17 research outputs found
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is
ideally suited to learn representations for structured data and speed up the
exploration of chemical space. While convolutional neural networks have proven
to be the first choice for images, audio and video data, the atoms in molecules
are not restricted to a grid. Instead, their precise locations contain
essential physical information, that would get lost if discretized. Thus, we
propose to use continuous-filter convolutional layers to be able to model local
correlations without requiring the data to lie on a grid. We apply those layers
in SchNet: a novel deep learning architecture modeling quantum interactions in
molecules. We obtain a joint model for the total energy and interatomic forces
that follows fundamental quantum-chemical principles. This includes
rotationally invariant energy predictions and a smooth, differentiable
potential energy surface. Our architecture achieves state-of-the-art
performance for benchmarks of equilibrium molecules and molecular dynamics
trajectories. Finally, we introduce a more challenging benchmark with chemical
and structural variations that suggests the path for further work
Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler–Parrinello networks as well as the end-to-end model SchNet. Both models obtain predictions of chemical properties by aggregating atom-wise contributions. These latent variables can serve as local explanations of a prediction and are obtained during training without additional cost. Due to their correspondence to well-known chemical concepts such as atomic energies and partial charges, these atom-wise explanations enable insights not only about the model but more importantly about the underlying quantum-chemical regularities. We generalize from atomistic explanations to 3d space, thus obtaining spatially resolved visualizations which further improve interpretability. Finally, we analyze learned embeddings of chemical elements that exhibit a partial ordering that resembles the order of the periodic table. As the examined neural networks show excellent agreement with chemical knowledge, the presented techniques open up new venues for data-driven research in chemistry, physics and materials science.EC/H2020/792572/EU/Machine Learning for Catalytic Carbon Dioxide Activation/MachineCatBMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataEC/H2020/725291/EU/Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments/BeStM
Automatic identification of chemical moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or be learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates
Machine learning of accurate energy-conserving molecular force fields
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big DataDFG, MU 987/20-
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both the
requirements of method development and application of atomistic machine
learning. Version 2.0 comes with an improved data pipeline, modules for
equivariant neural networks as well as a PyTorch implementation of molecular
dynamics. An optional integration with PyTorch Lightning and the Hydra
configuration framework powers a flexible command-line interface. This makes
SchNetPack 2.0 easily extendable with custom code and ready for complex
training task such as generation of 3d molecular structures
Autonomous robotic nanofabrication with reinforcement learning
The ability to handle single molecules as effectively as macroscopic
building-blocks would enable the construction of complex supramolecular
structures inaccessible to self-assembly. The fundamental challenges
obstructing this goal are the uncontrolled variability and poor observability
of atomic-scale conformations. Here, we present a strategy to work around both
obstacles, and demonstrate autonomous robotic nanofabrication by manipulating
single molecules. Our approach employs reinforcement learning (RL), which finds
solution strategies even in the face of large uncertainty and sparse feedback.
We demonstrate the potential of our RL approach by removing molecules
autonomously with a scanning probe microscope from a supramolecular structure
-- an exemplary task of subtractive manufacturing at the nanoscale. Our RL
agent reaches an excellent performance, enabling us to automate a task which
previously had to be performed by a human. We anticipate that our work opens
the way towards autonomous agents for the robotic construction of functional
supramolecular structures with speed, precision and perseverance beyond our
current capabilities.Comment: 3 figure