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Related Concept Videos

The Delta-to-Delta Circuit01:17

The Delta-to-Delta Circuit

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In a delta-delta configuration, the source and the load are connected in a delta manner, forming a closed loop that divides the network into three distinct phases. This configuration makes the phase voltages identical to line voltages. Assuming the sources are in positive sequence, the phase voltages can be expressed directly without having a neutral wire.
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The Delta-to-Y Circuit01:16

The Delta-to-Y Circuit

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In the delta-wye circuit, the source is delta-connected, while the load is in a wye configuration. This means that the phase voltage of the delta-connected source is equal to the line voltage of the wye-connected load. The connection between two-line currents originates from the delta-connected source. The phase difference in the balanced system allows for calculating one line current given the other, utilizing the positive sequence of phases. In the delta-wye system, the phase currents in the...
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The Y-to-Delta Circuit01:19

The Y-to-Delta Circuit

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A balanced wye-to-delta circuit comprises balanced Y-connected voltage sources and delta-connected loads with no neutral line connection.
The initial step in analyzing a wye-to-delta circuit is to assume a positive phase sequence. These phase voltages are then utilized to calculate the line voltages that occur directly across the delta-connected load impedances. Van, Vbn, and Vcn are the phase voltages in wye, and Vab, Vbc, and Vca are the line voltages for a delta circuit. The relation between...
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Indeterminate Structure01:18

Indeterminate Structure

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Indeterminate structures refer to structures where internal forces and reactions cannot be determined using only the equations of static equilibrium.  Indeterminate structures have more unknown forces and reaction forces than equations of static equilibrium that can be used to determine them. Indeterminate structures are often used in engineering to create complex, efficient, and aesthetically pleasing structures. There are various types of indeterminate structures used in engineering and...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Active Δ-learning with universal potentials for global structure optimization.

Joe Pitfield1, Mads-Peter Verner Christiansen1, Bjørk Hammer1

  • 1Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark. joepitfield@gmail.com.

Physical Chemistry Chemical Physics : PCCP
|December 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Active learning with Gaussian process regression (GPR) models enhances universal machine learning interatomic potentials (uMLIPs) for global optimization. This approach efficiently identifies global minima in complex material systems.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Universal machine learning interatomic potentials (uMLIPs) offer broad applicability but may require further data for out-of-sample refinement.
  • Global optimization of material structures is crucial for discovering novel properties and functionalities.

Purpose of the Study:

  • To develop and evaluate an active learning scheme for improving uMLIPs in global optimization tasks.
  • To compare the performance of this active learning approach against various global optimization algorithms and foundation models.

Main Methods:

  • Implemented an active learning strategy augmenting foundation models with a Δ-model using Gaussian process regression (GPR) and SOAP descriptors.
  • Evaluated the scheme using silver-sulfur clusters and surface reconstructions on Ag(111) and Ag(100).
  • Compared performance against random structure search, basin hopping, GOFEE, and replica exchange (REX).

    • Main Results:

    • Active learning with GPR-based Δ-models demonstrated robustness in identifying global minima.
    • Replica exchange (REX) proved most efficient in terms of total CPU time.
    • CHGNet, MACE-MP0, and MACE-MPA were compared as foundation models.

    Conclusions:

    • Active learning combined with GPR-based Δ-models is a reliable method for enhancing uMLIPs in global structure prediction.
    • While active learning excels at accuracy, REX offers superior computational efficiency for global optimization tasks.