(2bc) Physics-Based and AI-Driven Design of Functional Soft Materials

Research Interests:
Many of the most pressing societal challenges of this century – from energy harvesting and storage to personalized healthcare and sustainability – share a common theme: their solution relies heavily on the development of new materials. Among emerging materials with advanced electronic, chemical, and mechanical functionalities, soft materials stand out. Soft materials are highly biocompatible, consist of earth-abundant elements, and are exceptionally tunable, which allows for the introduction of many desired properties into a single material. In particular, soft materials with electronic properties offer unique solutions for stretchable electronics, biomedical sensors, and recyclable electronics. However, the many tunable parameters of soft (electronic) materials, while offering great opportunities, also pose great challenges to the search for materials with tailored properties. Labor-intensive experimental trial-and-error cycles are too time-consuming and must be guided by in silico design.

My group will integrate physics-based multiscale methods with artificial intelligence techniques to explore and engineer soft materials that possess advanced electronic, chemical, and mechanical functionalities. By combining these approaches, we will aim to gain a deep understanding of the underlying principles governing these materials and leverage this knowledge to design novel soft materials with enhanced properties. We will initially focus on the following research areas:

1. We will develop methodologies, combining physics-based and machine learning techniques, to incorporate quantum mechanical phenomena at large spatiotemporal scales. Such machine learning-enhanced multiscale methodologies will enable the simulation of inherently multiscale phenomena such as electronic conduction in soft disordered materials from the bottom-up.

2. New types of soft materials exhibit unique features, including the combination of ionic and electronic conduction as well as the interaction between electronic and mechanical properties. To gain a fundamental understanding of these materials, we will use machine learning-enhanced multiscale models to unravel the fundamental mechanisms of (mixed) electronic and ionic conduction and its coupling to mechanical properties such as stretchability, and use this knowledge to design new soft materials for applications in bio-compatible electronics, all-organic batteries, and degradable materials.

3. The emergence of machine learning approaches to computational modeling has transformed the landscape of materials research, providing data-driven strategies to enhance conventional computational methods. While powerful for many problems in chemistry, engineering, and materials science, successful application of machine learning to problems in soft materials has been much more limited. This has to do with challenges that are inherent to soft materials, such as their statistical and disordered nature, and the dependence of properties on processing conditions and hierarchical interactions. We will address these challenges and integrate machine learning techniques and high-throughput molecular simulations to facilitate the design of soft materials with tailored properties.

Teaching Interests:
In addition to my research interests, I am a dedicated teacher. As a graduate student at the University of Groningen, I have served as a teaching assistant for physical chemistry, molecular dynamics, and quantum chemistry courses. During my time as a postodoc, I have co-developed short undergraduate and graduate level courses on coarse-grained modeling and enhanced sampling techniques, including lectures and hands-on tutorials.

Background:
I received my Ph.D. in 2019 from the University of Groningen, the Netherlands as a NWO Advanced Materials Graduate fellow with Profs. Siewert-Jan Marrink and Ria Broer. My graduate work focused on multiscale modeling of organic semiconductors and Martini coarse-grained model development. I then joined the Pritzker School of Molecular Engineering at the University of Chicago as a postdoc with Prof. Juan de Pablo in the fall of 2020 and was subsequently awarded a NWO Rubicon Postdoctoral Fellowship. My postdoctoral work has focused on the development of the development of machine learning-enhanced multiscale modeling techniques and their application to the design of radical-containing polymers for next-generation energy storage materials.

Successful Proposals:
Dutch Research Council (NWO) Rubicon Fellowship, European Commission HPC-Europa3 Transnational Access Programme grant, Dutch Research Council (NWO) Graduate Programme Advance Materials Fellowship.

Selected Publications:
1. R. Alessandri, J. J. de Pablo, "Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions", Macromolecules 2023, 56, 3574–3584.
2. R. Alessandri, J. Barnoud, A. S. Gertsen, I. Patmanidis, A. H. de Vries, P. C. T. Souza, S. J. Marrink, "Martini 3 Coarse-Grained Force Field: Small Molecules", Adv. Theory Simul. 2022, 5, 2100391.
3. R. Alessandri, P. C. T. Souza, S. Thallmair, M. N. Melo, A. H. de Vries, S. J. Marrink, "Pitfalls of the Martini Model", J. Chem. Theory Comput. 2019, 15, 5448–5460.
4. R. Alessandri, J. J. Uusitalo, A. H. de Vries, R. W. A. Havenith, S. J. Marrink, "Bulk Heterojunction Morphologies with Atomistic Resolution from Coarse-Grain Solvent Evaporation Simulations", J. Am. Chem. Soc. 2017, 139, 3697–3705.