From 3457c30d8e2bec08ce9b53d1506d3947f13e4bac Mon Sep 17 00:00:00 2001 From: Anders Johansson Date: Thu, 21 Mar 2024 17:24:21 -0400 Subject: [PATCH] add tutorial to readme, remove dead links --- README.md | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index b6c08a578..1ab6f7a8d 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@

-FLARE is an open-source Python package for creating fast and accurate interatomic potentials. +FLARE is an open-source Python package for creating fast and accurate interatomic potentials. ## Major Features @@ -18,7 +18,7 @@ Note: We implement Sparse GP, all the kernels and descriptors in C++ with Python interface. -We implement Full GP, Mapped GP, RBCM, Squared Exponential kernel and 2+3-body descriptors in Python. +We implement Full GP, Mapped GP, RBCM, Squared Exponential kernel and 2+3-body descriptors in Python. Please do NOT mix them. @@ -33,20 +33,16 @@ Documentation of the code can be accessed here: https://mir-group.github.io/flar [FLARE (ACE descriptors + sparse GP)](https://colab.research.google.com/drive/1rZ-p3kN5CJbPJgD8HuQHSc7ecmwZYse6). The tutorial shows how to run flare with ACE and SGP on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field. All the trainings use yaml files for configuration. +[FLARE (LAMMPS active learning)](https://bit.ly/flarelmpotf) +This tutorial demonstrates new functionality for running active learning all within LAMMPS, with LAMMPS running the dynamics to allow arbitrarily complex molecular dynamics workflows while maintaining a simple interface. This also demonstrates how to use the C++ API directly from Python through `pybind11`. Finally, there's a simple demonstration of phonon calculations with FLARE using `phonopy`. + [FLARE (ACE descriptors + sparse GP) with LAMMPS](https://colab.research.google.com/drive/1qgGlfu1BlXQgSrnolS4c4AYeZ-2TaX5Y). The tutorial shows how to compile LAMMPS with FLARE pair style and uncertainty compute code, and use LAMMPS for Bayesian active learning and uncertainty-aware molecular dynamics. -[FLARE (ACE descriptors + sparse GP) Python API](https://colab.research.google.com/drive/18_pTcWM19AUiksaRyCgg9BCpVyw744xv). -The tutorial shows how to do the offline and online trainings with python scripts. -A video walkthrough of the tutorial, including detailed discussion of expected outputs, is available [here](https://youtu.be/-FH_VqRQrso). - -[FLARE (2+3-body + GP)](https://colab.research.google.com/drive/1Q2NCCQWYQdTW9-e35v1W-mBlWTiQ4zfT). -The tutorial shows how to use flare 2+3 body descriptors and squared exponential kernel to train a Gaussian Process force field on-the-fly. - [Compute thermal conductivity from FLARE and Boltzmann transport equations](https://phoebe.readthedocs.io/en/develop/tutorials/mlPhononTransport.html). The tutorial shows how to use FLARE (LAMMPS) potential to compute lattice thermal conductivity from Boltzmann transport equation method, with [Phono3py](https://phonopy.github.io/phono3py/) for force constants calculations and [Phoebe](https://mir-group.github.io/phoebe/) for thermal conductivities. -[Using your own customized descriptors with FLARE](https://colab.research.google.com/drive/1VzbIPmx1z-uygKstOYTj2Nqr53AMC5NL?usp=sharing). +[Using your own customized descriptors with FLARE](https://colab.research.google.com/drive/1VzbIPmx1z-uygKstOYTj2Nqr53AMC5NL?usp=sharing). The tutorial shows how to attach your own descriptors with FLARE sparse GP model and do training and testing. All the tutorials take a few minutes to run on a normal desktop computer or laptop (excluding installation time). @@ -80,7 +76,7 @@ flare++ is tested on a Linux operating system (Ubuntu 20.04.3), but should also ### Hardware requirements There are no non-standard hardware requirements to download the software and train simple models—the introductory tutorial can be run on a single cpu. To train large models (10k+ sparse environments), we recommend using a compute node with at least 100GB of RAM. - + ## Tests We recommend running unit tests to confirm that FLARE is running properly on your machine. We have implemented our tests using the pytest suite. You can call `pytest` from the command line in the tests directory. @@ -95,7 +91,7 @@ pytest - If you use FLARE++ including B2 descriptors, NormalizedDotProduct kernel and Sparse GP, please cite the following paper: > [1] Vandermause, J., Xie, Y., Lim, J.S., Owen, C.J. and Kozinsky, B., 2021. *Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics.* [arXiv preprint arXiv:2106.01949](https://arxiv.org/abs/2106.01949). - + - If you use FLARE active learning workflow, full Gaussian process or 2-body/3-body kernel in your research, please cite the following paper: > [2] Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M. & Kozinsky, B. *On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events.* npj Comput Mater 6, 20 (2020). https://doi.org/10.1038/s41524-020-0283-z