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Related Concept Videos

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Related Experiment Video

Updated: Jun 23, 2026

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations
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Published on: April 26, 2024

Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.

Nicolas H Wong1, Julia H Yang1

  • 1Department of Chemical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30363, United States.

Journal of Chemical Theory and Computation
|June 20, 2026
PubMed
Summary
This summary is machine-generated.

Fine-tuning universal machine-learned interatomic potentials (uMLIPs) is crucial for accuracy. Iterative fine-tuning, unlike naive methods, prevents model bias and ensures stable molecular dynamics simulations.

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Last Updated: Jun 23, 2026

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations
05:57

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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Universal machine-learned interatomic potentials (uMLIPs) show promise for materials discovery due to their broad applicability.
  • Current uMLIPs achieve low error on benchmark datasets but struggle with out-of-domain predictions.

Purpose of the Study:

  • To investigate the impact of fine-tuning strategies on uMLIP performance for molecular dynamics (MD).
  • To identify and mitigate model biases introduced during the fine-tuning process.

Main Methods:

  • Comparison of two fine-tuning approaches: naive (parallel MD trajectories) and iterative (sequential fine-tuning).
  • Analysis of data generation strategies and their influence on model generalizability.
  • Evaluation of model performance using MD simulations and Q-residual analysis for uncertainty quantification.

Main Results:

  • Naive fine-tuning leads to constrained datasets, hindering MD simulation accuracy and extrapolation.
  • Iterative fine-tuning produces more generalizable and accurate models, enabling stable MD simulations.
  • Principal component analysis and Q-residual analysis help identify unphysical behavior and quantify uncertainty.

Conclusions:

  • Iterative fine-tuning is essential for improving uMLIP accuracy and reliability in MD simulations.
  • Understanding and addressing uMLIP bias during fine-tuning is critical for robust predictions.
  • Q-residual analysis serves as a valuable proxy for epistemic uncertainty in large-scale simulations.