Rapid flip-flop of phospholipids across the two leaflets of biological membranes is crucial for
many aspects of cellular life. The transport proteins that facilitate this process are classified …

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC
resources to explore the time-dependent dynamics of molecular systems. We use this …

Highlights•Recent successes of artificial intelligence (AI) and machine learning (ML)
techniques can be leveraged to obtain quantitative insights into how intrinsically disordered …

Interdependence across time and length scales is common in biology, where atomic
interactions can impact larger-scale phenomenon. Such dependence is especially true for a …

Simulations of biological macromolecules are important in understanding the physical basis
of complex processes such as protein folding. However, even with increasing computational …

The convergence of HPC and data intensive methodologies provide a promising approach
to major performance improvements. This paper provides a general description of the …

Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained
models is extremely challenging due to the need to solve the Poisson equation at every …

Increasingly, scientific discovery requires sophisticated and scalable workflows. Workflows
have become the``new applications,''wherein multi-scale computing campaigns comprise …

Machine learning (ML)-based steering can improve the performance of ensemble-based
simulations by allowing for online selection of more scientifically meaningful computations …

Recent advances in both theory and methods have created opportunities to simulate
biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble …