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Molecule Design by Latent Prompt Transformer [76.2112075557233]
This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
arXiv Detail & Related papers (2024-02-27T03:33:23Z)
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control [54.132297393662654]
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in the training dataset, we often are more concerned with other properties, such as the aesthetic quality of the generated images. We present theoretical and empirical evidence that demonstrates our framework is capable of efficiently generating diverse samples with high genuine rewards.
arXiv Detail & Related papers (2024-02-23T08:54:42Z)
Adaptive Conditional Quantile Neural Processes [9.066817971329899]
Conditional Quantile Neural Processes (CQNPs) are a new member of the neural processes family. We introduce an extension of quantile regression where the model learns to focus on estimating informative quantiles. Experiments with real and synthetic datasets demonstrate substantial improvements in predictive performance.
arXiv Detail & Related papers (2023-05-30T06:19:19Z)
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
We propose a Monte Carlo PDE solver for training unsupervised neural solvers. We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles. Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
arXiv Detail & Related papers (2023-02-10T08:05:19Z)
Generative structured normalizing flow Gaussian processes applied to spectroscopic data [4.0773490083614075]
In the physical sciences, limited training data may not adequately characterize future observed data. It is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity.
arXiv Detail & Related papers (2022-12-14T23:57:46Z)
Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer [0.0]
Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer.
arXiv Detail & Related papers (2022-10-25T07:55:20Z)
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients [0.12352483741564477]
We introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients. SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups.
arXiv Detail & Related papers (2022-09-09T06:21:05Z)
Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
arXiv Detail & Related papers (2021-10-18T14:43:50Z)
Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z)
Large-scale Neural Solvers for Partial Differential Equations [48.7576911714538]
Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. Recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. We examine the applicability of continuous, mesh-free neural solvers for partial differential equations, physics-informed neural networks (PINNs) We discuss the accuracy of GatedPINN with respect to analytical solutions -- as well as state-of-the-art numerical solvers, such as spectral solvers.
arXiv Detail & Related papers (2020-09-08T13:26:51Z)
Learning the Ising Model with Generative Neural Networks [0.0]
We study the representational characteristics of Boltzmann machines (RBMs) and variational autoencoders (VAEs) Our results suggest that the considered RBMs and convolutional VAEs are able to capture the temperature dependence of magnetization, energy, and spin-spin correlations. We also find that convolutional layers in VAEs are important to model spin correlations whereas RBMs achieve similar or even better performances without convolutional filters.
arXiv Detail & Related papers (2020-01-15T15:04:21Z)

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