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Computing landscape is evolving rapidly. Exascale computers have arrived, which can perform 10^18 mathematical operations per second. At the same time, quantum supremacy has been demonstrated, where quantum computers have outperformed these fastest supercomputers for certain problems. Meanwhile, artificial intelligence (AI) is transforming every aspect of science and engineering. A highly anticipated application of the emerging nexus of exascale computing, quantum computing and AI is computational design of new materials with desired functionalities, which has been the elusive goal of the federal materials genome initiative. The rapid change in computing landscape resulting from these developments has not been matched by pedagogical developments needed to train the next generation of materials engineering cyberworkforce. This gap in curricula across colleges and universities offers a unique opportunity to create educational tools, enabling a decentralized training of cyberworkforce. To achieve this, we have developed training modules for a new generation of quantum materials simulator, named AIQ-XMaS (AI and quantum-computing enabled exascale materials simulator), which integrates exascalable quantum, reactive and neural-network molecular dynamics simulations with unique AI and quantum-computing capabilities to study a wide range of materials and devices of high societal impact such as optoelectronics and health. As a singleentry access point to these training modules, we have also built a CyberMAGICS (cyber training on materials genome innovation for computational software) portal, which includes step-by-step instructions in Jupyter notebooks and associated tutorials, while providing online cloud service for those who do not have access to adequate computing platform. The modules are incorporated into our open-source AIQ-XMaS software suite as tutorial examples and are piloted in classroom and workshop settings to directly train many users at the University of Southern California (USC) and Howard University—one of the largest historically black colleges and universities (HBCUs), with a strong focus on underrepresented groups. In this paper, we summarize these educational developments, including findings from the first CyberMAGICS Workshop for Underrepresented Groups, along with an introduction to the AIQ-XMaS software suite. Our training modules also include a new generation of open programming languages for exascale computing (e.g., OpenMP target) and quantum computing (e.g., Qiskit) used in our scalable simulation and AI engines that underlie AIQ-XMaS. Our training modules essentially support unique dual-degree opportunities at USC in the emerging exa-quantum-AI era: Ph.D. in science or engineering, concurrently with MS in computer science specialized in high-performance computing and simulations, MS in quantum information science or MS in materials engineering with machine learning. The developed modular cyber-training pedagogy is applicable to broad engineering education at large. 

Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag₂S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag₂S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag₂S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

 

Optoelectronic properties of devices made of two-dimensional materials depend largely on the dielectric constant and thickness of a substrate. To systematically investigate the thickness dependence of dielectric constant from first principles, we have implemented a double-cell method based on a theoretical framework by Martyna and Tuckerman [J. Chem. Phys. 110, 2810 (1999)] and therewith developed a general and robust procedure to calculate dielectric constants of slab systems from electric displacement and electric field, which is free from material-specific adjustable parameters. We have applied the procedure to a prototypical substrate, Al 2 O 3 , thereby computing high-frequency and static dielectric constants of a finite slab as a function of the number of crystalline unit-cell layers. We find that two and four layers are sufficient for the high-frequency and static dielectric constants of (0001) Al 2 O 3 slabs to recover 90% of the respective bulk values computed by a Berry-phase method. This method allows one to estimate the thickness dependence of dielectric constants for various materials used in emerging two-dimensional nanophotonics, while providing an analytic formula that can be incorporated into photonics simulations. 

Mechanical behavior of 2D materials such as MoS₂can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS₂kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS₂kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.