Computational Chemistry Commons^™

Electronic Structure Discretization And Compression Using Diagonal Basis Sets, Casey Lee Dowdle

Dartmouth College Ph.D Dissertations

Numerically solving the electronic structure problem is a fundamentally difficult problem due to the exponential growth in the dimension of the Hilbert space as the system size increases. In order to solve problems at a chemically relevant accuracy, both the choice of basis set and numerical method are important factors that are intrinsically connected.

In this thesis, we study the discretization and resulting compression of electronic Hamiltonians using diagonal basis sets. A diagonal basis set approximately diagonalizes the matrix and tensor representations of the one- and two-body potentials. This can reduce storage, simplify matrix-vector products, and lower the complexity of …

Probing Feni Alloys With Atomic Precision, Scipio Han, Kaushik Rajkumar Jayaprabha, Xin Qi

Wetterhahn Science Symposium Posters

We compared EAM and MEAM atomic potentials for modeling FeNi (tetrataenite), a rare 50/50 alloy found in meteorites. Picking 4 potentials, we calculated minimum lattice constants and order-disorder transition temperatures. MEAM potentials accurately predicted the experimental transition temperature, confirming that MEAM better captures FeNi's directional bonding.

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

Optimizing Mn-Al Permanent Magnet Performance Through Control Of The Phase Transformation, Ternary Element Addition, And Advanced Processing, Thomas R. Keller

Dartmouth College Ph.D Dissertations

The growing need for electrical power in machines and vehicles brings with it a growing need for critical materials. The current high-performance permanent magnets (PMs) based on rare-earth (RE) elements Nd and Sm cannot escape the problem of raw material cost, geographic scarcity in the earth’s crust, and lack of circular global supply chains. This poses a problem of industrial ecology: can PMs be made from inexpensive, more abundant materials while still meeting sufficient performance criteria? PMs based on the magnetic τ phase of Mn-Al offer a possible alternative to REPMs. However, years of innovation have not yet achieved real-world …

Machine Learning For Electronic And Atomistic Simulations, Jun Yang

Dartmouth College Ph.D Dissertations

The demand for accurate and efficient atomistic simulations and electronic structure calculations in materials science and quantum chemistry has motivated the development of novel computational methodologies. The rapid evolution of machine learning has brought new techniques for advancing the accuracy, efficiency, and predictive power of atomistic simulations and electronic structure calculations.

In this thesis, we explore the symmetry requirements and physics intuitions needed for developing machine-learning interatomic potentials, which are the most critical component in atomistic simulations. Specifically, we introduce a novel physics-inspired graph neural network interatomic potential that enables accurate and efficient atomistic simulations of complex materials. The machine …