Search · four archives

Results for multiple kernel function

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

11 papers · ranked by Valyu relevance

01
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
65relevance
02
CchemRxiv28 Aug 2025
An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction
Authors not listed
We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular…
65relevance
03
CchemRxiv28 Dec 2025
Accurate and Robust Analysis of Molecular Kinetics with Random Features
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
64relevance
04
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
63relevance
05
CchemRxiv15 Jul 2022
Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes
Souvik Manna, Diptendu Roy, Sandeep Das, Biswarup Pathak
Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important…
61relevance
06
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
61relevance
07
CchemRxiv11 Nov 2024
Minimum Energy Conical Intersection Optimization Using DFT/MRCI(2)
Authors not listed
The combined density functional theory and multi-reference configuration interaction (DFT/MRCI) method is a semi-empirical electronic structure approach that is both computationally efficient and of predictive accuracy for the calculation of electronic excited states and simulation of electronic spectroscopies.…
60relevance
08
CchemRxiv21 Sep 2023
Machine Learning of Reactive Potentials
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, Olexandr Isayev + 1 more
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological and material sciences. The construction and training of MLPs enables fast and accurate simulations and analysis on thermodynamic and kinetic properties. This review focuses on the applications of…
59relevance
09
CchemRxiv25 Mar 2024
Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
59relevance
10
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
55relevance
11
CchemRxiv25 Nov 2021
Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model
Carsten Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi + 2 more
State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for…
51relevance

Results for multiple kernel function

Krein Support Vector Machine Classification of Antimicrobial Peptides

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Minimum Energy Conical Intersection Optimization Using DFT/MRCI(2)

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model

Krein Support Vector Machine Classification of Antimicrobial Peptides

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Minimum Energy Conical Intersection Optimization Using DFT/MRCI(2)

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model