arXiv subjects

Erik W. Draeger

Publications and source records attributed to Erik W. Draeger.

4 recordsLinked to original sources

arXiv2025

A Unifying Framework to Enable Artificial Intelligence in High Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly-coupled HPC/AI hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/ML can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. The current ecosystem and sparsely-connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

cs.DC↗

arXiv2020

Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral

With the current rate of progress in quantum computing technologies, systems with more than 50 qubits will soon become reality. Computing ideal quantum state amplitudes for circuits of such and larger sizes is a fundamental step to assess both the correctness, performance, and scaling behavior of quantum algorithms and the fidelities of quantum devices. However, resource requirements for such calculations on classical computers grow exponentially. We show that deferring tensor contractions can extend the boundaries of what can be computed on classical systems. To demonstrate this technique, we present results obtained from a calculation of the complete set of output amplitudes of a universal random circuit with depth 27 in a 2D lattice of $7 \times 7$ qubits, and an arbitrarily selected slice of $2^{37}$ amplitudes of a universal random circuit with depth 23 in a 2D lattice of $8 \times 7$ qubits. Combining our methodology with other decomposition approaches found in the literature, we show that we can simulate $7 \times 7$-qubit random circuits to arbitrary depth by leveraging secondary storage. These calculations were thought to be impossible due to resource requirements.

quant-ph↗

arXiv2015

Lithium Ion Solvation and Diffusion in Bulk Organic Electrolytes from First Principles and Classical Reactive Molecular Dynamics

Lithium-ion battery performance is strongly influenced by the ionic conductivity of the electrolyte, which depends on the speed at which Li ions migrate across the cell and relates to their solvation structure. The choice of solvent can greatly impact both solvation and diffusivity of Li ions. We use first principles molecular dynamics to examine the solvation and diffusion of Li ions in the bulk organic solvents ethylene carbonate (EC), ethyl methyl carbonate (EMC), and a mixture of EC/EMC. We find that Li ions are solvated by either carbonyl or ether oxygen atoms of the solvents and sometimes by the PF$_6^-$ anion. Li$^+$ prefers a tetrahedrally-coordinated first solvation shell regardless of which species are involved, with the specific preferred solvation structure dependent on the organic solvent. In addition, we calculate Li diffusion coefficients in each electrolyte, finding slightly larger diffusivities in the linear carbonate EMC compared to the cyclic carbonate EC. The magnitude of the diffusion coefficient correlates with the strength of Li$^+$ solvation. Corresponding analysis for the PF$_6^-$ anion shows greater diffusivity associated with a weakly-bound, poorly defined first solvation shell. These results may be used to aid in the design of new electrolytes to improve Li-ion battery performance.

cond-mat.mtrl-sci↗

arXiv2011

Path Integral Calculations of the Hydrogen Hugoniot Using Augmented Nodes

We calculate the hydrogen Hugoniot using ab initio path integral Monte Carlo. We introduce an efficient finite-temperature fixed-node approximation for handling fermions, which includes an optimized mixture of free particle states and atomic orbitals. The calculated Hugoniot confirms previous fixed-node path integral calculations at temperatures around T=30,000 K and above, while approaching smoothly the low temperature gas gun results. The ability to optimize the free energy within the path integral opens many new possibilities for developing nodal density matrices for path integral simulations of other chemical systems.

cond-mat.mtrl-sci↗