We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| i-pi/i-pi | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| Message | Ran | i-pi/i-pi/drivers/py/driver.py pointer only (licence: NONE) · get_code("41326889cc51391e") |
| mask_from_fix | Ran | i-pi/i-pi/ipi/engine/barostats.py pointer only (licence: NONE) · get_code("f4cb24d66e7dc129") |
| read_xml | Ran | i-pi/i-pi/demos/2D-IR-Raman/noneqm-response.py pointer only (licence: NONE) · get_code("08b6fd10f68e07ff") |
| recv_data | Ran | i-pi/i-pi/drivers/py/driver.py pointer only (licence: NONE) · get_code("0e0f4f7dfcada81f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques, thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.15224")
get_code_for_paper("2405.15224")
have("2405.15224")
Connect an agent — have() is free.