Bourns College of Engineering

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary: Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Food packaging (FP) is essential for preserving food quality, safety, and extending shelf-life. However, growing concerns about the environmental and health impacts of conventional packaging materials, particularly per- and polyfluoroalkyl substances (PFAS) and microplastics, are driving a major transformation in FP design. PFAS, synthetic compounds with dual hydro- and lipophobicity, have been widely employed in food packaging materials (FPMs) to impart desirable water and grease repellency. However, PFAS bioaccumulate in the human body and have been linked to multiple health effects, including immune system dysfunction, cancer, and developmental problems. The detection of microplastics in various FPMs has raised significant concerns regarding their potential migration into food and subsequent ingestion. This comprehensive review examines the current landscape of FPMs, their functions, and physicochemical properties to put into perspective why there is widespread use of PFAS and microplastics in FPMs. The review then addresses the challenges posed by PFAS and microplastics, emphasizing the urgent need for sustainable and bio-based alternatives. We highlight promising advancements in sustainable and renewable materials, including plant-derived polysaccharides, proteins, and waxes, as well as recycled and upcycled materials. The integration of these sustainable materials into active packaging systems is also examined, indicating innovations in oxygen scavengers, moisture absorbers, and antimicrobial packaging. The review concludes by identifying key research gaps and future directions, including the need for comprehensive life cycle assessments and strategies to improve scalability and cost-effectiveness. As the FP industry evolves, a holistic approach considering environmental impact, functionality, and consumer acceptance will be crucial in developing truly sustainable packaging solutions.

The recently synthesized monolayer fullerene network in a quasi-hexagonal phase (qHP-C₆₀) exhibits superior electron mobility and optoelectronic properties compared to molecular fullerene (C₆₀), making it highly promising for a variety of applications. However, the microscopic carrier dynamics of qHP-C₆₀ remain unclear, particularly in realistic environments, which are of significant importance for applications in optoelectronic devices. Unfortunately, traditional ab initio methods are prohibitive for capturing the real-time carrier dynamics of such large systems due to their high computational cost. In this work, we present the first real-time electron-nuclear dynamics study of qHP-C₆₀ using velocity-gauge density functional tight binding, which enables us to perform several picoseconds of excited-state electron-nuclear dynamics simulations for nanoscale systems with periodic boundary conditions. When applied to C₆₀, qHP-C₆₀, and their solvated counterparts, we demonstrate that water/moisture significantly increases the electron-hole recombination time in C₆₀ but has little impact on qHP-C₆₀. Our excited-state electron-nuclear dynamics calculations show that qHP-C₆₀ is extremely unique and enable exploration of time-resolved dynamics for understanding excited-state processes of large systems in complex, solvated environments.

Machine-learning models are key to modern biology, yet models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories due to both technical and biological differences. Domain adaptation, a type of transfer learning, alleviates this problem by aligning different datasets so that models can be applied across them. However, most state-of-the-art domain adaptation methods were designed for large-scale data such as images, whereas biological datasets are smaller and have more features, and these are also complex and heterogeneous. This Review discusses domain adaptation methods in the context of such biological data to inform biologists and guide future domain adaptation research. We describe the benefits and challenges of domain adaptation in biological research and critically explore some of its objectives, strengths, and weaknesses. We argue for the incorporation of domain adaptation techniques to the computational biologists toolkit, with further development of customized approaches.

CRISPR-based DNA adenine base editors (ABEs) hold remarkable promises to address human genetic diseases caused by point mutations. ABEs were developed by combining CRISPR-Cas9 with a transfer RNA (tRNA) adenosine deaminase enzyme and through directed evolution, conferring the ability to deaminate DNA. However, the molecular mechanisms driving the efficient DNA deamination in the evolved ABEs remain unresolved. Here, extensive molecular simulations and biochemical experiments reveal the biophysical basis behind the astonishing base editing efficiency of ABE8e, the most efficient ABE to date. We demonstrate that the ABE8es DNA deaminase domain, TadA8e, forms remarkably stable dimers compared to its tRNA-deaminating progenitor and that the strength of TadA dimerization is crucial for DNA deamination. The TadA8e dimer forms robust interactions involving its R98 and R129 residues, the RuvC domain of Cas9 and the DNA. These locking interactions are exclusive to ABE8e, distinguishing it from its predecessor, ABE7.10, and are indispensable to boost DNA deamination. Additionally, we identify three critical residues that drive the evolution of ABE8e toward improved base editing by balancing the enzymes activity and stability, reinforcing the TadA8e dimer and improving the ABE8es functionality. These insights offer new directions to engineer superior ABEs, advancing the design of safer precision genome editing tools.

Thermal treatment of perfluoroalkyl and polyfluoroalkyl substances (PFASs) presents a promising opportunity to halt the PFAS cycle. However, how co-occurring materials such as granular activated carbon (GAC) influence thermal decomposition products of PFASs, and underlying mechanisms remain unclear. We studied the pyrolysis of two potassium salts of perfluoroalkanesulfonates (PFSAs, C_nF_2n+1SO₃K), perfluorobutanesulfonate (PFBS-K), and perfluorooctanesulfonate (PFOS-K), with or without GAC. PFBS-K is more stable than PFOS-K for pure standards, but when it is adsorbed onto GAC, its thermal stabilities and decomposition behaviors are similar. Temperatures and heating rates can significantly influence the decomposition mechanisms and products for pure standards, while these effects are less pronounced when PFSAs are adsorbed onto GAC. We further studied the underlying decomposition mechanisms. Pure standards of C_nF_2n+1SO₃K can decompose directly in their condense phase by reactions: F(CF₂)_nSO₃K → F(CF₂)_n-2CF═CF₂ + KFSO₃ or F(CF₂)_nSO₃K → F(CF₂)_n^- + K⁺ + SO₃. GAC appears to facilitate breakage of the C-S bond to release SO₂ at temperatures as low as 280 °C. GAC promotes fluorine mineralization through functional reactive sites. SiO₂ is particularly important for the surface-mediated mineralization of PFASs into SiF₄. These findings offer valuable insights into optimizing thermal treatment strategies for PFAS-contaminated waste.

Manta rays use wing-like pectoral fins for intriguing oscillatory swimming. It provides rich inspiration for designing potentially fast, efficient, and maneuverable soft swimming robots, which, however, have yet to be realized. It remains a grand challenge to combine fast speed, high efficiency, and high maneuverability in a single soft swimmer while using simple actuation and control. Here, we report leveraging spontaneous snapping stroke in the monostable flapping wing of a manta-like soft swimmer to address the challenge. The monostable wing is pneumatically actuated to instantaneously snap through to stroke down, and upon deflation, it will spontaneously stroke up by snapping back to its initial state, driven by elastic restoring force, without consuming additional energy. This largely simplifies designs, actuation, and control for achieving a record-high speed of 6.8 body length per second, high energy efficiency, and high maneuverability and collision resilience in navigating through underwater unstructured environments with obstacles by simply tuning single-input actuation frequencies.

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.