Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances

Second-harmonic generation tensors from high-throughput density-functional perturbation theory

Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

Multi-fidelity machine learning for predicting bandgaps of nonlinear optical crystals

Nonlinear optical (NLO) materials are of great importance in modern optics and industry because of their intrinsic capability of wavelength conversion. Bandgap is a key property of NLO crystals. In recent years, machine learning (ML) has become a powerful tool to predict the bandgaps of compounds before synthesis. However, the shortage of available experimental data of NLO crystals poses a significant challenge for the exploration of new NLO materials using ML. In this work, we proposed a new multi-fidelity ML approach based on the multilevel descriptors developed by us (Z.-Y. Zhang, X. Liu, L. Shen, L. Chen and W.-H. Fang, J. Phys. Chem. C, 2021, 125, 25175-25188) and the gradient boosting regression tree algorithm. The calculated and experimental bandgaps of NLO crystals were collected as the low- and high-fidelity labels, respectively. The experimental values were predicted based on chemical compositions of crystals without prior knowledge about crystal structures. The multi-fidelity ML model overcame the performance of single-fidelity predictor. Furthermore, it was observed that less accurate predictions on the low-fidelity label may result in more accurate prediction on the high-fidelity label, at least in the present case. Using the multi-fidelity ML model with the best performance in this work, the mean absolute error on the test set of experimental bandgaps was 0.293 eV, which is smaller than that using the single-fidelity model (0.355 eV). It is far from perfect but accurate enough as an effective computational tool in the first step to discover novel NLO materials.

A Machine Learning Study on High Thermal Conductivity Assisted to Discover Chalcogenides with Balanced Infrared Nonlinear Optical Performance

Exploration of novel nonlinear optical (NLO) chalcogenides with high laser-induced damage thresholds (LIDT) is critical for mid-infrared (mid-IR) solid-state laser applications. High lattice thermal conductivity (κL ) is crucial to increasing LIDT yet often neglected in the search for NLO crystals due to lack of accurate κL data. A machine learning (ML) approach to predict κL for over 6000 chalcogenides is hereby proposed. Combining ML-generated κL data and first-principles calculation, a high-throughput screening route is initiated, and ten new potential mid-IR NLO chalcogenides with optimal bandgap, NLO coefficients, and thermal conductivity are discovered, in which Li2 SiS3 and AlZnGaS4 are highlighted. Big-data analysis on structural chemistry proves that the chalcogenides having dense and simple lattice structures with low anisotropy, light atoms, and strong covalent bonds are likely to possess higher κL . The four-coordinated motifs in which central cations show the bond valence sum of +2 to +3 and are from IIIA, IVA, VA, and IIB groups, such as those in diamond-like defect-chalcopyrite chalcogenides, are preferred to fulfill the desired structural chemistry conditions for balanced NLO and thermal properties. This work provides not only an efficient strategy but also interpretable research directions in the search for NLO crystals with high thermal conductivity.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Maximized Frequency Doubling through the Inverse Design of Nonlinear Metamaterials

The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for a nonlinear metamaterial. The algorithm produces a plasmonic pattern that can maximize the second-order nonlinear effect of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm. The optimal pattern produced yielded second-harmonic generation from the nanolaminate with normal incident fundamental light. The deep learning architecture applied in this research can be expanded to other optical responses and light-matter interaction processes.

Mg2In3Si2P7: A Quaternary Diamond-like Phosphide Infrared Nonlinear Optical Material Derived from ZnGeP2

Balancing the second-harmonic generation (SHG) coefficient, band gap, and birefringence is a vital but addressable challenge for designing infrared nonlinear optical materials. By applying a "rigidity-flexibility coupling" strategy, a quaternary diamond-like phosphide, Mg₂In₃Si₂P₇, with wurtzite-type superstructure was successfully designed and synthesized. Remarkably, it achieved the rare coexistence of giant second-harmonic generation (2 × ZnGeP₂ and 7.1 × AgGaS₂), suitable band gap (2.21 eV), moderate birefringence (0.107), and wide IR transparent range (0.56-16.4 μm). First-principles calculations revealed that the giant SHG response and large birefringence can be attributed to the synergy of arrangement-aligned [InP₄] and [SiP₄] tetrahedra. This work not only opens a new avenue for designing advanced infrared nonlinear optical materials but also may spur more explorations on quaternary diamond-like pnictides.