{ "cells": [ { "cell_type": "markdown", "id": "88a4b6e6", "metadata": {}, "source": [ "# Tutorial 3 – Training Nonadiabatic Couplings\n", "\n", "\n", "When focusing solely on the ground state, machine learning force fields are usually created by learning the energies and forces (energy gradients) associated with it. \n", "However, when developing machine learning force fields for multiple electronic states, encompassing both the ground and excited states, it becomes necessary not only to account for the energies and forces of each specific state, but also to consider the interactions or couplings between these electronic states. \n", "These couplings arise from factors like changes in the dipole moment or geometry.\n", "\n", "## NN Challenges: Taming Tricky Properties\n", "\n", "### The Phase Problem\n", "\n", "The energy of a given electronic state can be derived from its wavefunction $\\Psi_i(\\mathbf{R}$) and the potential energy operator $\\hat{V}$ according to\n", "\n", "\\begin{equation}\n", "E_i = \\left\\langle\\Psi_i(\\mathbf{R}) | \\hat{V} | \\Psi_i(\\mathbf{R}) \\right\\rangle ~. \n", "\\end{equation}\n", "\n", "This approach is applicable for predicting both, energies of ground and excited states.\n", "However, there are additional important properties that emerge when two distinct electronic states are coupled. An example is the transition dipole moment ($\\mathbf{\\mu}_{ij}$), which is influenced by the dipole operator ($\\mu$), expressed as\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathbf{\\mu}_{ij}(\\mathbf{R}) = \\left\\langle \\Psi_{i}(\\mathbf{R}) | \\hat{\\mathbf{\\mu}} | \\Psi_{j}(\\mathbf{R}) \\right\\rangle ~.\n", "\\end{equation}\n", "\n", "Another crucial property, particularly relevant for nonadiabatic molecular dynamics simulations, is the nonadiabatic coupling vector ($\\mathbf{C}_{ij}$), defined as\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathbf{C}_{ij} = \\left\\langle \\Psi_{i}(\\mathbf{R}) | \\nabla_{\\mathbf{R}} \\Psi_{j}(\\mathbf{R}) \\right\\rangle ~.\n", "\\end{equation}\n", "\n", "Wavefunctions ($\\Psi_i$) are subject to phase variations, leading to arbitrary signs. \n", "Consequently, properties related to different states, *i.e.* $\\mathbf{\\mu}_{ij}$ and $\\mathbf{C}_{ij}$, also possess arbitrary signs. \n", "This sign issue can lead neural network predictions to cancel out, as the same input (geometry) might yield opposite sign outputs. To mitigate this numerical challenge, a phase-less loss function is employed for such properties. The phase-less variant of the mean absolute error ($\\mathsf{MAE}$, $n=1$) and mean squared error ($\\mathsf{MSE}$, $n=2$) are defined as\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathcal{L}_p = \\frac{1}{3N} \\sum_{k}^{N_{c}} \\min_i\\left( \\sum_{l,m} \\left| Q_k^{ref} 1_2^{\\top} - Q_k^{NN} 1_2^{\\top} \\odot \\begin{pmatrix}\n", " 1 \\\\ -1 \n", " \\end{pmatrix}^{\\top}~\\right|_{lm}^n \\right) ~,\n", "\\end{equation}\n", "\n", "where $Q^{NN}$ represents the predicted and $Q^{ref}$ the reference values.\n", "Summation is performed using the indices $k$, $l$ and $m$, whereas the values of $k$ range from 1 to the number of couplings ($N_c$), $l$ ranges from 1 to $N_{atoms}$ and $m$ ranges from 1 to 3 (xyz-direction, vectorial property).\n", "\n", "In simpler terms, the $\\mathsf{MAE}$ and $\\mathsf{MSE}$ can be expressed as\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathsf{MAE} = \\min\\left(|| Q^{ref} - Q^{NN} ||, || Q^{ref} + Q^{NN} || \\right)~\\text{and},\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathsf{MSE} = \\min\\left(|| Q^{ref} - Q^{NN} ||_2, || Q^{ref} + Q^{NN} ||_2 \\right)~.\n", "\\end{equation}\n", "\n", "Notably, for properties like transition dipoles and nonadiabatic couplings, which are vectorial in nature, the '$-$' or '$+$' sign in these equations pertains to the entire vector." ] }, { "cell_type": "markdown", "id": "25f00e51", "metadata": {}, "source": [ "### The singularity problem\n", "\n", "The effects that go beyond the Born-Oppenheimer approximation and nonadiabatic transitions between distinct electronic states are determined by nonadiabatic couplings denoted as $\\mathbf{C}_{ji}$. \n", "These couplings can be expressed in a Hellmann-Feynman-like form, which reads as\n", "\n", "\\begin{equation}\n", " \\mathbf{C}_{ij} = \\left\\langle \\Psi_i(\\mathbf{R}) | \\nabla_{\\mathbf{R}} \\Psi_j(\\mathbf{R}) \\right\\rangle = \\frac{\\left\\langle \\Psi_i(\\mathbf{R}) | \\nabla_{\\mathbf{R}}\\hat{H} | \\Psi_j(\\mathbf{R}) \\right\\rangle}{E_j - E_i} ~~~~~~\\text{with}~ j > i~.\n", "\\end{equation}\n", "\n", "This equation reveals that the nonadiabatic couplings become singular when approaching the intersection of two electronic states, *i.e.*, as $E_j - E_i \\rightarrow 0$. \n", "This singularity poses challenges for neural network (NN) predictions. \n", "To mitigate this, we choose to train the NN with respect to $\\mathbf{C}_{ij}\\left(E_j - E_i\\right)$, which we refer to as smoothed nonadiabatic couplings, denoted as $\\mathbf{C}_{ij}^s$. \n", "These smoothed couplings are well-defined properties and are utilized in this tutorial as part of the database (```smooth_nacs```). \n", "It's worth noting that an alternative approach involves calculating $\\mathbf{C}_{ij}^s$ during training, either using reference energies or NN-predicted energies. \n", "However, this method is computationally more demanding and less robust, especially when relying on NN-predicted energies.\n" ] }, { "cell_type": "markdown", "id": "56cffaa7", "metadata": {}, "source": [ "## Load and Preprocess Data\n", "\n", "To process and prepare data for training, we begin with importing all relevant packages and specifying the location of an existing ASE (Atomic Simulation Envirnonment) database. Please return to the first tutorial to learn how to convert original quantum chemical data into an ASE database." ] }, { "cell_type": "code", "execution_count": 1, "id": "4a46cd29", "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "\n", "from ase.db import connect\n", "\n", "import torch\n", "import torchmetrics\n", "import pytorch_lightning as pl\n", "import schnetpack as spk\n", "\n", "import spainn\n", "import spainn.properties as sp" ] }, { "cell_type": "markdown", "id": "02d44dbd", "metadata": {}, "source": [ "Here, we want to train a model for three electronic states, namely $S_0$, $S_1$, and $S_2$.\n", "Therefore, we train the NN with respect to the energies ($E_i$), forces (-$\\nabla_{\\mathbf{R}}E_i$), and smoothed nonadiabatic couplings ($\\mathbf{C}_{ij}^s$).\n", "In the following way, one can check, if the desired properties, *i.e.* the property keys `energy`, `forces`, and `smooth_nacs` are stored in the databse:" ] }, { "cell_type": "code", "execution_count": 2, "id": "0708bea8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['energy', 'socs', 'forces', 'nacs', 'smooth_nacs', 'dipoles'])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbname = 'spainn_ch2nh2+.db'\n", "datapath = os.path.join(os.getcwd(), 'data', dbname)\n", "db = connect(datapath)\n", "\n", "db.get(1).data.keys()" ] }, { "cell_type": "markdown", "id": "91edb6eb", "metadata": {}, "source": [ "Next the data is loaded form the `spainn_ch2nh2+.db` from the data folder.\n", "For this we use the ``SPAINN`` class that inhertits the ``AtomsDataModule`` class provided by SchNetPack." ] }, { "cell_type": "code", "execution_count": 3, "id": "8125c081", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:00<00:00, 128.19it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'energy': (tensor([-15.7648, -15.7358, -15.7207], dtype=torch.float64), tensor([0.0103, 0.0065, 0.0053], dtype=torch.float64))}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%rm ./train/split.npz\n", "\n", "data_module = sp.SPAINN(\n", " n_states = 3, # singlet states 0, 1, 2\n", " n_nacs = 3, # couplings: 01, 02, 12\n", " datapath=datapath, # path to database\n", " batch_size=2,\n", " num_train=0.7, # 70% of databse for training\n", " num_val=0.1, # 10% of database for validation\n", " split_file=os.path.join('train', 'split.npz'),\n", " splitting=spk.data.splitting.RandomSplit(),\n", " load_properties=['energy', 'forces', 'smooth_nacs'],\n", " transforms=[\n", " # remove mean of energy in every electronic state\n", " spk.transform.RemoveOffsets(sp.SPAINN.energy, remove_mean=True, remove_atomrefs=False),\n", " spk.transform.ASENeighborList(cutoff=5.0),\n", " spk.transform.CastTo32(),\n", " ],\n", ")\n", "\n", "# setup everything\n", "data_module.prepare_data()\n", "data_module.setup()" ] }, { "cell_type": "markdown", "id": "6beb0449", "metadata": {}, "source": [ "To check, if the desired properties, namely `energy`, `forces`, and `smooth_nacs` are loaded to the datamodule, you can access the keys and shapes from the data module in the following way:" ] }, { "cell_type": "code", "execution_count": 4, "id": "9e30d6e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded properties:\n", " energy\ttorch.Size([1, 3])\n", " forces\ttorch.Size([6, 3, 3])\n", " smooth_nacs\ttorch.Size([6, 3, 3])\n", "\n" ] } ], "source": [ "properties = data_module.dataset[0]\n", "print('Loaded properties:\\n', \n", " *[str(i)+'\\t'+str(properties[i].shape)+'\\n' for i in properties.keys() if not i.startswith('_')])\n" ] }, { "cell_type": "markdown", "id": "eff6f93d", "metadata": {}, "source": [ "## Define the Model\n", "\n", "After the data preparation, we now build a model for training the NN PESs. \n", "This is done in three core steps:\n", "\n", "1. Defining input modules (pairwise distance)\n", "2. Building a representation (PaiNN representation)\n", "3. Defining output modules (module for `energy`, `forces` and `smooth_nacs`)\n", "\n", "### Defining Input Modules\n", "\n", "Here we use a paiwise distance as input module and use the PaiNN representation." ] }, { "cell_type": "code", "execution_count": 5, "id": "321b5af9", "metadata": {}, "outputs": [], "source": [ "n_atom_basis = 50\n", "cutoff = 10.0\n", "\n", "# input module: calculates pairwise distances between atoms\n", "pairwise_distance = spk.atomistic.PairwiseDistances()\n", "\n", "# radial basis for convolution\n", "radial_basis = spk.nn.GaussianRBF(n_rbf=20, cutoff=cutoff)" ] }, { "cell_type": "markdown", "id": "ede65f4b", "metadata": {}, "source": [ "### Building a Representation\n", "\n", "In the previous tutorial, we trained the NN with respect to $E_i$ and $-\\nabla_{\\mathbf{R}}E_i$ using the SchNet representation.\n", "Using this representation, allows energy predictions that are invariant to rotations and force predictions that are equivariant to rotations (as we learn the forces from the negative gradient of the energy).\n", "However, SchNet provides nonadiabatic coupling predictions that are invariant to rotations.\n", "However, we would like to represent the atomwise contribution of the nonadiabatic couplings within the chemical environment by equivariant vectors.\n", "For this reason, we rely here on the PaiNN representation, which allows the following predictions with respect to rotation:\n", "\n", "- `energy`: invariant\n", "- `forces`: equivariant\n", "- `smooth_nacs`: equivariant\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "33df7538", "metadata": {}, "outputs": [], "source": [ "# PaiNN Representation\n", "painn = spk.representation.PaiNN(\n", " radial_basis = radial_basis,\n", " n_atom_basis=n_atom_basis,\n", " n_interactions=6,\n", " cutoff_fn=spk.nn.CosineCutoff(cutoff)\n", ")" ] }, { "cell_type": "markdown", "id": "3c75960b", "metadata": {}, "source": [ "### Defining Output Modules\n", "\n", "As we want to model energies, forces and nonadiabatic couplings, we need three output modules. \n", "We will use an `Atomwise` module to predict the energies of the electronic states as implemented in SchNetPack, by adjusting the number of outputs to the number of electronic states, *i.e.*, `n_out=3`.\n", "To describe the forces as derivative of the energy for multiple electronic states, we use the `Forces` module of **SPaiNN**, that computes the negative derivative of the energy for every electronic state with respect to the nuclear positions.\n", "For the nonadiabatic couplings, we use the `Nacs` module of **SPaiNN** with `n_out` being the number of couplings ($N_C$), according to:\n", "\n", "\\begin{equation}\n", "\\displaystyle N_C = \\frac{1}{2} N_{\\text{singlets}} \\left( N_{\\text{singlets}} - 1\\right)\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 7, "id": "d309db60", "metadata": {}, "outputs": [], "source": [ "pred_energy = spainn.model.Atomwise(\n", " n_in=n_atom_basis,\n", " n_out=3, # number of electronic states (S0, S1, S2)\n", " n_layers=3,\n", ")\n", "\n", "pred_forces = spainn.model.Forces()\n", "\n", "pred_nacs = spainn.model.Nacs(\n", " n_in=n_atom_basis,\n", " n_out=3, # number of couplings (C01, C02, C12)\n", " nac_key=sp.SPAINN.smooth_nacs,\n", " use_vector_repr=True # False for SchNet\n", ")" ] }, { "cell_type": "markdown", "id": "030e0ec1", "metadata": {}, "source": [ "In the following the representation as well as input and output modules are assembled to a NN potential. \n", "In this step, we also specify a post-processor, that adds the mean, which we removed as offset in the data pre-processing, to the predictions to obtain energies on the scale of the reference data. \n", "This is done with the `AddMultipleOffsets` post-processor of **SPaiNN**." ] }, { "cell_type": "code", "execution_count": 8, "id": "b86112ae", "metadata": {}, "outputs": [], "source": [ "nnpot = spk.model.NeuralNetworkPotential(\n", " representation=painn,\n", " input_modules=[pairwise_distance],\n", " output_modules=[pred_energy, pred_forces, pred_nacs],\n", " input_dtype_str='float32',\n", " do_postprocessing=True,\n", " postprocessors=[\n", " spk.transform.CastTo64(),\n", " spk.transform.AddOffsets(spainn.SPAINN.energy, add_mean=True, add_atomrefs=False),\n", " ],\n", ")" ] }, { "cell_type": "markdown", "id": "deac0e04", "metadata": {}, "source": [ "## Training the Model\n", "\n", "For training a model, the training task, *i.e.*, model, loss-functions and optimizers have to be defined. First the output of the models are connected to loss functions using the `ModelOutput` class of SchNetPack. Here, the combined loss function reads as:\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathcal{L}(E_{ref},\\mathbf{F}_{ref},\\mathbf{C}_{ref},E_{NN},\\mathbf{F}_{NN},\\mathbf{C}_{NN}) = -\\frac{1}{n_{train}}\\sum_{n=1}^{n_{train}} \\left( \\rho_1 \\mathcal{L}_E + \\rho_2 \\mathcal{L}_F + \\rho_3 \\mathcal{L}_C \\right)\n", "\\end{equation}\n", "\n", "The loss weights $\\rho_k$ are introduced to control the tradeoff between energies, forces and nonadiabatic couplings.\n", "\n", "For predicting energies and forces, we employ the standard mean-square error loss functions, that read as\n", "\\begin{equation}\n", "\\displaystyle \\mathcal{L}_E(E_{ref},E_{NN}) = ||E_{ref} - E_{NN}||_2~,~\\text{and}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathcal{L}_F(\\mathbf{F_{ref}},-\\nabla_{\\mathbf{R}}E_{NN}) = ||\\mathbf{F_{ref}} - \\left( -\\nabla_{\\mathbf{R}}E_{NN}\\right) ||_2~.\n", "\\end{equation}\n", "\n", "For predicting the nonadiabadtic couplings, we employ the phase-less loss-function ```PhaseLoss``` of **SpaiNN**.\n", "As described above, the phase vector of every wavefunction $\\Psi_i(\\mathbf{R})$ has an arbitrary sign and resultingly, the nonadiabatic couplings, will also have an arbitrary sign.\n", "This sign problem will cause NN predictions to collapse to zero, as the same input, *i.e.* geometry can lead to opposite signs of output.\n", "To overcome this numerical bottleneck, we use the ```PhaseLoss``` loss function, which is a phase-less version of the mean absolute error, and can be expressed as\n", "\n", "\\begin{equation}\n", "\\displaystyle \\mathcal{L}_C = \\min\\left(|| \\mathbf{\\hat{C}}_{ij}^{s} - \\mathbf{C}_{ij}^{s} ||, || \\mathbf{\\hat{C}}_{ij}^{s} + \\mathbf{C}_{ij}^{s} || \\right) ~,\n", "\\end{equation}\n", "\n", "where $\\mathbf{\\hat{C}}_{ij}^s$ represents the reference and $\\mathbf{C}_{ij}^s$ the predicted smoothed nonadiabatic couplings.\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "896d01b2", "metadata": {}, "outputs": [], "source": [ "output_energy = spk.task.ModelOutput(\n", " name=spainn.SPAINN.energy,\n", " loss_fn=torch.nn.MSELoss(),\n", " loss_weight=1.0, #0.05,\n", " metrics={\n", " \"MSE\": torchmetrics.MeanSquaredError(),\n", " },\n", ")\n", "\n", "output_forces = spk.task.ModelOutput(\n", " name=spainn.SPAINN.forces,\n", " loss_fn=torch.nn.MSELoss(),\n", " loss_weight=1.0, #0.10,\n", " metrics={\n", " \"MSE\": torchmetrics.MeanSquaredError(),\n", " },\n", ")\n", "\n", "output_nacs = spk.task.ModelOutput(\n", " name=sp.SPAINN.smooth_nacs,\n", " # MSE loss function for phase properties \n", " loss_fn=spainn.loss.PhaseLossAtomisticMSE(atoms=6),\n", " loss_weight=1.0, #0.85,\n", " metrics={\n", " \"MSE\": spainn.metric.PhaseAtomisticMSE(atoms=6),\n", " },\n", ")" ] }, { "cell_type": "markdown", "id": "c3ecdfa0", "metadata": {}, "source": [ "In the next step we set up the task and trainer, *i.e.*, a PyTorch Lightning `Trainer` and train for 100 epochs." ] }, { "cell_type": "code", "execution_count": 10, "id": "56f6059b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "\n", " | Name | Type | Params\n", "---------------------------------------------------\n", "0 | model | NeuralNetworkPotential | 202 K \n", "1 | outputs | ModuleList | 0 \n", "---------------------------------------------------\n", "202 K Trainable params\n", "0 Non-trainable params\n", "202 K Total params\n", "0.811 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ccec955ef3344143abe70aeca88eecc7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Validation: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "`Trainer.fit` stopped: `max_epochs=100` reached.\n" ] } ], "source": [ "import warnings; warnings.simplefilter('ignore')\n", "\n", "task = spk.task.AtomisticTask(\n", " model=nnpot,\n", " outputs=[output_energy, output_forces, output_nacs],\n", " optimizer_cls=torch.optim.AdamW,\n", " optimizer_args={\"lr\": 1e-4},\n", " scheduler_monitor=\"val_loss\",\n", ")\n", "\n", "modelpath = os.path.join(os.getcwd(), 'train')\n", "callbacks = [\n", " spk.train.ModelCheckpoint(\n", " model_path=os.path.join(modelpath, \"best_model_E_F_C\"),\n", " save_top_k=1,\n", " monitor=\"val_loss\"\n", " )\n", "]\n", "\n", "trainer = pl.Trainer(\n", " log_every_n_steps=1,\n", " callbacks=callbacks,\n", " logger=pl.loggers.TensorBoardLogger(save_dir=modelpath),\n", " default_root_dir=modelpath,\n", " max_epochs=100, # for testing, we restrict the number of epochs\n", ")\n", "\n", "trainer.fit(task, datamodule=data_module)" ] }, { "cell_type": "markdown", "id": "970ca10e", "metadata": {}, "source": [ "## Using the Model\n", "\n", "To use the trained model, we load the `best_model_E_F_C` file and, *e.g.*, evaluate on the test data." ] }, { "cell_type": "code", "execution_count": 11, "id": "bf29da8a", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import spainn.plotting as sp\n", "\n", "plotter = sp.PlotMAE(\n", " database = datapath,\n", " split_file = os.path.join(os.getcwd(), 'train', 'split.npz'),\n", " model_file = os.path.join(os.getcwd(), 'train', 'best_model_E_F_C'),\n", " cutoff = cutoff,\n", " properties2plot=['energy', 'smooth_nacs'],\n", " subset2plot=['train', 'test'],\n", ")\n", "\n", "plotter.plot()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }