12 papers · ranked by Valyu relevance
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
David S. Cerutti, Rafal Wiewiora, Simon Boothroyd, Woody Sherman
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, multicore central processor units (CPUs) and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM…
Sikao Guo, Nenad Korolija, Kent Milfeld, Adip Jhaveri + 3 more
Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into…
Kecong Tang, Ahsan Sanaullah, Degui Zhi, Shaojie Zhang
Durbin’s positional Burrows-Wheeler transform (PBWT) enables algorithms with the optimal time complexity of O(MN) for reporting all vs all haplotype matches in a population panel with M haplotypes and N variant sites. However, even this efficiency may still be too slow when the number of haplotypes reaches millions. To…
Authors not listed
Protein conformational landscapes contain the functionally relevant information useful for understanding biological processes. Mapping out conformational landscapes provides valuable insights into protein behaviors and biological phenomena, and has relevance to therapeutic design. While experimental structural biology…
Manuel Carrer, Henrique Musseli Cezar, Sigbjørn Løland Bore, Morten Ledum + 1 more
We develop #-HylleraasMD (#-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. #-HyMD is templated on the recently established HylleraaasMD software, while…
Authors not listed
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
A. Lucas Martins, Alexandre Laborde, Michael Orger
We present Sardine, a software framework built with.NET to control experimental setups through the reliable execution of dynamic networks of independent modules, where each module can interface with a hardware device (e.g., camera, motor) or represent an operation over data (e.g., image filter, data stream). The…
Marco Savioli, Paolo Calligari, Ugo Locatelli, Gianfranco Bocchinfuso
We introduce GROMODEX, a novel tool designed to optimise GROMACS molecular dynamics (MD) simulations using a structured Design of Experiments (DoE) approach. GROMACS, though efficient, requires extensive tuning of parameters to perform optimally on different hardware and molecular systems. Manual tuning is tedious and…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of…