Search · four archives

Results for Delta Debugging

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

6 papers · ranked by Valyu relevance

01
CchemRxiv11 Aug 2025
A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data
Authors not listed
The accurate prediction of fuel mixture properties is essential for the development of alternative fuels, yet remains challenging under data-scarce conditions due to the combinatorial complexity of multi-component systems. In this study, we present a systematic evaluation of three machine learning (ML)…
59relevance
02
CchemRxiv4 Aug 2021
COVID-19: Attacks Immune Cells and Interferences with Antigen Presentation through MHC-Like Decoy System
liu wenzhong, li hualan
The high mortality of COVID-19 is related to poor antigen presentation and lymphopenia. In this present study, domain search results showed that many proteins of the SARS-COV-2 virus had MHC-like domains, which were similar to decoys for the human immune system. MHC-like structures could bind to MHC receptors of immune…
56relevance
03
CchemRxiv6 Sep 2022
Path integral representation of the Dirac equation and integration order
Han Geurdes
In this paper we ask if there is an interesting twist in mathematics of the Feynman propagator of the present day [1] formalism for the Dirac equation. The case studied is with only a potential function V . If there is no special integration order per step for e.g. dx(0) = dx1dx2dx3 and dp(0) = dp1dp2dp3, then a delta…
55relevance
04
CchemRxiv27 Mar 2023
∆-Learning for Coarse-Grained Potentials
Aditi Khot, Brett Savoie
Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery…
54relevance
05
CchemRxiv16 Apr 2025
Potential energy surfaces: Δ-machine learning from analytical functional forms
Authors not listed
Delta-machine learning (Δ-ML) is a highly cost-effective approach to developing high-level potential energy surfaces (PES) from a large number of low-level configurations. In particular, the high flexibility of the analytical PES-2008 is exploited to efficiently sample points from the low-level data set and, using…
52relevance
06
CchemRxiv13 Dec 2024
Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks
Authors not listed
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this…
51relevance

Results for Delta Debugging

A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data

COVID-19: Attacks Immune Cells and Interferences with Antigen Presentation through MHC-Like Decoy System

Path integral representation of the Dirac equation and integration order

∆-Learning for Coarse-Grained Potentials

Potential energy surfaces: Δ-machine learning from analytical functional forms

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks

A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data

COVID-19: Attacks Immune Cells and Interferences with Antigen Presentation through MHC-Like Decoy System

Path integral representation of the Dirac equation and integration order

∆-Learning for Coarse-Grained Potentials

Potential energy surfaces: Δ-machine learning from analytical functional forms

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks