The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.Keywords --- Computational catalysis, density functional theory (DFT), adsorption energies, nudged elastic band calculations, binding energies, barrier heights 

A document by Yi Jin. Click on the document to view its contents.

Engineering microscopic collectives of cells or microrobots is challenging due to the often-limited capabilities of the individual agents, our inability to reliably program their motion and local interactions, and difficulties visualising their behaviours. Here, we present a low-cost, modular and open-source Dynamic Optical MicroEnvironment (DOME) and demonstrate its ability to augment microagent capabilities and control collective behaviours using light. The DOME offers an accessible means to study complex multicellular phenomena and implement de-novo microswarms with desired functionalities. Corresponding author(s) Email:    [email protected] [email protected]

Memristive devices being applied in neuromorphic computing are envisioned to significantly improve the power consumption and speed of future computing platforms. The materials used to fabricate such devices will play a significant role in their viability. Graphene is a promising material, with superb electrical properties and the ability to be produced sustainably. In this paper, we demonstrate that a fabricated graphene-pentacene memristive device can be used as synapses within Spiking Neural Networks (SNNs) to realise Spike Timing Dependent Plasticity (STDP) for unsupervised learning in an efficient manner. Specifically, we verify operation of two SNN architectures tasked for single digit (0-9) classification: (i) a simple single-layer network, where inputs are presented in 5x5 pixel resolution, and (ii) a larger network capable of classifying the Modified National Institute of Standards and Technology (MNIST) dataset, where inputs are presented in 28x28 pixel resolution. Final results demonstrate that for 100 output neurons, after one training epoch, a test set accuracy of up to 86% can be achieved, which is higher than prior art using the same number of output neurons. We attribute this performance improvement to homeostatic plasticity dynamics that we used to alter the threshold of neurons during training. Our work presents the first investigation of the use of green-fabricated graphene memristive devices to perform a complex pattern classification task. This can pave the way for future research in using graphene devices with memristive capabilities in neuromorphic computing architectures. In favour of reproducible research, we make our code and data publicly available https://anonymous.4open.science/r/c69ab2e2-b672-4ebd-b266-987ee1fd65e7.

AbstractIn recent years, there has been a growing interest in the development of universal soft grippers that can handle objects of varying form factors (including flat objects), surface condition (including moistened or oily objects), and mechanical properties (deformable and fragile). Yet, there is no single gripper that can gently grip objects with such a wide range of properties. In this paper, we present a soft gripper that combines granular jamming (GJ) and electroadhesion (EA) to gently grasp and release a large set of diverse objects. The gripper can operate in GJ mode only, in EA mode only, or in a combination mode that simultaneously activates GJ and EA. In GJ mode, the gripper can grasp objects with different surface properties, lift objects 38 times its own weight using negative pressure, and release objects by applying positive pressure, but has difficulty in handling flat and fragile objects. In EA mode, the gripper can manipulate flat and fragile objects but encounters difficulties with different surface properties such as oily or moistened. In the combination mode, the gripper can generates grasping forces up to 35% higher than in the GJ mode for all object sizes and certain shapes such as a cylinder.IntroductionThe softness of the human hand is a critical factor that allows us to hold, lift, and manipulate a variety of objects and has inspired roboticists to incorporate softness in gripper design and materials. The compliance of soft materials enables passive adaptation of the gripper during grasping operations allowing manipulation of a wide range of objects without bringing additional control complexities.[1,2] In recent years, there has been a growing interest in the development of universal soft grippers that can work with objects of different form factors, rigidity, surface properties, and level of fragility.[3–6] A possible approach to create such highly versatile grippers is to combine different gripping technologies that complement their individual limitations.[1,3,7–10] Yet, it is still challenging to develop a single gripper that can grasp and release objects of different form factor including flat objects, surface conditions (wet, porous, oily, and powdered), and mechanical properties (fragile and deformable).In this paper, we present a soft gripper capable of manipulating different objects with varying physical properties, such as shape, surface conditions, and rigidity. The proposed gripper combines two different technologies: granular jamming to control stiffness and electroadhesion to control adhesion. Here we show that not only does this combination mutually compensate for the limitations of each individual technology, but it also makes the gripper capable of performing multi-stage grasping tasks that consist of diverse grasping and releasing operations on objects made of different material, surface, and shape. The manipulation of a book is an example of multi-stage operation that requires grasping and turning a rigid cover and flipping through single pages.Granular jamming (GJ) enables reversible stiffness change between soft and rigid configurations by means of negative pressure[5,11,12]. High compliance in the soft state allows a GJ gripper to envelope the manipulated object by pressing on it. When negative pressure is applied, the gripper becomes stiff and holds the encaged object.[2] Variable stiffness can also be achieved by integrating phase-change materials that vary mechanical properties under thermal stimulation.[7,13] However, granular jamming offers comparatively faster response time (~100ms), independence from environmental temperature, higher lifting force, easier fabrication, higher robustness, and lower cost.[2,5,14,15] The grasping force produced by granular jamming is sufficient to grasp objects of different morphologies, almost independently of the surface conditions of the object.[5,11,16] The grasping force of GJ grippers can vary from 0.09 to 1.2 kN.[12] GJ has been combined with soft pneumatic actuators to provide more dexterous grasp and lift heavier objects because of the enhanced holding forces.[10,17] However, GJ grippers cannot lift flat objects, such as a sheet of paper. Also, the grasping performance of delicate, fragile and easily deformable objects such as a thin layer of cloth, an egg, or water balloons, as well as larger objects than the active area of the granular bag can be challenging and have not been demonstrated so far.Electroadhesion (EA) instead is an adhesive technology that leverages the shear force generated by electrostatic forces.[18] Electroadhesive pads have been combined with different actuation technologies, such as dielectric elastomer actuation,[1] soft pneumatic actuation,[19] layer jamming,[20] and Fin-Ray structured actuation[21]. The enhanced shear force makes EA-based grippers capable of delicately grasping both flat and fragile objects without squeezing or breaking them.[1,22–26] While the adhesive force of EA pads can be tuned by regulating electrical input, EA effectiveness is highly dependent on the environmental and surface conditions of the object being grasped.[18] In particular electroadhesion is less effective for objects that are greasy, rough, or wet.[2] An additional challenge of soft grippers that rely on electroadhesion is the residual electrostatic charge that remains for a few seconds after removing the voltage and can result in difficult release of light objects.[27]

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email:   [email protected]  

Soft actuators and robotic devices have been increasingly applied to the field of rehabilitation and assistance, where safe human and machine interaction is of particular importance. Compared with their widely used rigid counterparts, soft actuators and robotic devices can provide a range of significant advantages; these include safe interaction, a range of complex motions, ease of fabrication and resilience to a variety of environments. In recent decades, significant effort has been invested in the development of soft rehabilitation and assistive devices for improving a range of medical treatments and quality of life. This review provides an overview of the current state-of-the-art in soft actuators and robotic devices for rehabilitation and assistance, in particular systems that achieve actuation by pneumatic and hydraulic fluid-power, electrical motors, chemical reactions and soft active materials such as dielectric elastomers, shape memory alloys, magnetoactive elastomers, liquid crystal elastomers and piezoelectric materials. Current research on soft rehabilitation and assistive devices is in its infancy, and new device designs and control strategies for improved performance and safe human-machine interaction are identified as particularly untapped areas of research. Finally, insights into future research directions are outlined.Corresponding author(s) Email:   [email protected], [email protected]

With advancements in automation and high-throughput techniques, complex materials discovery with multiple conflicting objectives can now be tackled in experimental labs. Given that physical experimentation is greatly limited by evaluation budget, maximizing efficiency of optimization becomes crucial. We discuss the limitations of using hypervolume as a performance indicator for desired optimality across the entire multi-objective optimization run and propose new metrics specific to experimentation: ability to perform well for complex high-dimensional problems, minimizing wastage of evaluations, consistency/robustness of optimization, and ability to scale well to high throughputs. With these metrics, we perform a comparison of two conceptually different and state-of-the-art algorithms (Bayesian and Evolutionary) on synthetic and real-world datasets. We discuss the merits of both approaches with respect to exploration and exploitation, where fully resolving the Pareto Front could be the main aim for greater scientific value in understanding materials space, and thus provide a perspective for materials scientists to implement optimization in their platforms.

Excavation of regolith is the enabling process for many of the in-situ resource utilization (ISRU) efforts that are being considered to aid in the human exploration of the moon and Mars. Most proposed planetary excavation systems are integrated with a wheeled vehicle, but none yet have used a screw-propelled vehicle which can significantly enhance the excavation performance. Therefore, CASPER, a novel screw-propelled excavation rover is developed and analyzed to determine its effectiveness as a planetary excavator. The excavation rate, power, velocity, cost of transport, and a new parameter, excavation transport rate, are analyzed for various configurations of the vehicle through mobility and excavation tests performed in silica sand. The optimal configuration yielded a 30 kg/hr excavation rate and 10.2 m/min traverse rate with an overall system mass of 3.4 kg and power draw of less than 30 W. These results indicate that this architecture shows promise as a planetary excavation because it provides significant excavation capability with low mass and power requirements. Corresponding author(s) Email:   [email protected]  

The substantial increase in global population and climate change, among other factors have led to global food security and supply chain challenges. The United Nations has laid out an agenda to sustainably achieve zero hunger by 2030 as one of its sustainable development goals. However, sustainably achieving improved food yield has become a challenge as excessive use of fertilizers has also led to adverse environmental impact. To address the aforementioned challenges, WisDM Green, an artificial intelligence (AI)-based platform that aims to pinpoint and prioritize compound (e.g. biostimulants) combinations in peat moss, is harnessed to sustainably improve the yield of Amaranthus cruentus (red spinach). In this proof-of-concept study, from a pool of 8 compounds, WisDM Green-pinpointed combinations (6-Benzylaminopurine/Ethylenediaminetetraacetic Acid Iron (III) and Humic Acid/Seaweed Extract) achieve 26.34±15.80 and 33.59±14.60 increase in %Yield, respectively. The study also indicates that compound combinations may exhibit concentration-dependent synergies and thus, properly adjusting the concentration ratios of combinations may further improve plant yield in the context of sustainable farming. P. Wang and K. You contributed to this work equally.Corresponding author(s) Email:   [email protected], [email protected], [email protected] 

"AI & Drug Discovery" mode has significantly promoted drug development and achieved excellent performance, especially with the rapid development of deep learning, making remarkable contributions to protecting human physiological health. However, due to the "black-box" characteristic of the deep learning model, the decision route and predicted results in different research stages assisted by deep models are usually unexplainable, limiting their application in practice and more in-depth research of drug discovery. Focusing on the drug molecules, we propose an explainable fragment-based molecular property attribution technique for analyzing the influence of particular molecule fragments on properties and the relationship between the molecular properties in this paper. Quantitative experiments on 42 benchmark property tasks demonstrate that 325 attribution fragments, which account for 90% of the overall attribution results obtained by the proposed method, have positive relevance to the corresponding property tasks. More impressively, most of the attribution results randomly selected are consistent with the existing mechanism explanations. The discovery mentioned above provides a reference standard for assisting researchers in developing more specific and practical drug molecule studies, such as synthesizing molecular with the targeted property using a fragment obtained from the attribution method.Corresponding author(s) Email:   [email protected]  

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the world wide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open source framework that offers an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state-of-the-art computational chemistry codes including NWChem and Psi4 and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.

Scene flow tracks the three-dimensional (3D) motion of each point in adjacent point clouds. It provides fundamental 3D motion perception for autonomous driving and server robot. Although the Red Green Blue Depth (RGBD) camera or Light Detection and Ranging (LiDAR) capture discrete 3D points in space, the objects and motions usually are continuous in the macro world. That is, the objects keep themselves consistent as they flow from the current frame to the next frame. Based on this insight, the Generative Adversarial Networks (GAN) is utilized to self-learn 3D scene flow with no need for ground truth. The fake point cloud of the second frame is synthesized from the predicted scene flow and the point cloud of the first frame. The adversarial training of the generator and discriminator is realized through synthesizing indistinguishable fake point cloud and discriminating the real point cloud and the synthesized fake point cloud. The experiments on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) scene flow dataset show that our method realizes promising results without ground truth. Just as human, the proposed method can identify the similar local structures of two adjacent frames even without knowing the ground truth scene flow. Then, the local correspondence can be correctly estimated, and further the scene flow is correctly estimated.   Corresponding author(s) Email: [email protected]

The explosive growth of data and information has motivated technological developments in computing systems that utilize them for efficiently discovering patterns and gaining relevant insights. Inspired by the structure and functions of biological synapses and neurons in the brain, neural network algorithms that can realize highly parallel computations have been implemented on conventional silicon transistor-based hardware. However, synapses composed of multiple transistors allow only binary information to be stored, and processing such digital states through complicated silicon neuron circuits makes low-power and low-latency computing difficult. Therefore, the attractiveness of the emerging memories and switches for synaptic and neuronal elements, respectively, in implementing neuromorphic systems, which are suitable for performing energy-efficient cognitive functions and recognition, is discussed herein. Based on a literature survey, recent progress concerning memories shows that novel strategies related to materials and device engineering to mitigate challenges are presented to primarily achieve nonvolatile analog synaptic characteristics. Attempts to emulate the role of the neuron in various ways using compact switches and volatile memories are also discussed. It is hoped that this review will help direct future interdisciplinary research on device, circuit, and architecture levels of neuromorphic systems. Corresponding author(s) Email:   [email protected]  

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment, for which many reports conclude the advantageous use of convolutional neural networks (CNNs) in prostate lesion detection and classification (PLDC). However, the network training inevitably involves prostate magnetic resonance (MR) images from multiple sites/cohorts. There always exists variation in scanning protocol among the cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Recent domain adaptation (DA) models can alleviate the inherent cross-site heterogeneity. Some could leverage cross-domain knowledge transfer using whole-slide images, without prior knowledge of lesion regions. In this paper, we propose a coarse mask-guided deep domain adaptation network (CMD²A-Net) in order to develop a fully automated framework for PLDC using multi-cohort images. No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion-related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. As a result, the features of both prostate lesion and region can be fused to align the robust features between the source and target domains. Experiments have been performed on 512 mpMRI sets from datasets of PROSTATEx (with 330 sets) and two cohorts, A (with 74 sets) and B (with 108 sets). Using the ensemble learning, our CMD²A-Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large-scale public dataset PROSTATEx to our small-scale target domains. Our results and ablation study also support the CMD²A-Net’s effectiveness in lesion classification between benign or malignant, compared to the state-of-the-art models Corresponding author(s) Email:   [email protected] (K.W. Kwok), [email protected] (V. Vardhanabhuti)

Conventional vision systems suffer from lots of data handling between memory and processing units. Inspired by how humans recognize noisy images and the flexible modulation on the timescale of ion dynamics inside an emerging memtransistor, we report a novel neuromorphic vision system based on the ion-modulated memtransistors. By controlling the ion doping processes under adequate stimuli strengths, both short-term and long-term ion dynamics can be utilized to deliver energy-efficient data processing. When dealing with image reconstructions, the short-term accumulation effect of the device can help filter noises in a set of received noisy images while enhancing the original pattern information. The increased contrast can help distinguish the actual contents. To demonstrate systematic performances with the reconfiguration of devices, we extract the nonlinear relationship between channel conductance variation and the amplitude of gate pulses into the network-level simulation. Also, with the nonvolatile conductance change characteristic, the task of recognizing noisy images is performed to verify the versatility of ion-modulated memtransistors in the neuromorphic artificial vision systems.

A document by Helen Payne. Click on the document to view its contents.

A prominent problem in computer vision is occlusion, which occurs when an object’s key features temporarily disappear behind another crossing body, causing the computer to struggle with image detection. While the human brain is capable of compensating for the invisible parts of the blocked object, computers lack such scene interpretation skills. Cloud computing using convolutional neural networks is typically the method of choice for handling such a scenario. However, for mobile applications where energy consumption and computational costs are critical, cloud computing should be minimized. In this regard, we propose a computer vision sensor capable of efficiently detecting and tracking covered objects without heavy reliance on occlusion handling software. Our edge-computing sensor accomplishes this task by self-learning the object prior to the moment of occlusion and uses this information to “reconstruct” the blocked invisible features. Furthermore, the sensor is capable of tracking a moving object by predicting the path it will most likely take while travelling out of sight behind an obstructing body. Finally, sensor operation is demonstrated by exposing the device to various simulated occlusion events. Keywords:  Computer vision, occlusion handling, edge computing, object tracking, dye sensitized solar cell. Corresponding author Email: [email protected]