Modern Astrochemistry faces crucial problems, such as predicting the abundance of molecular species from computational models or understanding reaction pathways of large complex organic molecules that are of prebiotic relevance. This has implications in lacking understanding of fundamental astrophysical processes, such as planet formation or characterizing exoplanet atmospheres. There is a need in the astrochemical community for developing more comprehensive and unbiased models to understand the complex and diverse chemistry in astrophysical environments. This workshop aims to interdisciplinarly connect between communities from Theoretical Chemistry, Astrochemistry and (Graph-based) Data Science to make progress in modeling complex (astro)chemical systems.
Astrochemistry is a relatively young yet thriving field, with more than 300 molecules detected so far in the interstellar medium. Among them, there are not only simple species but also interstellar complex organic molecules, the building blocks of prebiotic species and of particular interest in the context of the origins of life.
Astrochemical modeling is a fundamental tool for reproducing observed molecular abundances and investigating their formation pathways. However, the challenge lies in handling complex reaction networks, which include gas-phase and grain-surface reactions. Only a few of these reactions have been studied theoretically or in laboratory experiments, mainly due to their computational expense or time-consuming nature. For some observed species, for instance the recently discovered indene, model predictions are inaccurate by several orders of magnitude, indicating that current formation pathways in astrochemical models are incomplete or incorrect. There is a need in the astrochemical community for developing more comprehensive and unbiased models to understand the complex and diverse chemistry in astrophysical environments.
One example problem is that the number of detected species is significantly growing but there is a lack of understanding their connections/reactions. This is a key issue as Chemistry is the science of transformations (of molecules). In an abstract manner, one can reformulate the problem by asking “What is the network if connections are unconstrained?” which sets a natural link between Chemistry and (Graph-based) Data Science. This question is aimed to get addressed in synergy from the (Astro)Chemistry and Data Science community. Theoretical Chemistry and Data Science might offer new insights into modeling complex (astro-)chemical systems. These new techniques are valuable not only for reproducing the abundances of large sets of measured molecules, but also to predict abundances for new candidate molecules that can be investigated with upcoming and future observational facilities. They might offer insights into the most promising reactions to be investigated in the laboratory, enabling more focused and tailored experiments.
As this workshop is highly interdisciplinary, we plan to have introductory talks to the following topics:
* Introductory talk: Astrochemistry
* Introductory talk: Reaction networks
* Introductory talk: ML in chemistry / Simulations in Chemistry
* Introductory talk: Astrochemical modeling
Topics of the workshop:
* Reaction networks (Theoretical Chemistry)
* Astrochemistry (in general and gas vs solid-state chemistry)
* (Graph-based) Data Science/Machine learning
Key scientific questions of this workshop:
* Which established (Graph-based) Data Science methods can help exploring chemical reaction networks?
* Can we model chemical reaction networks from a list of molecules only (without knowing the reactions)?
* What is the molecular network of different astrophysical sources, e.g. exoplanet atmospheres?
* How do complex (astro)chemical systems evolve?
* Do we need better data or better analysis methods?
(Potentially) common topics between (Astro)chemistry and Data Science communities:
* Complexity (definition, how to measure)
* Evolution of systems/networks
* Observation and simulation of data