Overview Data is essential in data-driven projects, as it forms the foundation for all subsequent analysis, modeling, and decision-making. Depending on the specific task, raw data may be collected from a wide variety of sources such as databases, APIs, sensors, logs, documents, or images. Before it can be effectively used for analysis or machine learning, […]
About this webinar Quantum-mechanical methods such as density functional theory (DFT) are accurate but limited to small systems and short timescales. Classical force fields are fast but often not accurate or transferable enough. Machine-learned interatomic potentials (MLIPs) could break this trade-off between accuracy and scale, and the field is now moving at remarkable speed. So-called […]
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Iceland.
