Cover Image

Data for "Using Structure to Evaluate the Accuracy and Transferability of Machine Learning Potentials of Biomolecules" paper

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Joshi, Nisarg, Biberić, Lejla, Pfaendtner, Jim
Format:	Recurso digital
Published:	Zenodo 2026
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Molecular Driving Forces in the Self‐Association of Silaffin Peptide R5 from MD Simulations
by: Coco M. Mao, et al.
Published: (2024)

Education and Learning in Studies of Nationalism: Anderson and Weber in Focus
by: Lejla Voloder
Published: (2025)

A Modular and Extensible CHARMM-Compatible Model for All-Atom Simulation of Polypeptoids
by: Berlaga, Alex, et al.
Published: (2024)

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
by: Goodwin, Zachary A. H., et al.
Published: (2024)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)

A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations
by: Kang, Christopher, et al.
Published: (2026)

Modelling & Steady State Compliance Testing of an Improved Time Synchronized Phasor Measurement Unit Based on IEEE Standard C37.118.1
by: Trivedi, Nisarg
Published: (2025)

The Complexity of Parenthood in Forced Migration: Risks, Commitments and Dilemmas
by: Lejla Sunagic
Published: (2025)

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
by: Shuang, Fei, et al.
Published: (2025)

Biomolecules
Published: (2013)

Verifying Lock-free Search Structure Templates
by: Patel, Nisarg, et al.
Published: (2024)

Enhancing Financial Data Visualization for Investment Decision-Making
by: Patel, Nisarg, et al.
Published: (2023)

Biomolecules & Biomedicine
Published: (2023)

TASI Lectures on Physics for Machine Learning
by: Halverson, Jim
Published: (2024)

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
by: Eastman, Peter, et al.
Published: (2026)

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
by: Xu, Jiayan, et al.
Published: (2025)

Evaluating Moment Tensor Potential in Ag-Cu Alloy: Accuracy, Transferability, and Phase Diagram Fidelity
by: Nitol, Mashroor S., et al.
Published: (2025)

DeepConf: Machine Learning Conformer Reconstruction of Biomolecules from Scanning Tunneling Microscopy Images
by: Seifert, Tim J., et al.
Published: (2026)

Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)

Extreme Events of Quantum Walks on Graphs
by: Vyas, Nisarg, et al.
Published: (2025)

Uncovering Exceptional Contours in non-Hermitian Hyperbolic Matter
by: Chadha, Nisarg, et al.
Published: (2023)

Vortical currents and reciprocal relations for transport coefficients in the electron hydrodynamic regime
by: Chadha, Nisarg, et al.
Published: (2023)

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
by: Bianchi, Michele Giovanni, et al.
Published: (2025)

Communication Practices of Croatian Scientists.
by: Cabrajec, Lejla, et al.
Published: (1991)

Diagenesis and Geochemistry of Upper Muschelkalk (Triassic) Buildups and Associated Facies in Catalonia (NE Spain): a paper dedicated to Francesc Calvet
by: Jim Marshall
Published: (2004)

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics
by: Andreichev, Valerii, et al.
Published: (2026)

Retracted: Biomolecule Protective and Photocatalytic Potential of Cellulose Supported MoS2/GO Nanocomposite
by: Bioinorganic Chemistry and Applications
Published: (2024)

The Role of Insects in Future Medicine: Diagnostic and Therapeutic Potential of Insect‐Derived Biomolecules and Microbiota
by: Ebrahim Abbasi
Published: (2025)

Radiatively Cooled Binary Mass Transfer: Flow Structure, Luminosities, and L2 Outflows Across Mass Transfer Rates
by: Scherbak, Peter, et al.
Published: (2025)

Efficient and Precise Force Field Optimization for Biomolecules Using DPA-2
by: Chang, Junhan, et al.
Published: (2024)

Peptide‐Carbazolyl Cyanobenzene Conjugates: Enabling Biomolecule Functionalization via Photoredox and Energy Transfer Catalysis
by: Xing‐Yu Liu, et al.
Published: (2025)

Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors
by: Joshi, Pritish, et al.
Published: (2025)

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2025)

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
by: Röcken, Sebastien, et al.
Published: (2025)

Two-dimensional silk
by: Shi, Chenyang, et al.
Published: (2024)

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
by: Zhu, Siya, et al.
Published: (2025)

"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning
by: Gleason, Samuel P., et al.
Published: (2023)

Evaluating the Integration of Spatial Features in Machine Learning for Model Accuracy and Residual Autocorrelation
by: Hyeongmo Koo, et al.
Published: (2026)