Portrait de Lena Simine n'est pas disponible

Lena Simine

Alumni

Publications

Path-filtering in path-integral simulations of open quantum systems using GFlowNets
Jeremy Lackman-Mincoff
Moksh Jain
Nikolay Malkin
Yoshua Bengio
Lena Simine
An important class of methods for modeling dynamics in open quantum systems is based on the well-known influence functional (IF) approach to… (voir plus) solving path-integral equations of motion. Within this paradigm, path-filtering schemes based on the removal of IF elements that fall below a certain threshold aim to reduce the effort needed to calculate and store the influence functional, making very challenging simulations possible. A filtering protocol of this type is considered acceptable as long as the simulation remains mathematically stable. This, however, does not guarantee that the approximated dynamics preserve the physics of the simulated process. In this paper, we explore the possibility of training Generative Flow Networks (GFlowNets) to produce filtering protocols while optimizing for mathematical stability and for physical accuracy. Trained using the trajectory balance objective, the model produces sets of paths to be added to a truncated initial set; it is rewarded if the combined set of paths gives rise to solutions in which the trace of the density matrix is conserved, the populations remain real, and the dynamics approach the exact reference. Using a simple two-level system coupled to a dissipative reservoir, we perform proof-of-concept simulations and demonstrate the elegant and surprising filtering solutions proposed by the GFlowNet.
2024-10-07
Journal of Chemical Physics (publié)
doi.org
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Siba Moussa
Michael Kilgour
Clara Jans
Alex Hernandez-Garcia
Miroslava Cuperlovic‐culf
Yoshua Bengio
Lena Simine
2022-08-09
ArXiv (prépublication)
doi.org
arxiv.org
Biological Sequence Design with GFlowNets
Moksh Jain
Emmanuel Bengio
Alex-Hernandez Garcia
Jarrid Rector-Brooks
Bonaventure F. P. Dossou
Chanakya Ekbote
Jie Fu
Tianyu Zhang
Michael Kilgour
Dinghuai Zhang
Lena Simine
Payel Das
Yoshua Bengio
Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several r… (voir plus)ounds of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple stages, with increasing levels of precision and cost of evaluation, where candidates are filtered. This makes the diversity of proposed candidates a key consideration in the ideation phase. In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round. We also propose a scheme to incorporate existing labeled datasets of candidates, in addition to a reward function, to speed up learning in GFlowNets. We present empirical results on several biological sequence design tasks, and we find that our method generates more diverse and novel batches with high scoring candidates compared to existing approaches.
2022-06-27
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D.
Michael Kilgour
Nicolas Gastellu
David Y. T. Hui
Yoshua Bengio
Lena Simine
Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new device… (voir plus)s. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size. We present a method based on deep learning that leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples. We benchmark performance on self-assembled nanoparticle aggregates and proceed to simulate monolayer amorphous carbon with atomistic resolution. This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems.
2020-09-23
Journal of Physical Chemistry Letters (publié)
doi.org