Molecular Optimization for Classical and Quantum Condensed Phase Systems

Condensed phase phenomena remain a theoretical challenge to thoroughly understand and elucidate due to the close interactions among large number of microscopic degrees of freedom. Such deviation from the non-interacting ideality necessitates an effective resolution of the constrained fluctuations and strong correlations in condensed phase systems, which can be methodically achieved using non-Euclidean optimization tools. This thesis is devoted to the optimization-based development of molecular simulations that facilitate our understanding of the static and dynamical properties of many-body systems. Chapter 1 introduces the background on simulating condensed phase systems and sets up the overall scope of the thesis. Chapter 2 provides a primary exposure to a few fundamental connections between functional minimization on manifolds and essential properties, for example statistical and spectral, of many-body systems. Chapter 3 considers methods adept at treating representative classical condensed-phase systems. We start with phenomenological spin models on a lattice and turn our attention to atomistic interfaces including aqueous electrolyte-electrode and polymer-protein composite. We discuss proficient schemes to implement and process our molecular simulations, allowing us to elucidate (a)typical structural-dynamical fluctuations to heterogeneities native to these classical systems. Chapter 4 considers methods capable of studying correlated quantum condensed-phase systems. In particular, we explore the theoretical and numerical underpinnings behind non-parametric simulation schemes that utilize the error-mitigating technique of quantum subspace expansion. We focus on the emergent scenario in which the sub- space is generated by a real-time evolution implemented efficiently on quantum hardware. The practical advantages of the schemes are highlighted through demonstration of their fast and accurate extraction of spectral information.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemistry

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Shen, Yizhi

Advisor dc:contributor.advisor

• Willard, Adam P.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150557

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150557