Tunable optical activities in chiral transition metal oxide nanoparticles.

Chiral transition metal oxides (TMOs) are widely used in various optoelectronic devices. However, the currently poor understanding of how the optical activities of TMOs can be regulated considerably hinders their applications. We have synthesized a series of chiral TMO nanoparticles (NPs), i.e., MoOx (x = 2, 2.4 and 2.5) and Co3O4. Compared with TMO NPs with L-/D-cysteine molecules as the capping ligand, L-/D-histidine-capped TMO NPs possess larger anisotropic factors (gabs), which are as high as ∼0.01 and ∼0.02 for L-/D-histidine-capped MoO2.5 and Co3O4 NPs, respectively. A nondegenerate coupled oscillator (NDCO) theoretical calculation confirms that L-/D-histidine molecules can generate a smaller electric dipole moment and thus induce higher optical activity than L-/D-cysteine molecules. Impressively, the chiral NPs exhibit broadband second harmonic generation. This work indicates that chiral TMO NPs have potential for application in nonlinear optical devices.

Regulating Optical Activity and Anisotropic Second-Harmonic Generation in Zero-Dimensional Hybrid Copper Halides.

Structural engineering permits the introduction of chirality into organic-inorganic hybrid metal halides (HMHs), which creates a promising and exclusive material for applications in various optoelectronics. However, the optical activity regulation of chiral HMHs remains largely unexplored. In this work, we have synthesized two pairs of lead-free chiral HMHs with a zero-dimensional tetrahedral arrangement, i.e., (R- and S-1-(1-naphthyl)ethylammonium)2CuCl4 and (R- and S-1-(2-naphthyl)ethylammonium)2CuCl4. The magnitude of optical activity in these HMHs can be efficiently modulated as a result of the different magnetic transition dipole moments. Furthermore, these HMHs exhibited effective second-harmonic generation (SHG) and distinct SHG-circular dichroism (CD), with (R-1-(1-naphthyl)ethylammonium)2CuCl4 having an anisotropy factor (gSHG-CD) of up to 0.41. This work not only provides insights into regulating the optical activity and anisotropic SHG effect of lead-free chiral HMHs but also confirms the feasibility of SHG-CD spectroscopy as a promising tool for characterizing the intrinsic optical activity of chiral materials.

Catalyst deep neural networks (Cat-DNNs) in singlet fission property prediction.

Many current deep neural network (DNN) models only focus on straightforward optimization over the given database. However, most numerical fitting procedures depart from physical laws. By introducing the concept of "catalysis" from physical chemistry, we propose that the physical correlations among molecular properties could spontaneously act as a catalyst in the DNNs, which increases the accuracy, and more importantly, guides the DNNs in the right way. These Catalysis-DNNs (Cat-DNNs) could precisely predict both the ground and excited-state properties, especially the molecules' screening with singlet fission character. We show that traditional machine learning metrics are not suitable for evaluating model accuracy in physical-chemical tasks and issue new physical errors. We believe that the agile transfer of fundamental physics or chemistry domain knowledge, like the catalyst, could significantly benefit both the architecture and application of artificial intelligence technology in the future.

Giant Optical Activity and Second Harmonic Generation in 2D Hybrid Copper Halides.

Hybrid organic-inorganic metal halides have emerged as highly promising materials for a wide range of applications in optoelectronics. Incorporating chiral organic molecules into metal halides enables the extension of their unique optical and electronic properties to chiral optics. By using chiral (R)- or (S)-methylbenzylamine (R-/S-MBA) as the organic component, we synthesized chiral hybrid copper halides, (R-/S-MBA)2 CuCl4 , and investigated their optical activity. Thin films of this material showed a record anisotropic g-factor as high as approximately 0.06. We discuss the origin of the giant optical activity observed in (R-/S-MBA)2 CuCl4 by theoretical modeling based on density functional theory (DFT) and demonstrate highly efficient second harmonic generation (SHG) in these samples. Our study provides insight into the design of chiral materials by structural engineering, creating a new platform for chiral and nonlinear photonic device applications of the chiral hybrid copper halides.

QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules.

In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry. In this work, we put forward the QM-symex with 173-kilo molecules. Each organic molecular in the QM-symex combines with the Cnh symmetry composite and contains the information of the first ten singlet and triplet transitions, including energy, wavelength, orbital symmetry, oscillator strength, and other quasi-molecular properties. QM-symex serves as a benchmark for quantum chemical machine learning models that can be effectively used to train new models of excited states in the quantum chemistry region as well as contribute to further development of the green energy revolution and materials discovery.

Symmetrical Graph Neural Network for Quantum Chemistry with Dual Real and Momenta Space.

Most of the current neural network models in quantum chemistry (QC) exclude the molecular symmetry and separate the well-correlated real space (R space) and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehendible method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to orbital symmetry for both ground and excited states. SY-GNN is an end-to-end model that can predict multiple properties in both K and R space within a single model, and it shows excellent performance in predicting both the absolute and relative R and K space quantities. Besides the numerical properties, SY-GNN can also predict orbital properties, providing the active regions of chemical reactions. We believe the symmetry-endorsed deep learning scheme covers the significant physics inside and is essential for the application of neural networks in QC and many other research fields in the future.

QM-sym, a symmetrized quantum chemistry database of 135 kilo molecules.

Applying deep learning methods in materials science research is an important way of solving the time-consuming problems of typical ab initio quantum chemistry methodology, but due to the size of large molecules, large and uncharted fields still exist. Implementing symmetry information can significantly reduce the calculation complexity of structures, as they can be simplified to the minimum symmetric units. Because there are few quantum chemistry databases that include symmetry information, we constructed a new one, named QM-sym, by designing an algorithm to generate 135k organic molecules with the Cnh symmetry composite. Those generated molecules were optimized to a stable state using Gaussian 09. The geometric, electronic, energetic, and thermodynamic properties of the molecules were calculated, including their orbital degeneracy states and orbital symmetry around the HOMO-LUMO. The basic symmetric units were also included. This database p rovides consistent and comprehensive quantum chemical properties for structures with Cnh symmetries. QM-sym can be used as a benchmark for machine learning models in quantum chemistry or as a dataset for training new symmetry-based models.

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction.

In this work, inspired by Phillips's ionicity theory in solid-state physics, we directly sort out the critical factors of the band gap's feature correlations in the machine learning architected with the Lasso algorithm. Even based on a small 2D materials data set, we can fundamentally approach an accurate and rational model about the band gap and exciton binding energy with robust transferability to other databases. Our machine learning outputs can reveal the exact physics pictures behind the predicted quantity as well as the "secondary understanding" of the correlation between the approximated physics models in exciton. This work stresses the significant value of physics endorsement on the machine learning (ML) algorithm and provides a symbolic regression solution for the "few-shot" training scheme for ML technology in materials science. Moreover, physics-inspired secondary understanding could be an essential supplement for ML in scientific research fields.

fvs-Si48: a direct bandgap silicon allotrope.

A structurally stable silicon allotrope is predicted by means of first principles calculations. This new structure is composed of a six-membered ring, a five-membered ring and a three-membered ring with the space group PA3[combining macron] and fvs topology, which is named fvs-Si48. The calculations of geometrical, vibrational, and electronic and optical properties reveal that fvs-Si48 has good mechanical stability with a mass density of 1.86 g cm-3. More importantly, it is a semiconductor with a direct band gap of 2.15 eV. From the analysis of its optical properties, there is the possibility of its synthesis in theory. This fvs-Si48 could have a wide range of applications in photo catalysts, optoelectronics, hydrogen storage and aerospace engineering.