Fix a bug the periodic boundary conditions in ANI SymmetryFunctions

src/pytorch/SymmetryFunctions.cpp

@@ -119,13 +119,15 @@ class Holder : public torch::CustomClassHolder {
                         for (const float thetas: ShfZ)
                             angularFunctions.push_back({eta, rs, zeta, thetas});
 
+            bool periodic = cellPtr != nullptr;
+
             if (device.is_cpu()) {
-                impl = std::make_shared<CpuANISymmetryFunctions>(numAtoms, numSpecies, Rcr, Rca, false, atomSpecies_, radialFunctions, angularFunctions, true);
+                impl = std::make_shared<CpuANISymmetryFunctions>(numAtoms, numSpecies, Rcr, Rca, periodic, atomSpecies_, radialFunctions, angularFunctions, true);
 #ifdef ENABLE_CUDA
             } else if (device.is_cuda()) {
                 // PyTorch allow to chose GPU with "torch.device", but it doesn't set as the default one.
                 CHECK_CUDA_RESULT(cudaSetDevice(device.index()));
-                impl = std::make_shared<CudaANISymmetryFunctions>(numAtoms, numSpecies, Rcr, Rca, false, atomSpecies_, radialFunctions, angularFunctions, true);
+                impl = std::make_shared<CudaANISymmetryFunctions>(numAtoms, numSpecies, Rcr, Rca, periodic, atomSpecies_, radialFunctions, angularFunctions, true);
 #endif
             } else
                 throw std::runtime_error("Unsupported device: " + device.str());

src/pytorch/TestOptimizedTorchANI.py

@@ -65,6 +65,40 @@ def test_compare_with_native(deviceString, molFile):
     else:
         assert grad_error < 5e-3
 
+@pytest.mark.parametrize('deviceString', ['cpu', 'cuda'])
+def test_compare_waterbox_pbc_with_native(deviceString):
+
+    if deviceString == 'cuda' and not torch.cuda.is_available():
+        pytest.skip('CUDA is not available')
+
+    from NNPOps import OptimizedTorchANI
+
+    device = torch.device(deviceString)
+
+    mol = mdtraj.load(os.path.join(molecules, 'water.pdb'))
+    atomicNumbers = torch.tensor([[atom.element.atomic_number for atom in mol.top.atoms]], device=device)
+    atomicPositions = torch.tensor(mol.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)
+    cell = mol.unitcell_vectors[0]
+    cell = torch.tensor(cell, dtype=torch.float32, device=device)*10.0
+    pbc = torch.tensor([True, True, True], dtype=torch.bool, device=device)
+
+    nnp = torchani.models.ANI2x(periodic_table_index=True).to(device)
+    energy_ref = nnp((atomicNumbers, atomicPositions), cell=cell, pbc=pbc).energies
+    energy_ref.backward()
+    grad_ref = atomicPositions.grad.clone()
+
+    nnp = OptimizedTorchANI(nnp, atomicNumbers).to(device)
+    energy = nnp((atomicNumbers, atomicPositions), cell=cell, pbc=pbc).energies
+    atomicPositions.grad.zero_()
+    energy.backward()
+    grad = atomicPositions.grad.clone()
+
+    energy_error = torch.abs((energy - energy_ref)/energy_ref)
+    grad_error = torch.max(torch.abs((grad - grad_ref)/grad_ref))
+
+    assert energy_error < 5e-7
+    assert grad_error < 7e-3
+
 @pytest.mark.parametrize('deviceString', ['cpu', 'cuda'])
 @pytest.mark.parametrize('molFile', ['1hvj', '1hvk', '2iuz', '3hkw', '3hky', '3lka', '3o99'])
 def test_model_serialization(deviceString, molFile):

src/pytorch/TestSymmetryFunctions.py

@@ -69,6 +69,44 @@ def test_compare_with_native(deviceString, molFile):
     else:
         assert grad_error < 5e-3
 
+
+@pytest.mark.parametrize('deviceString', ['cpu', 'cuda'])
+def test_compare_waterbox_pbc_with_native(deviceString):
+
+    if deviceString == 'cuda' and not torch.cuda.is_available():
+        pytest.skip('CUDA is not available')
+
+    from NNPOps.SymmetryFunctions import TorchANISymmetryFunctions
+
+    device = torch.device(deviceString)
+
+    mol = mdtraj.load(os.path.join(molecules, 'water.pdb'))
+    atomicNumbers = torch.tensor([[atom.element.atomic_number for atom in mol.top.atoms]], device=device)
+    atomicPositions = torch.tensor(mol.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)
+    cell = mol.unitcell_vectors[0]
+    cell = torch.tensor(cell, dtype=torch.float32, device=device)*10.0
+    pbc = torch.tensor([True, True, True], dtype=torch.bool, device=device)
+
+    nnp = torchani.models.ANI2x(periodic_table_index=True).to(device)
+    energy_ref = nnp((atomicNumbers, atomicPositions), cell=cell, pbc=pbc).energies
+    energy_ref.backward()
+    grad_ref = atomicPositions.grad.clone()
+
+    nnp.aev_computer = TorchANISymmetryFunctions(nnp.species_converter, nnp.aev_computer, atomicNumbers)
+    energy = nnp((atomicNumbers, atomicPositions), cell=cell, pbc=pbc).energies
+    atomicPositions.grad.zero_()
+    energy.backward()
+    grad = atomicPositions.grad.clone()
+
+    energy_error = torch.abs((energy - energy_ref)/energy_ref)
+    grad_error = torch.max(torch.abs((grad - grad_ref)/grad_ref))
+
+    assert energy_error < 5e-7
+    assert grad_error < 7e-3
+
+
+
+
 @pytest.mark.parametrize('deviceString', ['cpu', 'cuda'])
 @pytest.mark.parametrize('molFile', ['1hvj', '1hvk', '2iuz', '3hkw', '3hky', '3lka', '3o99'])
 def test_model_serialization(deviceString, molFile):