Day-2

Instruction for day 2 of the tutorial

July 14 @ Princeton CSI center

Tutors: Marcos Calegari Andrade (Princeton CHEM), Yixiao Chen (Princeton PACM), Linfeng Zhang (Princeton PACM)

Aims

1. Train a deep neural network potential that can properly describe rare events in molecular dynamics (MD) simulations;
2. Use enhanced sampling techniques during the exploration steps of active learning.

Pre-requisites

In order to run this tutorial, please make sure you have the following codes compiled:

1. DeepMD-kit (with tensorflow version 1.14.0);
2. Lammps interfaced with DeepMD-kit and Plumed;
3. PWscf code of Quantum-ESPRESSO.

Please write the path to pw.x and lammps executable at path_to_codes. No need to compile these codes or to change the paths if you're running the tutorial on GoldVM.

Quick start

You can run one DP training iteration with:

./run_one_dp_iteration.sh &> log &

We describe below what quatities you should look during the active learning cycle.

While the code is running, take a look at run_one_dp_iteration.sh and the log file it produces. Try to understand each step the code is executing. Not every system will use exactly the same parameters used in this tutorial, so it is important that you understand what we do here before running enhanced sampling to train DP models for a different system.

Introduction and Motivation

As shown in the first hands-on tutorial, DP-Gen builds a robust training data for deep neural network potentials based on an iterative scheme. This scheme consists of: (1) exploration, (2) labeling and (3) training.

However, the type of exploration is critical to define the accuracy of the DNN potentials over a range of physical events of interest. Transition states, for instance, are rarely sampled from conventional molecular dynamics (MD) simulations. An exploration scheme based only on conventional MD would thus miss configurations at the transition state, producing a DP potential with low-prediction accuracy for transition states.

In this tutorial, we use enhanced sampling techniques to increase the probability of a system to visit rarely sampled configurations. Our system is composed of a single ethane (C2H6) in vacuum. The rare event in this system consists of the H-C-C-H dihedral rotation.

Our aim is to perform 3 active-learning iterations in 3 h 30 min. No attempt to obtain a fully converged potential is made. The tutorial should provide a consistent improvement of the DNN potential with the number of iterations.

For the sake of time, we already provide you an initial set of 3 potentials at 03-train/iter.000/?/graph?.pb ("?" correspond to integers 1,2 or 3). The training data used to build these potentials have no atomic configurations close to the transition state. See 03-train/iter.000/raw_files/README.md for more details of the atomic configurations used to train these potentials.

You can easily perform one iteration cycle running ./run_one_dp_iteration.sh &> log & in the current folder. This script will sweep over the 3 steps of active learning training. Below, we give instruction on what quantities we should look during the training. Take a look at the log file to see some recommendations on what you should plot during each iteration.

We provide all the outputs of 3 active learning iterations at ./benchmark.

Exploration

This is the first step of active learning. We use Lammps interfaced with Plumed to perform enhanced sampling simulations using the DNN potential. At this stage, we should mainly focus on two files: COLVAR and model_devi.out. COLVAR contains outputs from Plumed. In this file, we can see the time evolution of our collective variable (H-C-C-H dihedral). This collective variable goes from -Pi to +Pi, and our enhanced sampling can explore the full rotation of the H-C-C-H dihedral. This is not the case of conventional MD. You can find the time evolution of the H-C-C-H dihedral on a ~200 ps simulation without enhanced sampling at benchmark/01-explore/00-test-without-enhanced-sampling/COLVAR.

To plot the time evolution of the H-C-C-H dihedral (iteration 1), do

cd 01-explore/iter.001/
gnuplot ../codes/plot_colvar.gnu
cd ../../

model_devi.out contains the deviation of energy and forces predicted from 3 different DNN potentials. As in the first-day tutorial, these potentials are trained with the same training data. They differ only by the random initialization of the DNN parameters. We here use the model deviation as a fast error estimatior of energy and forces. As the iterations go, you should compare how the model deviation change as a function of the number of completed iterations.

To plot the time evolution of the maximum model deviation in forces (iteration 1), do

cd 01-explore/iter.001/
gnuplot ../codes/plot_model_devi.gnu
cd ../../

At the end of the exploration, we will use the model deviation to select new atomic configurations to retrain. Only configurations with high maximum deviation in atomic forces will be selected for labeling.

If you're interested, you can also compute the free energy surface for this system at the end of each exploration step. Please read 01-explore/README for more details.

Labeling

Now that we selected a set of atomic configurations from the exploration step, we should label these configuration. Labeling consists of evaluation of atomic forces and energy using first-principles methods. We use the PBE functional, as implemented in the PW code of Quantum-ESPRESSO, to evaluate first-principles energy and forces. At the end of this step, raw files will be extracted from PW outputs and later appended to the existing DP training data.

Training

This is the last step of one active learning iteration. We will use the old and the new training data collected from labeling to train an improved DNN potential.

Our DNN potential is slightly different from the one used in the first-day. We use the local-frame descriptors, instead of the smooth descriptors, for computational efficiency.

During training, you should look at lcurve.out. This file contains the fitting error of the DNN as a function of the number of training steps. Use gnuplot to plot the training and testing error forces for one the potentials in folders 1, 2 or 3 in 03-train/iter.00?. For example, you could plot the learning curve of the first iteration with:

cd 03-train/iter.001/1
gnuplot ../../codes/plot_lcurve.gnu
cd ../../../