Zobrazeno 1 - 10
of 362
pro vyhledávání: '"Gómez‐Bombarelli, Rafael"'
Long-timescale processes pose significant challenges in atomistic simulations, particularly for phenomena such as diffusion and phase transitions. We present a deep reinforcement learning (DRL)-based computational framework, combined with a temporal
Externí odkaz:
http://arxiv.org/abs/2411.17839
Autor:
Liu, Qibang, Cai, Pengfei, Abueidda, Diab, Vyas, Sagar, Koric, Seid, Gomez-Bombarelli, Rafael, Geubelle, Philippe
Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in
Externí odkaz:
http://arxiv.org/abs/2410.17518
Autor:
Subramanian, Akshay, Damewood, James, Nam, Juno, Greenman, Kevin P., Singhal, Avni P., Gómez-Bombarelli, Rafael
Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules hav
Externí odkaz:
http://arxiv.org/abs/2410.08833
Autor:
Subramanian, Akshay, Qu, Shuhui, Park, Cheol Woo, Liu, Sulin, Lee, Janghwan, Gómez-Bombarelli, Rafael
Amorphous molecular solids offer a promising alternative to inorganic semiconductors, owing to their mechanical flexibility and solution processability. The packing structure of these materials plays a crucial role in determining their electronic and
Externí odkaz:
http://arxiv.org/abs/2410.07539
Autor:
Liu, Sulin, Nam, Juno, Campbell, Andrew, Stärk, Hannes, Xu, Yilun, Jaakkola, Tommi, Gómez-Bombarelli, Rafael
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework that separates the
Externí odkaz:
http://arxiv.org/abs/2410.06264
We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submod
Externí odkaz:
http://arxiv.org/abs/2410.01464
Autor:
Peng, Jiayu, Damewood, James, Karaguesian, Jessica, Lunger, Jaclyn R., Gómez-Bombarelli, Rafael
Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energy storage, by predicting properties from structures. Multicomponent ma
Externí odkaz:
http://arxiv.org/abs/2409.13851
Autor:
Nam, Juno, Gómez-Bombarelli, Rafael
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the c
Externí odkaz:
http://arxiv.org/abs/2404.10746
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP). However, the complexity of molecular systems, characterized by in
Externí odkaz:
http://arxiv.org/abs/2402.03753
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an e
Externí odkaz:
http://arxiv.org/abs/2402.01542