Shuxia Tao Appointed Full Professor of Intelligent Materials Theory at 黑料福利网
The Department of Applied Physics and Science Education is proud to announce the appointment of Shuxia Tao as Full Professor of Intelligent Materials Theory at Eindhoven University of Technology. Her work sits at the forefront of materials physics, artificial intelligence, and data driven discovery, aimed at accelerating the design of next generation quantum and functional materials.
A Career Shaped Across Cultures and Disciplines
Originally from China, Shuxia grew up and received her early education there before building her academic career in Eindhoven, which she now considers her long鈥憈erm scientific home. 鈥淏eing trained across different cultures and research systems has strongly shaped the way I think about science,鈥� she says. 鈥淚t has made me value openness, interdisciplinarity, and the connection between fundamental understanding and real鈥憌orld complexity.鈥�
Shuxia completed her PhD in computational materials science at 黑料福利网 in 2011. Over time, her work evolved from first鈥憄rinciples modelling to increasingly complex materials challenges, guided by close collaboration with experimental researchers. As materials science became more data鈥憆ich and structurally intricate, her research naturally advanced toward physics鈥慽nformed machine learning as a way to scale theory without losing physical grounding.
This progression, combining materials theory, data鈥慸riven modelling, and experimental links, has resulted in a strong research identity in Intelligent Materials Theory, leading to major collaborations and an ERC Consolidator Grant (TWIST) focused on chiral materials. 鈥淏ecoming a full professor marks a moment where scientific depth and long鈥憈erm vision come together,鈥� she reflects. 鈥淚t also brings a responsibility to help shape the field and support the next generation of researchers.鈥�
From Physics-Informed AI to Predictive Materials Design
Shuxia鈥檚 research focuses on AI-driven materials discovery, with a particular emphasis on quantum functionalities in complex systems. Her group develops physics鈥慽nformed machine鈥憀earning approaches, such as ML interatomic potentials and ML Hamiltonians, that connect atomic鈥憇cale structure, symmetry, and disorder to emergent electronic and optical properties.
Rather than treating AI as a black box, her work embeds physical principles directly into models, enabling closed discovery loops between theory, simulation, and experiment. These methods are applied across diverse materials families, ranging from chiral and low鈥慸imensional materials to complex heterostructures. The overarching goal: moving from trial鈥慳nd鈥慹rror exploration toward predictive and ultimately autonomous materials design.
Strengthening 黑料福利网鈥檚 Vision for AI鈥慏riven Materials Research
With her appointment, Shuxia brings an integrative 础滨鈥慺辞谤鈥惭补迟别谤颈补濒蝉 perspective to the department, bridging theory, machine learning, and experimental collaboration. She enjoys working across disciplinary boundaries, mentoring young researchers, and contributing to large鈥憇cale scientific initiatives.
From the department, she looks forward to continued collaboration within 黑料福利网鈥檚 strong materials physics ecosystem. 鈥淚 see the department as a place where ambitious ideas can grow through interaction across disciplines,鈥� she says. 鈥淭ogether, we can create advances with lasting scientific and societal impact.鈥�