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Related Concept Videos

Introduction to Structures01:30

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A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Related Experiment Video

Updated: Jun 12, 2025

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
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Beyond theory-driven discovery: introducing hot random search and datum-derived structures.

Chris J Pickard1,2

  • 1Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, UK. cjp20@cam.ac.uk.

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|September 19, 2024
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Summary
This summary is machine-generated.

New machine-learning methods accelerate materials discovery by enhancing random structure searching. These approaches, including hot AIRSS and EDDPs, efficiently find complex low-energy structures for elements like boron and carbon.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Machine-learned interatomic potentials (MLIPs) significantly accelerate computational chemistry.
  • Traditional data-driven methods contrast with theory-driven discovery, such as ab initio random structure searching (AIRSS).

Purpose of the Study:

  • To introduce novel methods combining ML acceleration with theory-driven structure searching.
  • To enhance the efficiency and scope of discovering low-energy material configurations.

Main Methods:

  • Incorporating ephemeral data-derived potentials (EDDPs) into AIRSS for biased sampling.
  • Developing hot AIRSS (hot-AIRSS) to handle more complex systems.
  • Generating candidate structures based on reference structures and actively learned EDDPs.

Main Results:

  • Successfully identified complex boron structures in large unit cells using hot-AIRSS.
  • Generated diverse low-energy carbon structures, including graphite, nanotubes, fullerenes, and tetrahedral frameworks.
  • Recovered the pyrope garnet structure using ML-accelerated searching from a related low-energy configuration.

Conclusions:

  • The developed methods offer significant speedups and enable the exploration of complex chemical spaces.
  • These ML-enhanced techniques provide a powerful framework for materials discovery, complementing existing generative models.