Merge branch 'master' into dependabot/pip/pygments-2.15.0

.github/dependabot.yml

@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"

.github/workflows/flare.yml

@@ -38,7 +38,7 @@ jobs:
 
     - name: Build
       run: |
-        sudo apt install liblapacke liblapacke-dev
+        sudo apt install liblapacke liblapacke-dev libopenmpi-dev
         mkdir ${BUILD_DIR}
         cd ${BUILD_DIR}
 
@@ -112,11 +112,23 @@ jobs:
         cd tests
         pytest test_lammps.py
 
+    - name: Run tutorial
+      run: |
+        pip install -U jupyter nbconvert
+        cp tutorials/sparse_gp_tutorial.ipynb tutorial.ipynb
+        jupyter nbconvert --to script tutorial.ipynb
+        sed -i '/^get_ipython()/s/^/# /' tutorial.py
+        sed -i '/^plt/s/^/# /' tutorial.py
+        wget http://quantum-machine.org/gdml/data/npz/md17_aspirin.npz
+        wget https://www.ctcms.nist.gov/potentials/Download/1999--Mishin-Y-Farkas-D-Mehl-M-J-Papaconstantopoulos-D-A--Al/2/Al99.eam.alloy
+        python tutorial.py
+        rm Al* aluminum.txt aspirin.txt md17_aspirin.npz tutorial.ipynb tutorial.py
+
     - name: Install Sphinx and Breathe
       run: |
         sudo apt-get update
         sudo apt-get install python3-sphinx python3-sphinx-rtd-theme python3-breathe python3-nbsphinx
-        
+
     - name: Run Doxygen
       uses: mattnotmitt/[email protected]
       with:
@@ -127,11 +139,12 @@ jobs:
 
     - name: Run Sphinx
       run: |
+        export PYTHONPATH=$PYTHONPATH:$PWD/lammps/python
         cd docs
         pwd
         ls
         make html
-        
+
     - name: Publish the docs
       uses: peaceiris/actions-gh-pages@v3
       with:
@@ -140,23 +153,3 @@ jobs:
         # Change the directory if changes in Doxyfile
         publish_dir: ./docs/build/html
       if: github.event_name == 'pull_request' && matrix.lapack == 'on' && matrix.omp == 'on'
-
-#     - name: Run tutorial
-#       run: |
-#         cd tests
-#         # Download colab notebook
-#         export fileid="18_pTcWM19AUiksaRyCgg9BCpVyw744xv"
-#         wget -O tutorial.ipynb 'https://docs.google.com/uc?export=download&id='${fileid}
-#         # Convert notebook into python script
-#         pip install -U jupyter nbconvert
-#         jupyter nbconvert --to script tutorial.ipynb
-#         # Remove bash commands in the notebook
-#         sed '/!/d' tutorial.txt > tutorial.py
-#         cat test_tutorial.py tutorial.py > tuttest.py
-#         # Download datasets needed for the tutorial
-#         wget http://quantum-machine.org/gdml/data/npz/aspirin_dft.npz
-#         wget https://www.ctcms.nist.gov/potentials/Download/1999--Mishin-Y-Farkas-D-Mehl-M-J-Papaconstantopoulos-D-A--Al/2/Al99.eam.alloy
-#         # Run script
-#         pytest -s tuttest.py
-#         # Remove output files
-#         rm Al* aspirin_dft.npz tutorial.ipynb tuttest.py tutorial.py tutorial.txt

.gitignore

@@ -73,3 +73,7 @@ _C_flare*
 **/xml
 **/dist
 **egg-info
+tutorials/Al*
+tutorials/*txt*
+tutorials/*npz*
+tutorials/*checkpoint*

CMakeLists.txt

@@ -13,6 +13,8 @@ set(CMAKE_CXX_STANDARD 11)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
 
+set(CMAKE_OSX_ARCHITECTURES "x86_64")
+
 set(FLARE_PYTHON_VERSION "" CACHE STRING "Python version to use for compiling modules")
 
 if(NOT CMAKE_BUILD_TYPE)
@@ -84,34 +86,13 @@ include_directories(${SOURCE_DIR})
 
 # pybind11
 ###############################################################################
-ExternalProject_Add(
-    pybind11_project
-    SOURCE_DIR "${CMAKE_BINARY_DIR}/External/pybind11"
-    URL "https://github.com/pybind/pybind11/archive/v2.3.0.tar.gz"
-    URL_HASH MD5=e2120c5b0e3c20a93f2dfcdc55026ba8
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-    INSTALL_COMMAND ""
+set(PYBIND11_FINDPYTHON ON)
+FetchContent_Declare(
+    pybind11
+    GIT_REPOSITORY https://github.com/pybind/pybind11
+    GIT_TAG v2.13.0
 )
-if (NOT FLARE_PYTHON_VERSION)
-    set(Python_ADDITIONAL_VERSIONS 3.8 3.7 3.6 3.5)
-endif()
-find_package(PythonLibsNew ${FLARE_PYTHON_VERSION} REQUIRED)
-
-add_library(pybind11 INTERFACE)
-ExternalProject_Get_Property(pybind11_project SOURCE_DIR)
-target_include_directories(pybind11 SYSTEM INTERFACE ${SOURCE_DIR}/include)
-target_include_directories(pybind11 SYSTEM INTERFACE ${PYTHON_INCLUDE_DIRS})
-
-if(APPLE)
-      TARGET_LINK_LIBRARIES(pybind11 INTERFACE "-undefined dynamic_lookup")
-      message(STATUS "Building in conda environment on MAC")
-else()
-      target_link_libraries(pybind11 INTERFACE ${PYTHON_LIBRARIES})
-endif()
-
-# Greatly reduces the code bloat
-target_compile_options(pybind11 INTERFACE "-fvisibility=hidden")
+FetchContent_MakeAvailable(pybind11)
 ###############################################################################
 
 # Specify source files.
@@ -123,6 +104,7 @@ set(FLARE_SOURCES
     src/flare_pp/bffs/sparse_gp.cpp
     src/flare_pp/bffs/gp.cpp
     src/flare_pp/descriptors/descriptor.cpp
+    src/flare_pp/descriptors/b1.cpp
     src/flare_pp/descriptors/b2.cpp
     src/flare_pp/descriptors/b2_norm.cpp
     src/flare_pp/descriptors/b2_simple.cpp
@@ -204,11 +186,8 @@ else()
 endif()
 
 # Create pybind module.
-add_library(flare_module SHARED ${PYBIND_SOURCES})
-target_link_libraries(flare_module PUBLIC flare pybind11)
-set_target_properties(flare_module PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
-                                              SUFFIX "${PYTHON_MODULE_EXTENSION}")
-add_dependencies(flare_module pybind11_project)
+pybind11_add_module(flare_module ${PYBIND_SOURCES})
+target_link_libraries(flare_module PUBLIC flare)
 set_target_properties(flare_module PROPERTIES OUTPUT_NAME "_C_flare")
 
 # Add test directory.

README.md

@@ -1,54 +1,40 @@
 [![Build Status](https://github.com/mir-group/flare/actions/workflows/flare.yml/badge.svg)](https://github.com/mir-group/flare/actions) [![pypi](https://img.shields.io/pypi/v/mir-flare)](https://pypi.org/project/mir-flare/) [![activity](https://img.shields.io/github/commit-activity/m/mir-group/flare)](https://github.com/mir-group/flare/commits/master) [![codecov](https://codecov.io/gh/mir-group/flare/branch/master/graph/badge.svg)](https://codecov.io/gh/mir-group/flare)
 
-***NOTE: This is the latest release [1.3.3](https://github.com/mir-group/flare/releases/tag/1.3.3) which includes significant changes compared to the previous version [0.2.4](https://github.com/mir-group/flare/releases/tag/0.2.4). Please check the updated tutorials and documentations from the links below.***
-
 # FLARE: Fast Learning of Atomistic Rare Events
 
 <p align="center">
   <img width="527" height="242" src="https://github.com/mir-group/flare/blob/master/docs/images/Flare_logo.png?raw=true">
 </p>
 
-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
 
 <p align="center">
   <img src="https://github.com/mir-group/flare/blob/development/docs/images/Flare_features.jpg?raw=true">
 </p>
 
-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. 
-
-Please do NOT mix them.
-
 ## Documentations and Tutorials
 
 Documentation of the code can be accessed here: https://mir-group.github.io/flare
 
 [Applications using FLARE and gallery](https://mir-group.github.io/flare/related.html)
 
-### Google Colab Tutorials
+### Tutorials
 
-[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 (ACE descriptors + sparse GP)](https://github.com/mir-group/flare/blob/master/tutorials/sparse_gp_tutorial.ipynb)
+This tutorial shows how to run flare with a sparse Gaussian process model trained on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field.
+
+[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).
@@ -82,7 +68,7 @@ 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&mdash;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.
 
@@ -94,11 +80,10 @@ pytest
 ```
 
 ## References
-
 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.* Nature Communications 13.1 (2022): 5183. https://www.nature.com/articles/s41467-022-32294-0
-  
+
 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

ctests/test_descriptor.cpp

@@ -1,4 +1,3 @@
-#include "b3.h"
 #include "descriptor.h"
 #include "test_structure.h"
 #include "gtest/gtest.h"
@@ -43,7 +42,7 @@ class DescTest : public StructureTest {
   }
 };
 
-using DescTypes = ::testing::Types<B2, B3>;
+using DescTypes = ::testing::Types<B1, B2, B3>;
 TYPED_TEST_SUITE(DescTest, DescTypes);
 
 //TEST_P(DescRotTest, RotationTest) {

ctests/test_structure.h

@@ -1,3 +1,4 @@
+#include "b1.h"
 #include "b2.h"
 #include "b2_norm.h"
 #include "b2_simple.h"

docs/source/conf.py

@@ -55,6 +55,8 @@
 breathe_projects = {"flare_pp": "xml"}
 breathe_default_project = "flare_pp"
 breathe_default_members = ("members", "undoc-members")
+napoleon_google_docstring = True
+napoleon_numpy_docstring = True
 napoleon_use_param = False
 
 # Add any paths that contain templates here, relative to this directory.

docs/source/flare/learners/learners.rst

@@ -5,5 +5,6 @@ Bayesian Active Learning
    :maxdepth: 2
 
    otf
+   lmpotf
    gp_from_aimd
    utils

docs/source/flare/learners/lmpotf.rst

@@ -0,0 +1,5 @@
+On-the-Fly Training in LAMMPS
+=============================
+
+.. automodule:: flare.learners.lmpotf
+    :members:

docs/source/tutorials/colabs.rst

@@ -1,20 +1,23 @@
 FLARE: Active Learning Bayesian Force Fields
 ============================================
 
-We have a few Google Colab tutorials that you can check out and play with.
+We have a few tutorial notebooks that you can check out and play with.
 
-`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 (ACE descriptors + sparse GP) <https://github.com/mir-group/flare/blob/master/tutorials/sparse_gp_tutorial.ipynb>`_.
+This tutorial shows how to run flare with a sparse Gaussian process model trained on energy and force data, demoing "offline" training on the MD17 dataset and "online" on-the-fly training of a simple aluminum force field.
 
 `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 (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 (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.
+.. `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, 

examples/test_SGP_Fake_fresh.yaml

@@ -27,8 +27,8 @@ flare_calc:
         - 14
         - 6
     single_atom_energies:
-        - 0
-        - 0
+        - -7.4
+        - -8.9
     cutoff: 4.0
     variance_type: local
     max_iterations: 1

examples/test_SGP_LMP_fresh.yaml

@@ -26,8 +26,8 @@ flare_calc:
         - 14
         - 6
     single_atom_energies:
-        - 0
-        - 0
+        - -7.4
+        - -8.9
     cutoff: 4.0
     variance_type: local
     max_iterations: 1

flare/_version.py

@@ -1 +1 @@
-__version__ = "1.3.3"
+__version__ = "1.4.2"

flare/bffs/sgp/sparse_gp.py

@@ -288,12 +288,18 @@ def from_dict(in_dict):
             rel_efs_noise = train_struc.info.get("rel_efs_noise", [1, 1, 1])
             rel_e_noise, rel_f_noise, rel_s_noise = rel_efs_noise
 
+            # check for stress training since ASE atoms assert stress arrays of length 6
+            if not in_dict["stress_training"]:
+                struc_stress = np.array(None)
+            else:
+                struc_stress = train_struc.stress
+
             gp.update_db(
                 train_struc,
                 train_struc.forces,
                 custom_range=custom_range,
                 energy=energy,
-                stress=train_struc.stress,
+                stress=struc_stress,
                 mode="specific",
                 sgp=None,
                 update_qr=False,
@@ -516,6 +522,7 @@ def duplicate(self, new_hyps=None, new_kernels=None, new_powers=None):
 
         n_kern = len(kernels)
         new_gp = SparseGP(kernels, hyps[n_kern], hyps[n_kern + 1], hyps[n_kern + 2])
+        new_gp.Kuu_jitter = self.sparse_gp.Kuu_jitter
 
         # add training data
         sparse_indices = self.sparse_gp.sparse_indices

flare/io/output.py

@@ -214,7 +214,7 @@ def write_md_header(self, dt, curr_step, dft_step):
         else:
             string += f"\n*-Frame: {curr_step} "
 
-        string += f"\nSimulation Time: {(dt * curr_step):10.3f} ps \n"
+        string += f"\nSimulation Time: {(dt * curr_step):10.4f} ps \n"
         return string
 
     def write_md_config(