Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Speech as a Biomarker for Depression

BACKGROUND

Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech.

OBJECTIVE

The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis.

CONCLUSION

Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.

Reducing the Quantum Many-Electron Problem to Two Electrons with Machine Learning

An outstanding challenge in chemical computation is the many-electron problem where computational methodologies scale prohibitively with system size. The energy of any molecule can be expressed as a weighted sum of the energies of two-electron wave functions that are computable from only a two-electron calculation. Despite the physical elegance of this extended "aufbau" principle, the determination of the distribution of weights─geminal occupations─for general molecular systems has remained elusive. Here we introduce a new paradigm for electronic structure where approximate geminal-occupation distributions are "learned" via a convolutional neural network. We show that the neural network learns the N-representability conditions, constraints on the distribution for it to represent an N-electron system. By training on hydrocarbon isomers with only 2-7 carbon atoms, we are able to predict the energies for isomers of octane as well as hydrocarbons with 8-15 carbons. The present work demonstrates that machine learning can be used to reduce the many-electron problem to an effective two-electron problem, opening new opportunities for accurately predicting electronic structure.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model

Imidacloprid (IMI), as the most widely used neonicotinoid insecticide, poses a serious threat to the water ecosystem due to the inefficient elimination in the traditional water treatment. Chitosan (CTS)-stabilized biochar (BC)-supported Ag nanoparticles (CTS@AgBC) are applied to eliminate the IMI in the water treatment effectively. Batch experiments depict that the modification of BC by CTS and Ag nanoparticles remarkably improve its adsorption performance. The pseudo-second-order and Elovich models have good performance in simulating the adsorption processes of CTS@AgBC and BC. This indicates that the chemical adsorption on real surfaces plays the dominant role in the adsorption of IMI by CTS@AgBC and BC. In addition, the multihead attention (MHA)-based convolutional neural network (CNN) combined with the time-dependent Cox regression model are initially applied to predict and dissect the adsorption elimination processes of IMI by CTS@AgBC. The proposed MHA-CNN model achieves more accurate concentration prediction of IMI than traditional models. According to influence weights by MHA module, biochar category, pH, and treatment temperature are considered the three dominant environmental variables to determine the IMI elimination processes. This study provides insights into roles of environmental variables in the elimination of IMI by CTS@AgBC and the accurate prediction of IMI concentration.

From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems

A general, feedforward neural network strategy for the treatment of a broad range of quantum problems including rotational and vibrational spectroscopy, tunnelling and band structure calculations is presented in this study.

Tandem solar cells efficiency prediction and optimization via deep learning

Optical design plays an important role in improving the performance of opto-electronic devices. However, conventional design processes using finite difference time domain (FDTD) or finite element methods are usually time and computing resource consuming, and often result in sub-optimal solutions due to an incomplete search of the parameter state space. In this paper, we propose a deep learning approach to predict and optimize the cell performance of perovskite/crystalline-silicon (c-Si) tandem solar cells. In particular, a deep neural network is established to predict the achievable short-circuit current for tandem solar cells with a given cell structure. After training on a FDTD numerical simulation data set, the proposed deep neural network achieves an accuracy of 98.3% and micro-second grade simulation time, which is an ultra-fast, highly accurate and computing resource-saving solution to investigate the current properties of tandem solar cells. Heuristic algorithms are further adopted to inversely optimize the device structure, where the optimal set of layer thicknesses is obtained to maximize the achievable short-circuit current. According to the calculated projected efficiency, the expected experimental short-circuit current and power conversion efficiency of tandem solar cells with the optimal selection of layer thickness can reach 15.79 mA cm-2 and 23.24%, which is improved by 14.42% and 28.4%, respectively, compared to the benchmark cells.