Machine learning is initiating to play a valuable role in building seamless connectivity between classical theories, scientific intuition, and tests from realistic optical responsive materials, however, this approach is still in its infancy. The light-matter interaction is an intricate phenomenon, the imagery of which needs inter-lively work between materials science, physics, chemistry, optics, and engineering. There are many opportunities for machine learning to be invoked at various- stages during the process of deciphering the mechanism of the light-matter interaction and while changing optical materials into imaging probes, data carriers or optical devices. In turn, the learning paradigms must be adaptable to solve domain problems.
Materials screening and discovery
Materials control light-matter interactions and determine the performances of optical devices. In turn, application-specific requirements constrain materials properties such as the electronic configuration, defect energetics, and structure and symmetry. Conventional trial-and-error methods used to discover ideal materials are time-consuming and resource-consuming. Fueled by the availability of extensive databases, materials discovery is presently trying to incorporate learning to uncover hidden empirical knowledge from both successful and failed tests, or for extracting atomic or molecular features. In several cases, learning models outperform traditional human-based strategies with great success. However, this does not mean that future has to completely rely on ML. To further enhance the role of ML, precise knowledge of a scientific problem is indispensable for optimizing ML algorithms. Moreover, experimental is always inescapable once training ML and improving its prediction capability, as noted by Oliynyk and co-staffs.
Optical observation of an object at the microscale or nanoscale yields invaluable and indispensable data. Ideally, microscopy should be quick operating, have super-determination and allow high throughput and multitasking. ML have to upgrade current microscopy to a fresh level with significantly enhanced performances in determination, throughput rate, and multitasking. Image analysis is crucial in extracting the sought after data, and specifically, ML algorithms have been reported to carry out well in image processing. For instance, deep learning enhances the imaging determination, accelerates the super-determination imaging speed, and carries out reconstruction.
Characterization of the structural-optical property correlation
A significant number of investigation projects aim at exploring and describing the light-matter interaction through characterization applying ML. Most of them try to establish structure-property correlations in a fast and accurate manner through microscopic characterization and demonstrating the capability of deep learning in recognizing subtle data hidden in a structure,,. With the target of device miniaturization, technology growth is currently shifting from microscale to nanoscale operations, where nanomaterials become key components. Future promising optical nanomaterials will need a carefully controlled design in terms of their optical , uniformity, and diversity. To accomplish this, single object characterization, which pushes the burden onto the microscopic determination, throughput, and denoising approach, is essential. By leveraging the programmed nature of ML, as well as its ability to rapidly find optimal solutions, high determination, high throughput, and signal-to-noise ratio is anticipated to be accomplished in single nanoparticle characterization.
Theoretical-calculation-guided optical materials exploration
The computation-guided design strategy has become a promising approach for materials studies based on high-throughput and accurate results. As a reliable theoretical method for probing the optical properties of both present and unknown materials, density functional theory DFT is able to supply initial prescriptions for experimental trials based on a comprehensive comprehending of optical processes, together with the electronic state properties such as the excitation power and oscillator strength in addition to the energetics of excited-state. These preliminary results will not only lower the experimental trial cost however also significantly contribute to the establishment of a broad-range optical materials database. Through the utilization of ML, the time-consuming issues of carrying out a theoretical calculation on complex systems will be further addressed, with the efficiency enhanced by several orders of magnitude. Given sufficiently large databases and sufficient domain knowledge, ML will accomplish faster simulation and analysis of electronic structures, as well as the determination of unknown physical properties or applications by uncovering the hidden latent data within the experimental data. The establishment of optical materials databases based on the collaboration between theoretical calculations and ML will accelerate unearthing the potential optical materials for a broad range of applications.
Designing materials with targeted optical properties
Quantum optics, optical communications, photonics, and green power production all need specific optical materials with enhanced performances. This means investigating the light-matter interaction within specific surroundings constraints, targeting one or extra vital characteristics. For instance, constraining light resonance in a cavity is a requirement of laser materials, while the Q factor reflects the efficiency of the cavity with respect to its lasing capability. in no distance time, by applying ML, Asano and co-staffs reported that the Q factor of two-dimensional photonic crystal nanocavities could be optimized to more than two orders of magnitude higher than the base cavity. Therefore, it seems that an increasing number of optical device fabrications and pilot trials will benefit from ML, increasing the success rate while reducing both the cost and fabrication time.
Explainable learning for optical materials
The properties and characteristics of optical materials are governed by underlying physical principles. However, as most existing learning methods are a black box in nature, the physics are ignored. The negative consequence of this situation is that investigators have difficulties interpreting the level to which a cause and effect have to be observed within a learning model. This indicates the significance of investigating explainable learning methods that will allow consumers to comprehend how the models make predictions and choices. Several directions may be viable to accomplish this objective. For instance, Bayesian deep learning, which leverages the data on uncertainty, will be capable of telling what the models recognize and what they do not know. It provides insights as soon as the black box fails. Another paradigm for explainable learning is human-in-the-loop learning, which combines human and intelligence to create operational learning models based on a continuous feedback loop. This means that expert knowledge will be fed into the model so that causal relations could be better constructed, and model bias will be avoided.
Addressing uncertainty to capture fundamental optical properties
learning relies on data, most of which are obtained through the testation of materials under variety of settings. The diverse combination of materials, investigation objectives, experimental settings, and operators will inevitably lead to inconsistency in the data distribution. This means that a model trained in one surrounding will fail to properly in a fresh surrounding. To investigate this issue, one should pay attention to the impact of the concept drift of data. In this scenario, transfer learning and drift learning may contribute to mitigating the problem. It is essential to investigate these learning paradigms in the area of light-matter interactions. This means that the comprehending and characterization of the domains and data topology are critical. fresh criteria to evaluate the similarity or dissimilarity of problem domains should be built. In summary, although ML will undoubtedly bring photonic materials into a fresh age, novel learning methods and benchmark datasets are needed to keep it pointed in the correct direction.
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