30 research outputs found
The Extended Parameter Filter
The parameters of temporal models, such as dynamic Bayesian networks, may be
modelled in a Bayesian context as static or atemporal variables that influence
transition probabilities at every time step. Particle filters fail for models
that include such variables, while methods that use Gibbs sampling of parameter
variables may incur a per-sample cost that grows linearly with the length of
the observation sequence. Storvik devised a method for incremental computation
of exact sufficient statistics that, for some cases, reduces the per-sample
cost to a constant. In this paper, we demonstrate a connection between
Storvik's filter and a Kalman filter in parameter space and establish more
general conditions under which Storvik's filter works. Drawing on an analogy to
the extended Kalman filter, we develop and analyze, both theoretically and
experimentally, a Taylor approximation to the parameter posterior that allows
Storvik's method to be applied to a broader class of models. Our experiments on
both synthetic examples and real applications show improvement over existing
methods
Open Source Infrastructure for Differentiable Density Functional Theory
Learning exchange correlation functionals, used in quantum chemistry
calculations, from data has become increasingly important in recent years, but
training such a functional requires sophisticated software infrastructure. For
this reason, we build open source infrastructure to train neural exchange
correlation functionals. We aim to standardize the processing pipeline by
adapting state-of-the-art techniques from work done by multiple groups. We have
open sourced the model in the DeepChem library to provide a platform for
additional research on differentiable quantum chemistry methods
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis
Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
Electrolytes play a critical role in designing next-generation battery
systems, by allowing efficient ion transfer, preventing charge transfer, and
stabilizing electrode-electrolyte interfaces. In this work, we develop a
differentiable geometric deep learning (GDL) model for chemical mixtures,
DiffMix, which is applied in guiding robotic experimentation and optimization
towards fast-charging battery electrolytes. In particular, we extend mixture
thermodynamic and transport laws by creating GDL-learnable physical
coefficients. We evaluate our model with mixture thermodynamics and ion
transport properties, where we show improved prediction accuracy and model
robustness of DiffMix than its purely data-driven variants. Furthermore, with a
robotic experimentation setup, Clio, we improve ionic conductivity of
electrolytes by over 18.8% within 10 experimental steps, via differentiable
optimization built on DiffMix gradients. By combining GDL, mixture physics
laws, and robotic experimentation, DiffMix expands the predictive modeling
methods for chemical mixtures and enables efficient optimization in large
chemical spaces