Beyond Ground State Simulations: Navigating Challenges in Excited States of Extended Molecules and Materials

You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/beyond-ground-state-simulations-navigating-challenges-in-excited-states-of-extended-molecules-and-materials-1285.

Registration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any).  However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.

Description
The computational study of excited states of molecules and materials plays a critical role in various scientific fields and technological applications [1,2]. It is instrumental in advancing our understanding and development in areas such as solar energy conversion (photocatalysts and organic electronics) [3-5] as well as forward and inverse design of molecules and materials [6-9]. By providing insights into the energetics, dynamics, and properties of excited states, computational studies enable the design of novel materials with enhanced functionalities, optimization of device performance, and the exploration of new avenues for sustainable technologies [10].
Despite the importance of excited-state simulations they are seriously limited by the high computational costs of quantum chemical calculations and the lack of accurate methods, especially in the realm of extended systems [2,11]. While theoretical models and methodologies for describing excited states of small molecular systems exist, there is a lack of complementary methods to investigate excited-state processes in larger systems such as large molecules (e.g., molecular electronics, and biomolecules) and materials (e.g., semiconductors and heterogeneous systems). The objective of this workshop is thus to bring together experts from computational chemistry that study excited-state of molecular systems with experts from computational materials science.
The workshop will focus on the following four challenges, which we have identified to require particular attention:

• 1) Charge Transfer: Charge transfer processes play a crucial role for efficient charge separation and transport across material interfaces or through molecular wires and junctions, which is pivotal in, e.g., solar energy conversion [12,13]. Therefore, it is crucial to study the excited states involved in charge transfer to understand the underlying mechanisms and to develop strategies to optimise the performance of molecules (photocatalysts) and materials (photovoltaics). However, the respective computational study of charge transfer processes faces challenges due to factors such as system size, complexity, accurate modeling of excited states, non-adiabatic effects and interactions with solvents and the environment. Addressing these challenges necessitates advances in theoretical methods.
• 2) Long-range interactions: Long-range interactions have been extensively studied in ground state simulations [14] but their significance in excited states has recently gained attention and is thought to have an even greater impact than in ground states.
• 3) Nonadiabatic Molecular Dynamics: Understanding the dynamics of excited states is essential for predicting their properties [2,11,15]. However, performing molecular dynamics in the excited states is not only computationally more demanding than in the ground-state, but often also requires the treatment of nuclear quantum effects that make the development of new methods particularly challenging.
• 4) Machine Learning for excited states of extended systems: Machine learning has shown promise in accelerating materials discovery and the simulation of extended systems in their ground state [5,7], but their excited states are far from being explored. Challenges are for instance the non-transferability of many excited-state properties between chemical systems or the increasing number of states with increasing system size that need to be incorporated properly in a machine learning model.