Building machine learning potentials (MLPs) for liquid water 
This notebook showcases the usage of PiNN with a practical problem of building machine learning potentials for liquid water on the H2O(l)-revPBE0-D3 dataset. It serves as a basic test and demonstration of the workflow with PiNN.
# Install PiNN
!pip install tensorflow==2.9
!pip install git+https://github.com/Teoroo-CMC/PiNN
Download the H2O(l)-revPBE0-D3 dataset
The H2O(l)-revPBE0-D3 dataset was published by Cheng et al (Proc. Nat. Acad. Sci. 2019, 116, 1110-1115). It comprises 1593 liquid water configurations involving 64 molecules with the energies and forces computed by DFT at the revPBE0-D3 level and presents an excellent example to test MLPs in the interpolation regime.
# step 1: download the dataset
import requests
with requests.get("https://raw.githubusercontent.com/BingqingCheng/ab-initio-thermodynamics-of-water/master/training-set/input.data", stream=True) as r:
r.raise_for_status()
with open("input.data", 'wb') as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
# step 2: create project directory
!mkdir MLP_Water
# step 3: move the dataset file to project directory
!mv input.data MLP_Water
Configure the model architecture and parameters
Three different model architectures are supplied in the PiNN package, i.e., Behler−Parrinello neural network (BPNN, Phys. Rev. Lett. 2007, 98, 146401), PiNet (J. Chem. Inf. Model. 2020, 60, 1184−1193), and PiNet2-P3. The BPNN and PiNet only involve invariant features, whereas PiNet2-P3 incorporates equivariant information as well. Generally speaking, the training loss of PiNet2-P3 is lower than that of BPNN/PiNet. Before training the MLPs, it is essential to define the model architecture and set corresponding parameters. In this case, we will use PiNet2-P3 as an example. You can also use following functions to know which networks and models are available.
import pinn
pinn.get_available_networks()
You can find detailed information about the parameters of both the model and the network on their respective pages under the Usage tab. These sections provide comprehensive guidance on how to configure and implement them effectively for your specific applications. Here is the configuration file for training water model:
import yaml
params = {
"model":
{"name": "potential_model",
"params":
{"e_loss_multiplier": 10.0,
"e_scale": 27.2114,
"e_unit": 27.2114,
"f_loss_multiplier": 100.0,
"log_e_per_atom": True,
"use_e_per_atom": False,
"use_force": True,}},
"network":
{"name": "PiNet2",
"params":
{"atom_types": [1, 8],
"basis_type": "gaussian",
"depth": 5,
"ii_nodes": [16, 16, 16, 16],
"n_basis": 10,
"out_nodes": [16],
"pi_nodes": [16],
"pp_nodes": [16, 16, 16, 16],
"rank": 3,
"rc": 6.0,
"weighted": False}},
"optimizer":
{"class_name": "Adam",
"config":
{"global_clipnorm": 0.01,
"learning_rate":
{"class_name": "ExponentialDecay",
"config":
{"decay_rate": 0.994,
"decay_steps": 100000,
"initial_learning_rate": 5.0e-05,}}}}
}
with open("MLP_Water/pinet2-pot.yml", 'w') as f:
yaml.dump(params, f)
Train the MLP models
import os
import yaml
import warnings
import tensorflow as tf
from pinn import get_model, get_network
from pinn.utils import init_params
from pinn.io import load_runner, write_tfrecord, load_tfrecord, sparse_batch
from tensorflow.python.lib.io.file_io import FileIO
from tempfile import mkdtemp, mkstemp
random_seed = 1 # random seed for data splitting
num_train_steps = int(5e6) # total number of training steps; typical training step is ~5M steps
eval_per_num_steps = 500 # evaluate the models every eval_per_num_steps steps
batch_size = 1 # batch size
nmax_ckpts = 1 # max number of checkpoint file
nckpt_every = 50 # save the checkpoint file every nckpt_every steps
nlog_every = 50 # write the log file every nlog_every steps
num_shuffle_buffer = 1000
bool_shuffle = True
bool_preprocess = True # turn on/off the preprocess of dataset
bool_cache = True
bool_early_stop = False # turn on/off the early stop
# set the GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# turn off the specific warning of tensorflow
index_warning = 'Converting sparse IndexedSlices'
warnings.filterwarnings('ignore', index_warning)
tf.get_logger().setLevel('ERROR')
project_dir = "MLP_Water/"
# split training and test sets
runner_dataset_file = project_dir + "input.data"
dataset = load_runner(runner_dataset_file, splits={'train':8, 'vali':2}, shuffle=bool_shuffle, seed=random_seed)
write_tfrecord(project_dir + 'train.yml', dataset['train'])
write_tfrecord(project_dir + 'vali.yml', dataset['vali'])
# get model parameters
params = {}
with FileIO(project_dir + "pinet2-pot.yml", 'r') as f:
params = yaml.load(f, Loader=yaml.Loader)
molde_dir = project_dir + "PiNet2_rPBE0_seed%d"%(random_seed)
params['model_dir'] = molde_dir
# initial some parameters (e.g., e_dress)
ds = load_tfrecord(project_dir + "train.yml")
init_params(params, ds)
scratch_dir = None
if scratch_dir is not None:
scratch_dir = mkdtemp(prefix='pinn', dir=scratch_dir)
def _dataset_fn(fname):
dataset = load_tfrecord(fname)
if batch_size is not None:
dataset = dataset.apply(sparse_batch(batch_size))
if bool_preprocess:
def pre_fn(tensors):
with tf.name_scope("PRE") as scope:
network = get_network(params['network'])
tensors = network.preprocess(tensors)
return tensors
dataset = dataset.map(pre_fn)
if bool_cache:
if scratch_dir is not None:
cache_dir = mkstemp(dir=scratch_dir)
else:
cache_dir = ''
dataset = dataset.cache(cache_dir)
return dataset
train_fn = lambda: _dataset_fn(project_dir + 'train.yml').repeat().shuffle(num_shuffle_buffer)
eval_fn = lambda: _dataset_fn(project_dir + 'vali.yml')
config = tf.estimator.RunConfig(keep_checkpoint_max=nmax_ckpts,
log_step_count_steps=nlog_every,
save_summary_steps=nlog_every,
save_checkpoints_steps=nckpt_every)
model = get_model(params, config=config)
if bool_early_stop:
early_stop = "loss:1000"
stops = {s.split(':')[0]: float(s.split(':')[1])
for s in early_stop.split(',')}
hooks = [tf.estimator.experimental.stop_if_no_decrease_hook(
model, k, v) for k,v in stops.items()]
else:
hooks=None
train_spec = tf.estimator.TrainSpec(input_fn=train_fn, max_steps=num_train_steps, hooks=hooks)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_fn, steps=eval_per_num_steps)
tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
print('done')
Check the training loss
import numpy as np
def log(log_dir, tag, fmt):
from glob import glob
from sys import stdout
from warnings import warn
from itertools import chain
from tensorboard.backend.event_processing.event_file_loader import LegacyEventFileLoader
files = sorted(glob(f'{log_dir}/events.out.*'), key=os.path.getmtime)
logs = {}
events = chain(*[LegacyEventFileLoader(log).Load() for log in files])
for event in events:
for v in event.summary.value:
if tag not in v.tag:
continue
if v.tag not in logs.keys():
logs[v.tag] = []
logs[v.tag].append([event.step, v.simple_value])
logs = {k: np.array(v) for k,v in logs.items()}
keys = sorted(list(logs.keys()))
steps = [logs[k][:,0] for k in keys]
data = [logs[k][:,1] for k in keys]
steps, rows = np.unique(np.concatenate(steps), return_inverse=True)
return (steps,data,keys)
log_dir_train = molde_dir + "/"
train_loss = log(log_dir_train,'RMSE', '%14.6e ')
log_dir_eval = molde_dir + "/eval"
eval_loss = log(log_dir_eval,'RMSE', '%14.6e ')
print("done")
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
train_steps = train_loss[0]
eval_steps = eval_loss[0]
train_metrics = train_loss[1]
eval_metrics = eval_loss[1]
metric_keys = train_loss[2]
num_metrics = len(metric_keys)
fig, axs = plt.subplots(1, num_metrics, figsize=(20, 4))
metrics_unit={"METRICS/E_PER_ATOM_RMSE":"eV/Atom",
"METRICS/E_RMSE":"eV",
"METRICS/F_RMSE":"eV/Ang"}
for index in range(0, num_metrics):
axs[index].set_xlabel(r'Step')
y_label = "%s(%s)"%(metric_keys[index], metrics_unit[metric_keys[index]])
axs[index].set_ylabel(y_label)
axs[index].set_xlim(0, num_train_steps)
axs[index].plot(train_steps, train_metrics[index], color='black', label="Training")
axs[index].plot(eval_steps, eval_metrics[index], color='red', label="Validation")
axs[index].legend(edgecolor='none', loc='best')
plt.subplots_adjust(hspace=500)
plt.show()
Run molecular dynamics with ASE
! pip install ase
import requests
with requests.get("https://raw.githubusercontent.com/Teoroo-CMC/PiNN/refs/heads/master/docs/notebooks/MLP_Water/water.xyz", stream=True) as r:
r.raise_for_status()
with open("water.xyz", 'wb') as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
! cp water.xyz MLP_Water
import pinn
from ase import units
from ase.io import read,write
from ase.io.trajectory import Trajectory
from ase.md import MDLogger
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.nptberendsen import NPTBerendsen
from ase.md.nvtberendsen import NVTBerendsen
setup = {
'ensemble': 'nvt', # ensemble
'T': 330.0, # temperature
't': 1000, # time in fs
'dt': 0.5, # timestep in fs
'taut': 10, # thermostat damping in steps
'taup': 1000, # barastat dampling in steps
'log-every': 100, # log interval in steps
'pressure': 1, # pressure in bar
'compressibility': 4.57e-4, # compressibility in bar^{-1}
}
ensemble=setup['ensemble']
T = float(setup['T'])
t=float(setup['t'])*units.fs
dt=float(setup['dt'])*units.fs
taut=int(setup['taut'])
taup=int(setup['taup'])
every=int(setup['log-every'])
pressure=float(setup['pressure'])
compressibility=float(setup['compressibility'])
# get calculator
calc = pinn.get_calc(molde_dir + "/params.yml")
# read initial structure
init_structure_file = project_dir + "water.xyz"
atoms = read(init_structure_file)
atoms.set_calculator(calc)
# assign initial velocities (i.e., momenta)
MaxwellBoltzmannDistribution(atoms, T * units.kB)
if ensemble == 'npt':
dyn = NPTBerendsen(atoms, timestep=dt, temperature=T, pressure=pressure,
taut=dt * taut, taup=dt * taup, compressibility=compressibility)
if ensemble == 'nvt':
dyn = NVTBerendsen(atoms, timestep=dt, temperature=T, taut=dt * taut)
log_file = project_dir + "asemd.log"
dyn.attach(
MDLogger(dyn, atoms,log_file,stress=True, mode="w"),
interval=int(50*units.fs/dt))
traj_file = project_dir + "asemd.traj"
dyn.attach(
Trajectory(traj_file, 'w', atoms).write,
interval=int(50*units.fs/dt))
# run simulations
dyn.run(int(t/dt))
print("done")
Analyze the trajectory
Once the trajectory has been acquired, you can perform various analyses based on it, such as calculating the radial distribution function (RDF) for specific pairs of atoms.
import itertools
from ase.io import iread
r_min = 0.0
r_max = 6.0
bin_num = 201
hist = 0
bins = np.linspace(r_min,r_max,bin_num)
r_mid = (bins[1:] + bins[:-1])/2
bin_vol = (bins[1:]**3 - bins[:-1]**3)*4*np.pi/3
from ase.io.trajectory import Trajectory
trajObj = Trajectory(traj_file)
start_fram_index = int(len(trajObj) / 2)
count = 0
traj = iread(traj_file, index=slice(start_fram_index, None))
for data in traj:
count += 1
listIndex1 = [idx for idx in range(len(data)) if data[idx].symbol == "O"]
index_ij = np.array(list(itertools.combinations(listIndex1, 2)))
data.wrap()
diff = data.positions[index_ij[:, 0]] - data.positions[index_ij[:, 1]]
listCellLen = [data.cell[0][0], data.cell[1][1], data.cell[2][2]]
diff = diff - np.rint(diff / listCellLen) * listCellLen
dist = np.linalg.norm(diff, axis=1)
h, edges = np.histogram(dist, bins)
# normalize by the number density
rho = dist.shape[0] / data.get_volume()
hist += h / bin_vol / rho
rdf = hist/count
print("done")
fig, axs = plt.subplots(1, 1, figsize=(4, 4))
axs.set_xlabel(r'$r \, (\mathrm{\AA}$)')
axs.set_ylabel(r'$g_\mathrm{OO}(r)$')
axs.set_xlim(2, 6)
max_value = np.max(rdf)
min_value = np.min(rdf)
axs.set_ylim(min_value, max_value+0.2)
axs.plot(r_mid, rdf, color='black')
plt.subplots_adjust(hspace=500)
plt.show()
Run jobs in batches
When there is a need for batch model training and MD running jobs, incorporating the aforementioned process into a Nextflow script is a good choice.
- Within the Nextflow framework, a pipeline script is composed of several processes that operate independently of each other.
- Each process is capable of defining multiple input and output channels and can be implemented using any scripting language, such as Bash or Python.
- The communication between two processes can be facilitated via their shared channels.
- A channel is capable of sending/receiving a message that may include values, pathes, files and so on.
You can find more information of Nextflow from https://www.nextflow.io/docs/latest/index.html
Here, we illustrate the Nextflow pipeline script of constructing a series of MLPs with varying hyperparameters across different training datasets.
Batch MLP training
Pipeline script
To instruct Nextflow on executing a batch job, it is necessary to create a pipeline script that outlines the workflow, processes and channels. Thus, we need to create a script file (for example, "train.nf") and include the following content within it.
#!/usr/bin/env nextflow
nextflow.enable.dsl=2
// step 1: create a collection of yml files including all hyperparameters of PiNN and store them in a directory named "pinn-adam".
// The format of yml files can be found in the section of "Configure the model architecture and parameters".
// After obtaining these files, we can define a parameter in the context of Nextflow for the convenience of creating channel.
params.inputs = './pinn-adam/*.yml'
// step 2: define other parameters.
params.dataset = './input.data' // path of the dataset
params.seeds = 1 // number of seeds to split the dataset
params.steps = 5000000 // max number of steps
params.batch = 1 // batch size
params.ckpts = 1 // number of ckpts to keep, 0 -> all
params.log_every = 50 // log/summary frequency
params.ckpt_every = 50 // checkpoint frequency
// step 3: create a channel that integrates various yml files, datasets, the number of training iterations, and different random split seeds.
model_config = Channel.fromPath(params.inputs)
.combine(Channel.fromPath(params.dataset))
.combine(Channel.value(params.steps))
.combine(Channel.of(1..params.seeds))
//step 4: define entry points for the entire workflow
workflow {
pinn_train(model_config)
}
def shorten(x) {sprintf('%.5E', (double) x).replaceAll(/\.?0*E/, 'E').replaceAll(/E\+0*/, 'E')}
//step 5: define the training process
process pinn_train {
publishDir 'models/', mode: 'link'
tag "PiNet2-$ds.baseName-$input.baseName-B${params.batch}-${shorten(step)}-$seed"
label 'pinn'
input:
tuple (file(input), file(ds), val(step), val(seed))
output:
path "PiNet2-$ds.baseName-$input.baseName-B${params.batch}-${shorten(step)}-$seed", type:'dir'
"""
# PiNN offers a command line interface for loading datasets and training MLPs
# Certainly, you can use the python source code in the section "Train the MLP models" as an alternative.
pinn convert $ds -o 'train:8,eval:2' --seed $seed
pinn train $input -d "PiNet2-$ds.baseName-$input.baseName-B${params.batch}-${shorten(step)}-$seed"\
--train-steps $params.steps --log-every $params.log_every --ckpt-every $params.ckpt_every\
--batch $params.batch --max-ckpts $params.ckpts --shuffle-buffer 1000 --init
"""
}
Configration file
When training MLPs on various machines, you can create a nextflow.config file to configure the execution settings on all machines. Here is an example for the alvis machine.
profiles {
alvis {
params{
image = 'None'
}
executor{
name = 'slurm'
queueSize = 100
submitRateLimit = '120 min'
}
process {
time = '2d'
accelerator = 1
executor = 'slurm'
withLabel: 'pinn' {
queue = 'alvis'
clusterOptions = '-N 1 -n 1 --gpus-per-node=V100:1 -J revPBE0'
}
}
singularity {
enabled = false
}
}
}
Run script
! nextflow run train.nf -profile alvis -bg > log.out
Collect all MLPs into a new directory
! mkdir Models
! mv ./work/*/*/PiNet2* ./Models/
Batch MD simulation
Once we have acquired a set of MLPs, we can execute a batch of MD simulations by Nextflow. Here, we will use NVT simulations as an example.
Pipeline script
First, we should create a script file nvt.nf and include the following content within it.
#!/usr/bin/env nextflow
nextflow.enable.dsl=2
// step 1: specify the path of all MLPs.
params.model = './Models/PiNet2*/'
// step 2: define other parameters.
params.md_init = "./water.xyz" //initial strucutre of MD
params.md_ps = 200 // length of NVT simulation
// step 3: create a channel that integrates initial strucutre, various MLPs, and different temperatures.
model_config = Channel.fromPath(params.md_init)
.combine(Channel.fromPath(params.model, type: 'dir'))
.combine(Channel.of(250,260,270,300,320))
//step 4: define entry points for the entire workflow
workflow {
pinn_nvt(model_config)
}
//step 5: define the nvt simulation process
process pinn_nvt {
publishDir "mds/$pinn_model.baseName", mode: 'link'
tag "$pinn_model.baseName"
label 'pinn'
input:
tuple (file(md_geo),file(pinn_model),val(temp))
output:
file "NVT-${temp}K-${params.md_ps}ps-${md_geo.simpleName}.log"
file "NVT-${temp}K-${params.md_ps}ps-${md_geo.simpleName}.traj"
"""
#!/usr/bin/env python3
import pinn
import tensorflow as tf
from ase import units
from ase.io import read
from ase.io.trajectory import Trajectory
from ase.md import MDLogger
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.nvtberendsen import NVTBerendsen
dTemp = $temp
calc = pinn.get_calc("$pinn_model")
atoms = read("$md_geo")
atoms.set_calculator(calc)
MaxwellBoltzmannDistribution(atoms, dTemp*units.kB)
dt = 0.5 * units.fs
dyn = NVTBerendsen(atoms, timestep=dt, temperature=dTemp, taut=dt*100)
dyn.attach(
MDLogger(dyn, atoms, 'NVT-${temp}K-${params.md_ps}ps-${md_geo.simpleName}.log',stress=True, mode="w"),
interval=int(100*units.fs/dt))
dyn.attach(
Trajectory('NVT-${temp}K-${params.md_ps}ps-${md_geo.simpleName}.traj', 'w', atoms).write,
interval=int(100*units.fs/dt))
for i in range($params.md_ps):
dyn.run(int(1e3*units.fs/dt))
"""
}
Run script
! nextflow run nvt.nf -profile alvis -bg > log.out