The Solid-Electrolyte Interphase: A Challenge for Battery R&D

Amadeus Barth is responsible for the business development and ensures that Quantistry can develop its full potential. Whether it's sales, marketing, PR or CRM: he keeps an eye on everything. In this interview, he tells us what are the most important strengths of Quantistry, where the company wants to go in the upcoming months and years and how he spends his free time when he's not busy with the computational chemistry market.

At Quantistry, our goal is to provide R&D with valuable tools so that they can leave well-trodden paths and break new ground. With our simulation platform Quantistry Lab, we have taken the first step in this direction. To enable new ideas to grow in the digital lab, we have implemented helpful features that tease out the greatest possible potential from the simulations.

Concerns about insufficient data security are the main reason why on-premise software is still dominant in the simulation sector today, despite considerable drawbacks. Cloud-based solutions, on the other hand, are still viewed critically, although the main arguments against this technology can now be easily refuted.

Stefan Mattsson holds a PhD in chemistry and is one of several experts at Quantistry responsible for the reliable functioning of our simulations and the scientific validity of our results. Learn more about him and his job.

Machine learning can be used to solve problems, especially in the area of structure-activity relationships, that would be impossible or would require immense effort to solve using standard methods due to their level of complexity. At Quantistry, we therefore use this technology to make our analyses even faster and more reliable.

For solving the vast majority of chemical problems in the industry it is indispensable to combine various simulation methods. In our platform, this is realized by the combination of different tasks in a workflow-logic. A prominent example is theoretical prediction and analysis of chemical reactions, where not only quantum chemical methods but also machine learning (ML) are used.