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Related Concept Videos

Strain and Elastic Modulus01:15

Strain and Elastic Modulus

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The quantity that describes the deformation of a body under stress is known as strain. Strain is given as a fractional change in either length, volume, or geometry under tensile, volume (also known as bulk), or shear stress, respectively, and is a dimensionless quantity. The strain experienced by a body under tensile or compressive stress is called tensile or compressive strain, respectively. In contrast, the strain experienced under bulk stress and shear stress is known as volume and shear...
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Elastic Strain Energy for Normal Stresses01:22

Elastic Strain Energy for Normal Stresses

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Strain energy quantifies the energy stored within a material due to deformation under loading conditions, a fundamental concept in materials science and engineering. The strain energy can be modeled when a material is subjected to axial loading with uniformly distributed stress. In this scenario, the stress experienced by the material is the internal force divided by the cross-sectional area, and the strain induced is directly proportional to this stress through the modulus of elasticity.
If...
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Elastic Strain Energy for Shearing Stresses01:20

Elastic Strain Energy for Shearing Stresses

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As discussed in previous lessons, strain energy in a material is the energy stored when it is elastically deformed, a concept crucial in materials science and mechanical engineering. This energy results from the internal work done against the cohesive forces within the material. When a material undergoes shearing stress and corresponding shearing strain, the strain energy density, which is the energy stored per unit volume, is calculated. Within the elastic limit, where the stress is...
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Elasticity01:12

Elasticity

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Elasticity is the ability of an object to withstand the effects of distortion and to return to its original size and shape once the forces causing deformation are removed. When an elastic material deforms under the action of an external force, it experiences internal resistance to the deformation. However, if no external force is applied, it returns to its original state.
The elasticity of an object can be described by a stress-strain curve, which represents the relationship between stress...
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Elasticity in Concrete01:20

Elasticity in Concrete

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Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...
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Deep elastic strain engineering of bandgap through machine learning.

Zhe Shi1,2, Evgenii Tsymbalov3, Ming Dao1

  • 1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America
|February 17, 2019
PubMed
Summary

Deep elastic strain engineering, guided by AI and ab initio calculations, enables precise control over semiconductor properties. This method optimizes silicon and diamond for advanced electronic and photonic applications by exploring material band structures.

Keywords:
bandgap engineeringelectronic band structurefirst-principles calculationneural networksemiconductor materials

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Materials Science

Background:

  • Semiconductor materials like silicon and diamond exhibit large elastic strain deformability without inelastic relaxation.
  • This deformability opens avenues for deep elastic strain engineering to tailor electronic band structure and device performance.

Purpose of the Study:

  • To present a general method combining machine learning and ab initio calculations for guiding strain engineering.
  • To design material properties and performance by exploring the full six-dimensional space of nonlinear elastic strain.

Main Methods:

  • Utilizing artificial intelligence and a limited dataset of ab initio calculations to train a surrogate model.
  • Predicting electronic bandgap with high accuracy (8 meV) and scanning the entire strain space.

Main Results:

  • Discovered indirect-to-direct bandgap and semiconductor-to-metal transitions in silicon.
  • Identified energy-efficient pathways to reduce diamond's ultrawide bandgap.
  • Demonstrated a framework for tailoring material figures of merit.

Conclusions:

  • Deep elastic strain engineering, powered by machine learning, offers a powerful approach to designing semiconductor properties.
  • This framework has broad applicability in microelectronics, optoelectronics, photonics, and energy technologies.