Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (~90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (~80 femtojoules per write) operation combined with excellent endurance (>109 write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs.

The optimal design of compounds through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance. This paper highlights key trends, challenges, and opportunities underpinning the Computer-Aided Molecular Design (CAMD) problems. A brief review of knowledge-driven property estimation methods and solution techniques, as well as corresponding CAMD tools and applications, are first presented. In view of the computational challenges plaguing knowledge-based methods and techniques, we survey the current state-of-the-art applications of deep learning to molecular design as a fertile approach towards overcoming computational limitations and navigating uncharted territories of the chemical space. The main focus of the survey is given to deep generative modeling of molecules under various deep learning architectures and different molecular representations. Further, the importance of benchmarking and empirical rigor in building deep learning models is spotlighted. The review article also presents a detailed discussion of the current perspectives and challenges of knowledge-based and data-driven CAMD and identifies key areas for future research directions. Special emphasis is on the fertile avenue of hybrid modeling paradigm, in which deep learning approaches are exploited while leveraging the accumulated wealth of knowledge-driven CAMD methods and tools.

Over the past 30 years, the use of glass and carbon-fiber reinforced composites in aerospace and other high-performance applications has soared along with the broad industrial adoption of composite materials. Key to the strength and versatility of these hybrid, layered materials in high-performance applications is the orientation of fibers in each layer. Recent innovations in additive manufacturing (3-D printing) have made it possible to finetune this factor, thanks to the ability to include within the CAD file discrete printer-head orientation instructions for each layer of the component being printed, thereby optimizing strength, flexibility, and durability for specific uses of the part. These 3-D-printing toolpaths (a series of coordinated locations a tool will follow) in CAD file instructions are therefore a valuable trade secret for the manufacturers. However, a team of researchers from NYU Tandon School of Engineering led by Nikhil Gupta, a professor in the Department of Mechanical and Aerospace Engineering showed that these toolpaths are also easy to reproduce--and therefore steal--with machine learning (ML) tools applied to the microstructures of the part obtained by a CT scan.

The importance of solvation or hydration mechanism and accompanying free energy change has made various in silico calculation methods for the solvation energy one of the most important application in computational chemistry[1-25]. The solvation free energy directly influences many chemical properties in solvated phases and plays a dominant role in various chemical reactions: drug delivery[2, 16, 18, 26], organic synthesis[27], electrochemical redox reactions[28-31], etc. The atomistic computer simulation approaches for the solvent and the solute molecules directly offer the microscopic structure of the solvation shell, which surrounds the solutes molecule[7, 8, 13, 17, 18, 32]. The solvation shell structure could provide us detailed physicochemical information like microscopic mechanisms on solvation or the interplay between the solvent and the solute molecules when we use an appropriate force field and molecular dynamics parameters. However, those explicit solvation methods we stated above need an extensive amount of numerical calculations since we have to simulate each individual molecule in the solvated system. The practical problems on the explicit solvation model restrict its applications to classical molecular mechanics simulations[7, 8, 17] or a limited number of QM/MM approaches[13, 32]. For classical mechanics approaches for macromolecules or calculations for small compounds at quantum-mechanical level, the idea of implicit solvation enables us to calculate solvation energy with feasible time and computational costs when one considers a given solvent as a continuous and isotropic medium in the Poisson-Boltzmann equation[1, 3-6, 9, 11, 15, 23, 24]. Many theoretical advances have been introduced to construct the continuum solvation model, which involves parameterized solvent properties: the polarizable continuum model (PCM)[9], the conductor-like screening model (COSMO)[1] and its variations[6, 33], generalized Born approximations like solvation model based on density (SMD)[5] or solvation model 6, 8, 12, etc. (SMx)[4, 11]. The structure-property relationship (SPR) is rather a new approach, which predicts the solvation free energy with a completely different point of view when compared to computer simulation approaches with precisely defined theoretical backgrounds[34, 35].

Technology is supposed to have a positive effect on humanity. That was the initial vision, correct? But for some reason this artificial intelligence hype has become a controversy and the new space race all in one. On one hand, Elon Musk, CEO of Tesla, says he's taking a cautious approach to the emerging technology. Musk says it's the most serious threat to the survival of the human race [1].

Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environments. The data stream mining community still faces some primary challenges and difficulties related to the comparison and evaluation of new proposals, mainly due to the lack of publicly available non-stationary real-world datasets. The comparison of stream algorithms proposed in the literature is not an easy task, as authors do not always follow the same recommendations, experimental evaluation procedures, datasets, and assumptions. In this paper, we mitigate problems related to the choice of datasets in the experimental evaluation of stream classifiers and drift detectors. To that end, we propose a new public data repository for benchmarking stream algorithms with real-world data. This repository contains the most popular datasets from literature and new datasets related to a highly relevant public health problem that involves the recognition of disease vector insects using optical sensors. The main advantage of these new datasets is the prior knowledge of their characteristics and patterns of changes to evaluate new adaptive algorithm proposals adequately. We also present an in-depth discussion about the characteristics, reasons, and issues that lead to different types of changes in data distribution, as well as a critical review of common problems concerning the current benchmark datasets available in the literature.

Learning-to-learn (using optimization algorithms to learn a new optimizer) has successfully trained efficient optimizers in practice. This approach relies on meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates. However, there were few theoretical guarantees on how to avoid meta-gradient explosion/vanishing problems, or how to train an optimizer with good generalization performance. In this paper, we study the learning-to-learn approach on a simple problem of tuning the step size for quadratic loss. Our results show that although there is a way to design the meta-objective so that the meta-gradient remain polynomially bounded, computing the meta-gradient directly using backpropagation leads to numerical issues that look similar to gradient explosion/vanishing problems. We also characterize when it is necessary to compute the meta-objective on a separate validation set instead of the original training set. Finally, we verify our results empirically and show that a similar phenomenon appears even for more complicated learned optimizers parametrized by neural networks.

Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the attention of deep learning and machine vision researchers. However, to create production-ready systems, there is still a long path to cover. Real-world problems such as varying illuminations and reaching acceptable accuracies need to be addressed in order to create robust systems. In this paper, we have addressed these issues and reached a state of the art performance, i.e., 97.35% accuracy rate for this task. Also, a new dataset containing 1231 images of 11 classes taken from several construction sites is gathered and publicly published to help other researchers in this field.

The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential.

The space of synthesizable molecules is greater than $10^{60}$, meaning only a vanishingly small fraction of these molecules have ever been realized in the lab. In order to prioritize which regions of this space to explore next, synthetic chemists need access to accurate molecular property predictions. While great advances in molecular machine learning have been made, there is a dearth of benchmarks featuring properties that are useful for the synthetic chemist. Focussing directly on the needs of the synthetic chemist, we introduce the Photoswitch Dataset, a new benchmark for molecular machine learning where improvements in model performance can be immediately observed in the throughput of promising molecules synthesized in the lab. Photoswitches are a versatile class of molecule for medical and renewable energy applications where a molecule's efficacy is governed by its electronic transition wavelengths. We demonstrate superior performance in predicting these wavelengths compared to both time-dependent density functional theory (TD-DFT), the incumbent first principles quantum mechanical approach, as well as a panel of human experts. Our baseline models are currently being deployed in the lab as part of the decision process for candidate synthesis. It is our hope that this benchmark can drive real discoveries in photoswitch chemistry and that future benchmarks can be introduced to pivot learning algorithm development to benefit more expansive areas of synthetic chemistry.