Technische Universität München
Professur für Digitale Katalyse (Prof. Stein)
Lichtenbergstr. 4
85748 Garching b. München
We utilize digital tools like data management, machine learning, and robotics to catalyze battery and catalysis research acceleration. We don't just discover and optimize materials, components, and processes but try to investigate non-linear effects originating from a materials-in-systems perspective, such as batteries and chemical reactors. Research in our group is predominantly experimental, emphasizing (hardware and software) engineering in chemical engineering. We also research on accelerating the very way how research is executed and deploy Materials Acceleration Platforms. Currently, we investigate aqueous and solid-state batteries, oxidation, and hydrogenation catalysis, as well as data generation for foundational models in the natural sciences (see research overview).
Accurate prediction of battery behavior under different dynamic operating conditions is critical for both fundamental research and practical applications. However, the diversity of emerging materials…
Accelerated formation protocols that utilize pulsed charging offer an unprecedented wealth of electrochemical data. Herein, methods are presented to extract diagnostic data relating to pseudodiffusion…
High-performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast-charging anode materials system…
The growing market for electric vehicles and portable electronics requires the reliability assessment of Li-ion batteries, especially in terms of nonlinear capacity degradation. For this purpose, a…
High-performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast-charging anode materials system…
Efforts to enhance the state of health (SOH) estimation for lithium-ion batteries have increasingly focused on diverse machine learning methods, especially with the promising artificial intelligence…
Anode-free Li-metal batteries offer high energy density but are prone to dendrite formation during charging which can cause catastrophic failures. Ensuring dendrite-free smooth Li deposits during…
P2-type cobalt-free MnNi-based layered oxides are promising cathode materials for sodium-ion batteries (SIBs) due to their high reversible capacity and well chemical stability. However, the phase…
Layered oxides constitute one of the most promising cathode materials classes for large-scale sodium-ion batteries because of their high specific capacity, scalable synthesis, and low cost. However,…
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing…
| Titel | Termine | Dauer | Art | Vortragende/r (Mitwirkende/r) |
|---|---|---|---|---|
| Hochdurchsatzmethoden und -verfahren (NAT0125) |
|
4 | VI | |
| Machinelles Lernen in den Naturwissenschaften |
|
2 | PS | |
| Programmierung und Data-Science (CH0103) |
|
4 | UE | |
| Repetitorium zu Machinelles Lernen in den Naturwissenschaften |
|
2 | RE | |
| Seminar und Aktuelle Themen in der Digitalen Katalyse (NAT0126) |
|
2 | SE | |
| Tag der Absolventinnen und Absolventen der Chemie |
|
0.1 | KO |
| Titel | Termine | Dauer | Art | Vortragende/r (Mitwirkende/r) |
|---|---|---|---|---|
| Hochdurchsatzmethoden und -verfahren (NAT0125) |
|
4 | VI | |
| Methods of Catalysis (CH3242a) |
|
2 | VO | |
| Methods of Catalysis, Seminar (CH3242b) |
|
1 | SE | |
| Seminar und Aktuelle Themen in der Digitalen Katalyse (NAT0126) |
|
2 | SE | |
| Theorie und Praxis zum Datenmanagement in der Chemie (NAT0127) |
|
2 | VI |