In silico materials development: Integrating atomistic simulation into academic chemistry and engineering labs

Computational chemistry is ubiquitous in academic research in chemistry, materials science, and engineering. Applied molecular modeling can drive or supplement a research project – accelerating discovery processes, minimizing the need for extensive experimental testing, and providing atomic scale insights.

In this webinar, we will explore Schrödinger’s leading physics-based and machine learning computational technologies and provide a comprehensive introduction to the capabilities of computational modeling in chemistry, materials science, and engineering.

We will discuss workflows and applications for polymeric materials, electronics, aerospace, renewable energy, catalysis, and formulations.

• • Molecular and periodic quantum mechanics (DFT) for property prediction and reaction mechanism elucidation
  • Accelerated polymer modeling with all-atom molecular dynamics
  • Coarse-grained methods to explore larger systems and longer timescales
  • Advanced machine learning models for new material discovery
  • Educational and training resources, such as Schrödinger’s seven online materials science certification courses

Following the webinar, the speaker will also be available to answer questions. Whether you are a student, an early career researcher, or an established expert seeking to expand your field of knowledge, this webinar promises to be a valuable resource for all levels of expertise interested in staying at the forefront of computational modeling in materials science.