Advance Materials Research with Atomistic Modeling and Machine Learning

In the pursuit of sustainable, high-performance semiconductors, batteries, and optical devices, the development of advanced materials is essential. Tackling the complexities of materials and processes necessitates innovative, data-driven research approaches. The Amsterdam Modeling Suite (AMS) provides a comprehensive framework for simulating materials across multiple levels of theory, integrating atomistic engines (DFT, DFTB, ReaxFF, ML potentials) with a central driver for exploring potential energy surfaces (PES) through molecular dynamics and Grand Canonical Monte Carlo simulations. The combination of the AMS driver with its dedicated Python interface facilitates the automatic screening of materials, optimizing their structural and electronic properties.<br/>Recent developments include a platerform for near-universal machine learning interatomic potentials, and an easy program to fine-tune them for your application. With ParAMS, users can train individual or multiple machine learning potentials from scratch, fine-tune universal models, or actively learn the PES based on target molecular dynamics simulations. Optimized models can be directly used as an engine with the AMS driver. This capability enables the rapid prediction of material properties and reaction mechanisms, accelerating the design and testing of new materials. We will showcase examples related to catalysis and battery modeling.<br/>Our data-driven approach empowers the materials R&D community to surpass traditional development obstacles, enabling accurate material performance predictions and the discovery of new materials and mechanisms, paving the way towards realizing innovative solutions.