal dichalcogenide (TMD) based 2D heterostructure samples, which require strict lattice (mis) match and interlayer twist angle requirements. Here, we explore a 2D heterostructure system with experimental observation of spin-valley layer coupling to realize helicity-resolved IXs, without the requirement of a specific geometric arrangement, i.e., twist angle or specific thermal annealing treatment of the samples in 2D Ruddlesden–Popper (2DRP) halide perovskite/2D TMD heterostructures. Using first-principle calculations, time-resolved and circularly polarized luminescence measurements, we demonstrate that Rashba spin-splitting in 2D perovskites and strongly coupled spin-valley physics in monolayer TMDs render spin-valley-dependent optical selection rules to the IXs. Consequently, a robust valley polarization of ∼14% with a long exciton lifetime of ∼22 ns is obtained in type-II band aligned 2DRP/TMD heterostructure at ∼1.54 eV measured at 80 K. Our work expands the scope for studying spin-valley physics in heterostructures of disparate classes of 2D semiconductors.

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Magneto-ionics, real-time ionic control of magnetism in solid-state materials, promise ultralow-power memory, computing, and ultralow-field sensor technologies. The real-time ion intercalation is also the key state-of-charge feature in rechargeable batteries. Here, we report that the reversible lithiation/delithiation in molecular magneto-ionic material, the cathode in a rechargeable lithium-ion battery, accurately monitors its real-time state of charge through a dynamic tunability of magnetic ordering. The electrochemical and magnetic studies confirm that the structural vacancy and hydrogen-bonding networks enable reversible lithiation and delithiation in the magnetic cathode. Coupling with microwave-excited spin wave at a low frequency (0.35 GHz) and a magnetic field of 100 Oe, we reveal a fast and reliable built-in magneto-ionic sensor monitoring state of charge in rechargeable batteries. The findings shown herein promise an integration of molecular magneto-ionic cathode and rechargeable batteries for real-time monitoring of state of charge.