Computational studies of pressure effects on complex systems
Heather Wiebe, Department of Chemistry, Vancouver Island University
High pressure environments are prevalent in our universe, ranging from the relatively modest pressure of the deep ocean to the extreme high pressure of planetary cores. Unlike temperature, which accelerates all elementary reactions regardless of their mechanism, increases in pressure will favour only those reactions that yield a decrease in the volume of the system. Application of pressure can therefore induce some very interesting and unexpected chemical, physical and electronic transformations. Pressures on the order of 1 kbar will result in the unfolding of most proteins, due to the loss of internal cavities and hydration of previously buried amino acid residues in the unfolded state. Extreme high pressures can result in surprising changes in the electronic properties of materials, such as the recent discovery of solid metallic hydrogen at 4.25 Mbar.
Unfortunately, extreme high pressure conditions are difficult and expensive to generate in a laboratory setting. Molecular dynamics simulations provide an attractive alternative for studying processes at high pressure. In this talk, I will review our recent work on the kinetic isotope effect in liquid metallic hydrogen using path integral molecular dynamics, and our investigation into the physico-chemical mechanism of pressure resistance in proteins from deep-sea organisms using alchemical free energy calculations and Archimedean displacement volumes.
 J. Roche et al, Proc. Natl. Acad. Sci. USA, 109, 6945-6950 (2012)
 P. Loubeyre et al, Nature, 577, 631-645 (2020)
Machine learning for nanoporous materials design
Seyed Mohamad Moosavi, Freie Universität Berlin
The success of the research on metal-organic frameworks (MOFs) and related porous materials over the past two decades makes it now possible to believe we can tailor-make materials with desired properties for several key environmental-related applications, such as carbon capture and energy storage. To fully realize the potential of this development, we need to find materials that perform optimally in multi-scale processes, considering scales from the molecular level to the chemical plant. However, this requires a holistic perspective over the full design and discovery process, which involves exploring immense materials spaces, various material properties, their synthesis, as well as process design and engineering. The complexity of exploring all potential options by conventional scientific approaches seems intractable. Therefore, we are now developing tools from the field of machine learning and artificial intelligence that will enable us to pursue our aim of understanding and designing materials in a new way.
In my talk, I will discuss some steps toward this approach, presenting machine learning methods in the context of the rational design of MOFs for adsorption applications, starting from the molecular scale with a view toward the next steps. We will discuss why and how to quantify the structural diversity of MOF material databases, assess newly-reported materials’ novelty, design MOFs with desired thermal properties, automate force field generation, and learn from failed experiments.