Computational Design of Inorganic Materials
In our lab we leverage computational tools and strategies to investigate fundamental chemical and physical processes to advance scientific knowledge, improve existing technologies, and design the next generation of (smart) materials.
Our projects span a range of material classes, length-scales, properties and activities.
Click each of the options below to learn more about the group’s philosophy.
Atomistic and multi-scale modeling
Atomistic and multi-scale modeling:
We can learn a great deal about materials properties through simulation and modeling. Models can sometimes be faster and cheaper than experimenting with real materials, and let us tune into the ‘bits’ of chemistry and physics we are specifically interested in. Models of course have limitations, and to be predictable often need to be validated by experiment.
Collaboration
Collaboration:
Experiments can be used to inform and improve models and, conversely, models can be used to clarify experiments. In our group we perform (multi-scale) modeling and collaborate with experimentalists so that together we can reach a deeper understanding of the phenomena we study. We also collaborate with other modelers, who have complimentary expertise to ours.
Conceptual models
Conceptual models:
We also develop our own models. In addition to predicting behavior, toy or conceptual models are useful to study the fundamental mechanisms of materials’ properties (e.g., mechanical stability or thermal transport). Heuristic and ad hoc models can also be a tool for quick decision making and to analyze large quantities of data in useful time.
Address impactful problems
Address impactful problems:
In addition to advancing scientific knowledge, we are motivated to address impactful problems (e.g., environmental concerns and energy sustainability) and increase human welfare. In our research we consider a wide range of materials (e.g., functional materials, semiconductors, nuclear materials) and applications (e.g., energy generation, heat management, gas sorption, sensing).
Materials by design
Materials by design:
The notion that “structure determines properties” is increasingly important at a time when machine learning algorithms have become widespread and efficient. It is our long-term aim to learn to predict materials’ behavior from their components and connectivity, and in turn use this information to design materials catered to specific applications.
Uncertainty quantification & software development
Uncertainty quantification & software development:
In science, to better understand one’s results one must also understand their associated error and uncertainty. For this reason, our group is also interested in uncertainty quantification. As we develop our own tools for modeling and analysis, we are also interested in sharing them through the development of software.
Contact information
Laura de Sousa Oliveira
Assistant Professor Department of Chemistry
Adjunct Professor School of Computing
UW Derecho Professor
Department of Chemistry
1000 E. University Ave.
Laramie WY 82071