249 citations · 928 across the 13 of their papers we have counts for
14 papers
PySAGES: flexible, advanced sampling methods accelerated with GPUs
Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo R. Pérez-Lemus +11
Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced…
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling
Ellis R. Crabtree, Juan M. Bello-Rivas, Andrew L. Ferguson +1
Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in m…
Reconstruction of Protein Structures from Single-Molecule Time Series
Maximilian Topel, Andrew L. Ferguson
Single-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a co…
Molecular Latent Space Simulators
Hythem Sidky, Wei Chen, Andrew L. Ferguson
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kin…
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani +14
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as…
Discovery of Self-Assembling -Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation
Kirill Shmilovich, Rachael A. Mansbach, Hythem Sidky +4
Electronically-active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from -c…
Statistically optimal continuous free energy surfaces from biased simulations and multistate reweighting
Michael R. Shirts, Andrew L. Ferguson
Free energies as a function of a selected set of collective variables are commonly computed in molecular simulation and of significant value in understanding and engineering molecu…
High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets
Hythem Sidky, Wei Chen, Andrew L. Ferguson
State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajec…
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems
Wei Chen, Hythem Sidky, Andrew L. Ferguson
Time-lagged autoencoders (TAEs) have been proposed as a deep learning regression-based approach to the discovery of slow modes in dynamical systems. However, a rigorous analysis of…
Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
Wei Chen, Hythem Sidky, Andrew L Ferguson
The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow coll…
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Wei Chen, Andrew L Ferguson
Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamic…
A study of the morphology, dynamics, and folding pathways of ring polymers with supramolecular topological constraints using molecular simulation and nonlinear manifold learning
Jiang Wang, Andrew Ferguson
Ring polymers are prevalent in natural and engineered systems, including circular bacterial DNA, crown ethers for cation chelation, and mechanical nanoswitches. The morphology and…
Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension
Andrew W. Long, Andrew L. Ferguson
Diffusion maps are a nonlinear manifold learning technique based on harmonic analysis of a diffusion process over the data. Out-of-sample extensions with computational complexity $…
Spin models inferred from patient data faithfully describe HIV fitness landscapes and enable rational vaccine design
Karthik Shekhar, Claire F. Ruberman, Andrew L. Ferguson +3
Mutational escape from vaccine induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Kno…