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Related papers: Machine Learning Approaches to Learn HyChem Models

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We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A…

Computational Physics · Physics 2022-01-25 Zheyong Fan , Zezhu Zeng , Cunzhi Zhang , Yanzhou Wang , Haikuan Dong , Yue Chen , Tapio Ala-Nissila

A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics is proposed and validated using high-temperature auto-ignitions, perfectly stirred reactors (PSR), and one-dimensional freely propagating flames of…

Machine Learning · Computer Science 2022-09-09 Zhiwei Wang , Yaoyu Zhang , Enhan Zhao , Yiguang Ju , Weinan E , Zhi-Qin John Xu , Tianhan Zhang

Reaction virtual screening and discovery are fundamental challenges in chemistry and materials science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions. In this work, we propose ChemHGNN, a…

Machine Learning · Computer Science 2025-06-16 Xiaobao Huang , Yihong Ma , Anjali Gurajapu , Jules Schleinitz , Zhichun Guo , Sarah E. Reisman , Nitesh V. Chawla

Knowledge Distillation (KD) has emerged as a promising technique for model compression but faces critical limitations: (1) sensitivity to hyperparameters requiring extensive manual tuning, (2) capacity gap when distilling from very large…

Machine Learning · Computer Science 2025-12-11 Gustavo Coelho Haase , Paulo Henrique Dourado da Silva

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations…

Numerical Analysis · Mathematics 2014-11-19 Elizabeth Skubak Wolf , David F. Anderson

In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable is modeled using various ML algorithms and commonly used…

Computational Physics · Physics 2019-09-04 Marc T. Henry de Frahan , Shashank Yellapantula , Ryan King , Marc S. Day , Ray W. Grout

Coarse-grained modeling in molecular simulations serves not only to extend accessible time and length scales beyond atomistic limits, but also to reduce high-dimensional chemical data to low-dimensional representations that expose the…

Chemical Physics · Physics 2026-05-19 Michael N. Sakano , Alejandro Strachan

Model reduction methods are relevant when the computation time of a full convection-diffusion-reaction simulation based on detailed chemical reaction mechanisms is too large. In this article, we review a model reduction approach based on…

Computational Physics · Physics 2014-05-20 Dirk Lebiedz , Jochen Siehr

Small CNN-based models usually require transferring knowledge from a large model before they are deployed in computationally resource-limited edge devices. Masked image modeling (MIM) methods achieve great success in various visual tasks…

Computer Vision and Pattern Recognition · Computer Science 2024-01-12 Ziming Wang , Shumin Han , Xiaodi Wang , Jing Hao , Xianbin Cao , Baochang Zhang

The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an…

Nuclear Theory · Physics 2020-11-17 Rui Wang , Yu-Gang Ma , R. Wada , Lie-Wen Chen , Wan-Bing He , Huan-Ling Liu , Kai-Jia Sun

Chemistry research has both high material and computational costs to conduct experiments. Institutions thus consider chemical data to be valuable and there have been few efforts to construct large public datasets for machine learning.…

Machine Learning · Computer Science 2022-05-10 Wei Zhu , Jiebo Luo , Andrew White

First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…

Superconductivity · Physics 2023-06-01 Huan Tran , Tuoc N. Vu

Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them…

Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially…

Computational Physics · Physics 2025-12-04 Paul Fuchs , Julija Zavadlav

Machine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data from existing literature introduces issues…

Computational Physics · Physics 2025-03-11 Devi Dutta Biswajeet , Sara Kadkhodaei

Chemical and biomass processing systems release volatile matter compounds into the environment daily. Catalytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine…

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered…

Data Analysis, Statistics and Probability · Physics 2020-06-03 Giles Chatham Strong

Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning…

Materials Science · Physics 2026-02-05 Jane Schlesinger , Simon Hjaltason , Nathan J. Szymanski , Christopher J. Bartel

Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction…

With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion systems. This study established a computational framework by…