Research
Generative AI: Sampling
We can think of the dynamics as a process of navigating a rugged terrain, where the system's configurations are represented by various cavities and canyons. The transitions between these configurations are characterized by high barriers, which correspond to infrequent microscopic events that must occur to move from one state to another. When conducting computer simulations, we aim to follow realistic trajectories and obtain a statistical understanding of the system we are studying, but this is often hard because of such pitfalls. In my research, I employ machine learning techniques to design exploration paths that are optimized for the specific landscape.
Deep Complexity: Glass transition
The glass transition, where liquids turn into amorphous solids upon cooling below their melting temperature, is a well-known phenomenon with a wide range of applications. Despite its long-standing recognition, the underlying physics of glass formation remains one of the most challenging and intriguing unsolved problems in solid state theory.
My research employs a blend of machine-learning models, cutting-edge theoretical methods and computer simulations to shed light on the physics of glass formation in both model systems and functional materials like vitrimers.
Material discovery : Inverse design
Inverse design is an innovative approach to solving complex problems by starting with a set of desired outcomes or constraints, like a specific pattern of particles, and then working backward to find solutions that meet those specifications. This works particularly well when combined with modern algorithms, such as an evolutionary strategy. For this reason, this inverse approach has become increasingly popular in fields such as engineering, materials science, and bio-inspired design, where the ability to optimize complex systems or discover new materials or structures is crucial.
In my research, I use machine learning tools and computer simulations to identify the correct type of soft microgel particles that assemble into the target pattern.
Sustainability: Smart Materials
Smart materials are materials that have the ability to respond to external stimuli such as temperature, light, pressure, or electromagnetic fields. They are designed to have specific properties that can be controlled in a precise manner. Examples of smart materials include shape memory alloys, piezoelectric ceramics, and self-healing polymers like vitrimers.
They have a wide range of applications in various fields such as aerospace, automotive, biomedical and construction, as they have the potential to revolutionize the way we design and manufacture products. In my research, I develop computational models to understand and control their complex behavior.