The latest batch of files released by the Department of Justice have conclusively proven the Trump administration is lying and engaged in a massive cover-up.

**Abstract:** This paper introduces a novel computational framework, Operator Neural Network – Reduced-Order Modeling (ONN-ROM), for accelerating pore-scale multiphase flow simulations crucial for Enhanced Oil Recovery (EOR) within nano-porous media. Existing conventional Lattice Boltzmann Method (LBM) simulations are computationally prohibitive, limiting their application to large-scale reservoir modeling and optimization. ONN-ROM leverages the strengths of both neural network acceleration and dimensionality reduction to achieve a 10-20x speedup while maintaining high accuracy in predicting dynamic wetting behavior and displacement patterns necessary for optimizing EOR strategies in nano-porous formations.

**Abstract:** This paper introduces a novel computational framework for simulating reaction rate distributions within nanoporous materials, specifically addressing challenges arising from non-Markovian diffusion processes. Conventional kinetic Monte Carlo (KMC) methods often struggle to accurately model reaction kinetics in confined geometries where diffusion is highly non-Markovian due to strong steric interactions and complex pore networks. Our approach, termed Stochastic Kinetic Monte Carlo with Non-Markovian Diffusion (SKMC-NMD), couples a modified KMC algorithm with a non-Markovian random walk process, enabling robust and accurate prediction of spatially varying reaction rates.

Published in Environmental science & technology

Accurate permeability prediction is critical in subsurface applications. This study developed a hybrid machine-learning framework that integrates imag…

Designing catalytic processes in porous reactors requires resolving coupled multiscale reaction–transport phenomena. We develop a machine-learning-accelerated pore-scale dual-network model with kinetics (DNMK), which captures reaction kinetics, pore-scale transport, and reactor-level behavior. As a mesoscopic framework, DNMK provides microscopic insight to interpret macroscopic performance, enabling rational design of efficient and scalable heterogeneous multiphase systems. Abstract A pore-scale dual-network model is presented with kinetics (DNMK), enhanced by machine learning (ML), for efficient multiscale modeling of reaction-transport coupled catalytic processes in porous systems. In such systems, apparent catalytic performance arises from the intricate interplay between intrinsic microkinetics and inter-particle transport phenomena. By explicitly resolving these coupled effects, DNMK provides mechanistic insight into how spatial particle arrangements and transport limitations govern overall reactor performance. A key innovation of this work is the integration of ML-based surrogates to accelerate the microkinetic module, effectively bridging the large spatial and temporal scale mismatches between transport and catalytic reactions. This hybrid approach achieves up to a 750-fold computational speed-up while preserving full physical and chemical fidelity. The framework is demonstrated for sorption-enhanced CO 2 hydrogenation to methanol, where DNMK identifies optimal catalyst-sorbent configurations that maximize apparent activity and reactor-scale performance. More broadly, DNMK establishes a high-resolution, ML-driven platform for digital catalytic experimentation, enabling predictive, in silico optimization of catalyst scaling, utilization, and process intensification. By allowing rapid, physically consistent evaluation of complex catalytic systems, DNMK reduces reliance on costly experimental trials and opens new pathways for data-driven reactor and process design across diverse chemical engineering applications.