A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Mathieu Jacomy, Tommaso Venturini, Sébastien Heymann, et al.
ForceAtlas2 is a force-directed graph layout algorithm designed for Gephi, integrating techniques like Barnes-Hut simulation and adaptive temperatures for network visualization.
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, et al.
ASE is a Python library for setting up, steering, and analyzing atomistic simulations with a uniform interface to external calculators and built-in simulation tasks.
Aidan P. Thompson, Hasan Metin Aktulga, Richard A. Berger, et al.
This paper serves as the definitive reference for LAMMPS, describing its fundamental algorithms, design strategies for flexibility, and recent capabilities enabled by its extensible architecture.
Berk Hess, Carsten Kutzner, David van der Spoel, et al.
GROMACS 4 introduces algorithmic optimizations and parallelization techniques that achieve high performance and scalability for molecular dynamics simulations.
Kai Stewart, Yasunori Toshimitsu, Robert K. Katzschmann
A real-time Jacobian estimation controller enables an anthropomorphic hand to write with a grasped pen via purely in-hand motion, achieving sub-millimeter precision without simulation training or demonstrations.
Muhammad Shahab, Guojun Zheng, Abbas Khan, et al.
This study uses machine learning models (k-NN, SVM, RF, GNB) to virtually screen compounds for CDK2 inhibition, identifying three novel potential inhibitors validated by molecular docking and MD simulation.
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This paper proposes a method to transfer exploratory policies from simulation to real-world reinforcement learning, overcoming the sim-to-real gap even when direct transfer fails.
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This paper proposes closing the sim-to-real loop by adapting simulation randomization based on real-world experience to improve robot learning transfer.
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This paper introduces a sim-to-real approach using randomized-to-canonical adaptation networks to transfer a vision-based closed-loop grasping reinforcement learning agent from simulation to a real robot.
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A review of systematic approaches to randomized simulations for robot learning, building on early methods to advance sim-to-real transfer.
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This paper introduces Flow Matching (FM), a simulation-free method for training continuous normalizing flows that enables the use of general probability paths.
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LLM agents grounded in qualitative or quantitative self-reports enable general-purpose simulation of individuals across outcomes.