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 

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]

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

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]  

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]

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.

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.

"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]  

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.

Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of isolated leukocytes acquired from a custom-built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, we not only achieved high accuracy in differentiating B and T lymphocytes, but also classified CD4 and CD8 cells, therefore outperforming the classification accuracy of most current hematology analyzers. We validated the performance of AIRFIHA in a randomly selected test set and cross-validated it across all blood donors. Owing to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.  Corresponding author(s) Email:    [email protected],  [email protected]

Supporting InformationYee-Fun Lim,* Chee Koon Ng, US Vaitesswar, Kedar Hippalgaonkar*

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] 

Table S1.  Performance of soft pneumatic actuators

IntroductionReal world Time on Treatment (rwToT), also known as real world time to treatment discontinuation (rwTTD), is defined as the length of time observed in real world data (as distinct from controlled clinical trials) from initiation of a medication to discontinuation of that medication1,2. The ending of the treatment can be caused by adverse events, deaths, switches of treatment and loss of follow up. Because time to treatment discontinuation can be readily obtained from electronic medical records, this effectiveness endpoint is convenient to evaluate the efficacy of a drug that is already approved for public use3. It is often used as a surrogate effectiveness endpoint, showing high correlation to progression-free survival and moderate-to-high correlation to overall survival4,5. As rwTTD is an important metric for drug effectiveness, it is routinely reported during the post-clinical trial phase2,4,6–9.Calculation of rwTTD in patient population is often equivalent to constructing a (Kaplan-Meier) KM curve, with each point representing the proportion of patients that are still on treatment at a specific time point 1. Either the entire curve, or mean rwTTD, restricted mean10, or the time point at which a specific portion of the patients (e.g. , 50%) dropping treatment is of interest. Currently, there is no existing machine learning scheme established to predict such a curve, or the midpoint, as the vast majority of the machine learning models have been focused on predicting individuals’ behavior rather than population-level behavior. Such a machine learning scheme, if established, has many meaningful clinical applications. For instance, given observed clinical parameters and outcomes in clinical trials, how do we derive expected time-to-treatment in the real-world? Given the rwTTD for a drug on one patient population, how can we predict the rwTTD when applying this drug to another population (e.g. , for a different disease)?This study establishes a machine learning framework to infer population-wise rwTTD. We showed that population-wise curve prediction differs substantially from aggregating all individuals’ results. Our framework models the population-wise curve and is generic to diverse base-learners for predicting rwTTD. We demonstrated the effectiveness of this framework based on both simulated data and real world Electronic medical records (EMR) data for pembrolizumab-treated cancer populations7,11,12. The study opens a new direction of modeling population-level rwTTD, which has great values for directing post-clinical stage drug administrations. This machine learning scheme will also have meaningful implications to population-based predictions for other problems, as machine learning algorithms have so far been focused on predictions for individual samples.

The theoretical capability of modular robot to organize the overall robot into different structures with different functions has broad prospects in space exploration. Therefore, we develop a novel modular space robot named Space Module, and inspired by biological cooperative and mutual assistance behaviors, a novel self-assembly method is proposed for it.To solve the mobility problem of non-mobile modules, a new meta-modules design for Space Module is presented, based on which the concept of mutual assistance is utilized to achieve position and posture reachability of assembled unit while minimizing the effect of meta-modules on granularity. Then, an assembly planner is designed to obtain the assembly sequences according to the unique motion characteristics of meta-module and mutual assistance to realize the self-manufacturing of desired configurations. Finally, several demonstrations are given to verify the validity and feasibility of the proposed assembly method.Corresponding author(s) Email:   [email protected]

This Supporting information includes interactive plots, videos, and data captured while performing evaluation and validation experiments for our paper.