Product
Our tech world view
Information technology has been exponentially improving for decades, yet material development remains well-nigh untouched by this transformation. While simulation techniques have long been developed in academia, they have still to be widely established in industrial applications. Common barriers are the highly specialized skills needed to use existing tools, computational infrastructure and lack of trust in the ability of simulations to reproduce material properties. In addition, most of the value in simulation trajectories go unused due to insufficient analysis techniques.
Compular is addressing these technology challenges by building a software designed to be easy to use for material scientists without computational background, where the simulation process is automated and run in the cloud, and much more value is extracted by our advanced analysis capabilities.
Client references
How it works
Setting up simulations
Generating geometries
Running simulations
Analysing data
Visualising results
Setting up simulations
The user simply specifies the system composition, the physical conditions and the target properties to be predicted. Based on this information the software automatically selects simulation package, level of theory and suitable simulation parameters, and sets up the full simulation case, saving time and eliminating the need for computational expertise. This is possible because we carefully validate new features for different classes of materials before launching them.
Generating geometries
Crystalline structures are generated by repeating a unit cell and adding optional defects at a fraction of sites. Amorphous systems are generated by randomizing a starting geometry and relaxing it until atoms are not unreasonably close given the material composition and density. Composites and liquid/solid systems are generated by combining separately generated regions. The user just needs to give the minimum needed information for our system builder to do the rest.
Running simulations
The simulations are automatically submitted to our elastic compute cloud and dispatched to hardware optimized for each job. The user can track the progress of running simulations from the app. We use different well-established third-party simulation software packages for different types of simulations and classes of materials to get the best trade-off between accuracy and performance in each case.
Analysing data
Visualising results
All simulation and analysis data is stored in a structured manner in our databases, or downloaded to your own data stores, and can be queried by our app locally or in the cloud. Based on the material properties requested by the user, an automatic dashboard will be generated to visualize the results in a default presentation. Results for different systems can also be compared. The data can be accessed by a Python API within the app, enabling custom scripting and visualisation. Graphs, tables and raw data can also be exported.
| Purpose | Key properties | With Compular you can... |
|---|---|---|
|
Power density & Fast charging |
✓ Max current ✓ Voltage ✓ Rate capabilites |
...optimize your electrolyte composition for (cat)ionic conductivity, transference number, rate of solvation dynamics, viscosity, diffusivities, stability vs electrodes |
|
Battery safety |
✓ Operating Temp. Range ✓ Volatility ✓ Flammability |
...optimize for conductivity, transference number etc. using low volatile salts/solvents ...perform high temperature screenings |
|
Energy Density |
✓ Voltage ✓ Capacity |
...compute HOMO/LUMO gaps and redox potentials to predict voltage stability of the electrolyte |
|
Environmental Friendliness |
✓ Toxicity ✓ Material Abundance ✓ Production Chain |
...screen electrolytes using more environmentally friendly chemistries without the need to purchase them beforehand |
|
Battery life |
✓ SEI growth and composition ✓ Resistance build-up ✓ Loss of active material and electrolyte |
...predict electrolyte composition at the molecular level and reaction mechanisms of degradation to better access the composition of SEI/CEI ...predict reduction and oxidation process to access the quality of SEI/CEI. |
|
Low temperature performance |
✓ Liquidus range ✓ Viscosity ✓ Ionic conductivity |
...optimize (cat)ionic conductivity, viscosity, liquidus range, diffusivities, rate of solvation dynamics for low temperature performance |
Free trial
Although we are hard at work creating the world’s most advanced and intuitive material property prediction software, we can’t wait to let users try it out right away. Therefore, we offer a free trial version of the full product to selected customers when booking a meeting.
Free trial
Although we are hard at work creating the world’s most advanced and intuitive material property prediction software, we can’t wait to let users try it out right away. Therefore, we offer a free trial version of the full product to selected customers when booking a meeting.
Application areas
Although Compular’s solution is mostly used within battery material development today, the opportunities are endless. Below we highlight just a few. Reach out to us if you wish to know what we can do in the area of your interest!
Batteries
Fuel cells
Metals and alloys
Pharma
Electronics
Specialty chemicals
Product benefits
Traditional R&D practices within material development have (until now) been characterized by trial-and-error-based working methods. Without an increased understanding of what compositions are the most promising, the R&D process becomes increasingly limited. This leads to high costs in salaries, equipment and materials; longer time to reach optimal compositions; and overall a more difficult path to reach the best products. Combining existing methods with Compular’s product, customers gain a more focused, resource-efficient and faster way to create competitive products.
- Higher lab trial success rates
- Faster product development cycles
- Enabling more qualitative products
Product benefits
- Higher lab trial success rates
- Faster product development cycles
- Enabling more qualitative products
Traditional R&D practices within material development have (until now) been characterized by trial-and-error-based working methods. Without an increased understanding of what compositions are the most promising, the R&D process becomes increasingly limited. This leads to high costs in salaries, equipment and materials; longer time to reach optimal compositions; and overall a more difficult path to reach the best products. Combining existing methods with Compular’s product, customers gain a more focused, resource-efficient and faster way to create competitive products.
Product benefits
Traditional R&D practices within material development have (until now) been characterized by trial-and-error-based working methods. Without an increased understanding of what compositions are the most promising, the R&D process becomes increasingly limited. This leads to high costs in salaries, equipment and materials; longer time to reach optimal compositions; and overall a more difficult path to reach the best products. Combining existing methods with Compular’s product, customers gain a more focused, resource-efficient and faster way to create competitive products.
- Higher lab trial success rates
- Faster product development cycles
- Enabling more qualitative products
Showcase results
Research references
R. Andersson, F. Årén, A. A. Franco and P. Johansson, Ion Transport Mechanisms via Time-dependent Local Structure and Dynamics in Highly Concentrated Electrolytes, Journal of the Electrochemical Society, 2020, DOI:10.1149/1945-7111/abc657
P. Jankowski, R. Andersson and P. Johansson, Designing high-performant lithium battery electrolytes by utilizing two natures of Li+ coordination: LiTDI/LiTFSI in Tetraglyme, Batteries & Supercaps, 2020, 4(1), 205-213, DOI:10.1002/batt.202000189.
R. Andersson, Dynamic Structure Discovery and Ion Transport in Liquid Battery Electrolytes, Chalmers University of Technology, 2020
R. Andersson, F. Årén, A. A. Franco and P. Johansson, CHAMPION: Chalmers Hierarchical Atomic, Molecular, Polymeric & Ionic Analysis Toolkit, Journal of Computational Chemistry, 2021, DOI:10.1002/jcc.26699
R. Andersson, O. Borodin, P. Johansson, Dynamic Structure Discovery Applied to the Ion Transport in the Ubiquitous Lithium-ion Battery Electrolyte LP30, Journal of the Electrochemical Society, 2022, DOI:10.1149/1945-7111/ac96af
F. Årén, R. Andersson, A. A. Franco, and P. Johansson, Global and Local Structure of Lithium Battery Electrolytes: Origin and Onset of Highly Concentrated Electrolyte Behavior, Journal of The Electrochemical Society, 2023, 170(6), DOI:10.1149/1945-7111/acd8f9