Condensed Matter Theory
The fundamental science to design and engineer complex materials
Our research team seeks to acquire a detailed understanding of condensed matter to design complex materials for use in high technology applications such as catalysts with greater selectivity and efficiency and new electronic devices.
Our focus is on ab initio investigations of materials and surface science phenomena. First-principles electronic structure calculations are used in conjunction with high performance computing to probe chemical reactions at interfaces and explore the energetics, atomic, electronic, and magnetic properties of polyatomic systems.
The premise of first principles calculations is that physical properties of materials can be calculated starting from the basic laws of Quantum Mechanics using only structural and chemical information as input parameters, without any empirical assumptions.
This allows not only to explain experimental observations, but also to make accurate predictions that can be used to guide experimental design of new and improved functional materials such as catalysts with greater activity, selectivity and stability, and novel electronic devices.
The processes that occur in solids and at surfaces play a critical role in the manufacture and performance of advanced materials (electronic, magnetic and optical devices, sensors, catalysts and hard coatings). Through simulation and understanding of these properties and processes, the aim is to accelerate materials discovery by developing new physical and chemical intuition and uncover materials design rules for applications in a range of physical, chemical, biological, medical engineering and material science problems.
We have a wide-ranging expertise in first principles calculations of structural, vibrational, electronic, transport and optical properties of novel metals, semiconductors and insulators, as well as application of efficient strategies for screening of large sets of structural and chemical compositions via machine learning approaches.
The Condensed Matter Theory Group stands at the forefront of unraveling the complexities of novel materials through the lens of quantum mechanics. This sophisticated approach lies at the heart of our understanding of the underlying principles that dictate the behavior of matter in its condensed phases. By employing quantum mechanical calculations, our research team delves deep into the microscopic world, revealing the interactions and phenomena that are crucial in the development of advanced technological applications. At the core of our research lies the commitment to a deep understanding of the fundamental mechanisms governing the physics of novel materials. Quantum mechanics, with its ability to describe the behavior of particles at the smallest scales, provides an indispensable framework. We utilize a variety of quantum mechanical models, including ab initio methods and density functional theory, to simulate and predict the behavior of materials with high accuracy. These theoretical models allow us to explore the electronic, magnetic, and optical properties of materials, offering insights into their behavior at an atomic level.
In the evolving landscape of condensed matter physics, the integration of machine learning models marks a transformative shift in our approach to studying and understanding complex materials. The Condensed Matter Theory Group is at the vanguard of this exciting intersection, leveraging the power of machine learning to uncover new insights and propel material science into a new era. By processing large datasets derived from experimental and computational sources, these models can identify patterns and correlations that are imperceptible to conventional analysis methods. In our research, we employ a variety of machine learning techniques, from neural networks to decision trees, to decipher the complex relationships between the atomic structure of materials and their physical properties. This innovative approach enables us to predict the behavior of materials with unprecedented speed and accuracy.
One of the most significant impacts of machine learning in condensed matter theory is the acceleration of material discovery and design. Traditional methods of material synthesis and characterization are often time-consuming and resource-intensive. Machine learning models, with their ability to quickly analyze vast datasets, offer a more efficient route to exploring the material landscape. We harness these models to predict new materials with desired properties, significantly reducing the trial-and-error process in laboratories. This not only speeds up the discovery of novel materials but also paves the way for designing materials withe specific applications in mind, such as energy storage, photonics, and nanotechnology.
For information about our research and opportunities to work or collaborate with us, please contact catherine.stampfl@sydney.edu.au.
