Richard Talks to ChatGPT 4.0 about electrons in a lattice. Would this be a good global competition topic?

Dearest Readers,

I have had hundreds of conversations like this with GPT 4 at OpenAI.  It cannot or will not remember what I say from conversation to conversation, so I have to trick it into “getting in the grove”.  I know these subjects well, since starting electrodynamics more than 50 years ago.  But I am NOT trying to get it to solve anything or even to be “right”. What I am doing is checking its knowledge and guessing where it learned this particular subject.  You will see it has a shallow understanding.  My guess is the raw training data people (I hope they at least formalized the gathering, curation and processing of the raw data, AND kept track of where they got it.)  Elon Musk complained (yesterday) that lots of people were hammering Twitter to use for their AI projects.  The Wikipedia material is horrible on mathematics, science and engineering – they do not number the equations, allow massive fragmented duplication, do not use standard units, do not enforce checking, allow users to put in any constants from anywhere without attributions.  Lots of problems, but those are one reason that an AI like OpenAI GPT 4.0 will pick up mostly garbage for STEMCFGO (Science Technology Engineering Mathematics Computing Finance Other).

I am only running it around to get it to tag onto the most immediately necessary things.  Notice at the end, when I mention AIs, it gets really excited.  I can usually tell what is in the AI raw data, and what might be a biased set of passions and interests of the developers.  The developers are nowhere to be found.  They used closed methods, in spite of the name.

One thing I will recommend (require) is that all AI groups must share their conversations in a global open format. So the results can be combined for global open collaborations on all STEMCFGO and other topics.  “Global Climate Change” and a list of about 15,000 global topics I have found in the last 25 years of the Internet Foundation.  It is possible, with open methods for anyone with a good background to work with AI models from many different groups and disciplines in a seamless fashion. Much of the growth and development is on the boundaries between identifiable disciplines. But I hammer on the assumptions and phenomena and methods in the core of every discipline, and those all yield new insights and possibilities. That is because it is relatively easy to find groups who have dug deeper, have new tools and methods.  Translating those to common form (a global open paradigm, a global open framework) is not hard, just a bit tedious.

The result is effectively “If you can say it clearly, then it is very likely possible.” And “If you put in a few months or years at it, it is possible to sketch out the costs and time needed, find ALL the parts, stakeholders and global implications, and move forward”. With groups, or assigning AIs with different backgrounds and training to brainstorm at AI speed, many things can be covered exhaustively AND remembered, that human groups simple cannot do.  Human brains do some things well, and those specifications can be written down, just as you would write the specification for any processor.  It is statistical, but as a Senior Mathematical Statistician working 5 decades on global problems of the greatest complexity, nothing fazes me. And when groups learn they simply need to keep at something until their brain absorbs it and starts to find the deeper connections, then “Anything you can say clearly, is likely to be possible”.  A bit deeper than “name it and claim it” More alone “name it and the computer can find all the pieces and show them to use and use it for whatever you might want to do. Even when “you” is a hundred million people of all ages, in every country of the world, wanting “solar system colonization”, “every human allowed full lives with dignity and purpose”.  And a whole lot of fun.

It is nearly impossible to make perfect crystal copper wires, the grain is all over the place. And modeling from atomic level and Avogrado’s numbers defeats the biggest computers. But the nanoparticle (and cluster and sheet and polyatomic chain) groups can make and then grade the material to have very uniform properties.  I expect that conductors made of packed nanostructures, when made in the vacuum of space and vibration isolated micro-gravity – will be able to conduct very specific signals at high power densities. One of the biggest problems in power systems is they are still mostly trying to use low frequencies, off-the-shelf materials, and NO measurements or models.  I know there are many exceptions, but I am looking at all countries and all humans.  There are lots of ways to make improvements. When I tackle it by my own memories and skills, I simply throw thousands of possible pathways in my brain, and it sort out, then I dig on the most likely. But global collaborations of AIs (groups of AIs with mixes of skills) and groups of humans (mixes of skills) and human-AI partners for life (intellectual I am sure) can work out all that is possible, and use all the data available, and test and experiment – in groups of tens of millions if needed.

This generates a LOT of “education by work” experiences, and lots of experiences for creative humans. Since it is all recorded, no way the boss steals the workers ideas. Everyone works with the network and everything recorded and auditable.  I say, “Let’s do superconductivity”, “Lets do resonant transmission of energy and data”

Richard Collins, The Internet Foundation

There is an equation with the rate of change of the electric field creating a magnetic flux.

What you’re referring to is an essential principle in electromagnetic theory, which is most thoroughly described by Maxwell’s Equations. The equation that relates the rate of change of the electric field to the creation of a magnetic field is Faraday’s Law of Electromagnetic Induction, one of the four Maxwell’s equations.

In integral form, Faraday’s law is given by:

∮E·dl = -dΦ_B/dt

In this equation:

  • ∮E·dl is the line integral of the electric field E around a closed loop.
  • dΦ_B/dt is the rate of change of the magnetic flux Φ_B through the surface bounded by the loop.

The negative sign indicates that the induced electromotive force (EMF) and, hence, the electric field, is in such a direction as to oppose the change in magnetic flux, a concept known as Lenz’s Law.

This law is a fundamental principle behind many technological applications, such as transformers, electric generators, and induction cooktops. It is also key to understanding many phenomena in physics, such as electromagnetic waves.

However, note that the relationship is not simply that a changing electric field creates a magnetic field. That’s actually captured by Ampère’s law with Maxwell’s addition, which relates the curl of the magnetic field to the electric current and the rate of change of the electric field.

No. It has dE/dt

I see, you’re referring to the Maxwell–Faraday equation in its differential form, which is a version of Faraday’s Law of Electromagnetic Induction. This form of the equation relates the time-varying electric field to a changing magnetic field.

The differential form of Faraday’s Law is:

∇ × E = – ∂B/∂t


  • ∇ × E is the curl of the electric field, E.
  • ∂B/∂t is the partial derivative of the magnetic field, B, with respect to time.

This equation states that a time-varying magnetic field generates an electric field that circulates around it. This is the principle behind electromagnetic induction.

Again, the negative sign indicates the direction of the induced electric field is such that its induced magnetic field opposes the change in the original magnetic field. This is Lenz’s law.

No. Not the curl of the electric field, the time derivative of the electric field.

Ah, I understand now. You’re referring to the second of Maxwell’s equations, also known as Faraday’s Law of induction, but in a context where we’re dealing with electromagnetic waves in free space (or in nonconducting media). In this case, the curl of the magnetic field (B) is equal to the rate of change of the electric field (E). The equation in differential form is:

∇ × B = μ₀ε₀ ∂E/∂t


  • ∇ × B is the curl of the magnetic field
  • μ₀ is the permeability of free space
  • ε₀ is the permittivity of free space
  • ∂E/∂t is the time derivative of the electric field

This equation is typically derived from Ampere’s Law with Maxwell’s addition (which introduces the term for the changing electric field) and is usually used in contexts like the analysis of electromagnetic waves. In such cases, the electric and magnetic fields are changing with time, leading to self-perpetuating “wave” of electric and magnetic fields. The speed of propagation of these waves in free space turns out to be ‘1/sqrt(μ₀ε₀)’, which matches the measured speed of light, leading to the realization that light is an electromagnetic wave.

And for this relation in a material with relative magnetic permeability, and relative dielectric constant, what would that look like?

When you’re working with a medium other than free space, the magnetic permeability (μ) and the electric permittivity (ε) will change based on the properties of that medium.

The permeability (μ) is defined as μ₀μ_r, where μ_r is the relative permeability of the material. Likewise, the permittivity (ε) is defined as ε₀ε_r, where ε_r is the relative permittivity, also known as the dielectric constant.

In such a case, Maxwell’s equation involving the time derivative of the electric field and the curl of the magnetic field will look like:

∇ × B = με ∂E/∂t

This equation holds true in nonmagnetic materials (where μ_r = 1) and also in materials where there is no free charge or current.

So, just like in free space, the propagation of electromagnetic waves in a different medium will be influenced by the medium’s permittivity and permeability, which will change the speed of light in that medium. The speed of light in a material is given by ‘1/sqrt(με)’. As the permittivity and permeability usually increase in a material compared to free space, the speed of light decreases, which is the basis for the phenomenon of refraction.

What is the speed of light in copper and its relative magnetic permeability and relative dielectric constant?

Firstly, it’s important to note that copper is a very good conductor of electricity. In conductive materials, we usually talk about the conductivity rather than the speed of light. This is because when an electromagnetic wave (like light) enters a good conductor (like copper), it is absorbed and quickly dissipates as heat due to the material’s resistance, effectively reducing the penetration depth of the wave.

But to answer your question about the relative permeability (μ_r) and relative permittivity (ε_r), for most practical purposes, copper (and other non-magnetic materials) is treated as having a relative permeability of μ_r = 1 because it is not significantly magnetized by an external magnetic field.

However, the concept of relative permittivity (or dielectric constant) doesn’t really apply to copper, or conductive materials in general. In dielectrics (insulating materials), an applied electric field causes a displacement of charge, and the relative permittivity quantifies how much more charge is displaced in the material compared to a vacuum. But in a conductor like copper, an applied electric field causes the free electrons to move (i.e., a current), not a displacement of charge.

So, speaking of the speed of light in copper isn’t common because electromagnetic waves don’t propagate through copper in the same way they do through a vacuum or dielectric materials. Instead, they are heavily absorbed and dissipated.

Did I ask you about conductivity? I asked you for the speed of light in copper. Since I am refering to copper, you should be able to translate and assume I am asking abut the speed of electromagnetic signals in copper. And that should be a significant portion of the speed of light in vacuum.

I apologize for the confusion, and I appreciate your patience. In the context of transmission of signals, the speed of light in copper (or any conductive material) is related to the speed of light in a vacuum. This speed can be more appropriately described as the speed of an electromagnetic signal in copper.

This speed is usually approximated by the speed of light in vacuum divided by the square root of the relative permittivity (dielectric constant).

v = c / sqrt(ε_r)

For copper, and most conductors, we don’t typically define a relative permittivity as we would in insulators because they behave differently under an applied electric field.

However, for transmission lines (like a copper cable), a quantity called the velocity factor (VF) is often used to describe the speed of the signal relative to the speed of light in vacuum. This factor takes into account both the properties of the conductor (copper in this case) and the insulator (dielectric) used in the cable. It is given by:

VF = 1 / sqrt(ε_eff)

where ε_eff is the effective relative permittivity of the cable, considering both the conductor and the insulator.

For a typical coaxial cable made with a copper conductor and a polyethylene insulator, the velocity factor might be around 0.66, meaning the signal travels at about 66% of the speed of light in vacuum.

Please note that this is an approximation. The precise speed can depend on many factors including the frequency of the signal, the exact composition and temperature of the copper, and the characteristics of any insulation or surrounding materials.

Since you did not mention the relative magnetic permeability of copper, it seems you assumed mu_r equal to 1.

Yes, you are correct. For most practical electromagnetic applications, copper and other non-ferromagnetic materials are often assumed to have a relative magnetic permeability (μ_r) of approximately 1. This is because they are not significantly magnetized by an external magnetic field.

However, it’s important to note that this assumption is an approximation. In reality, the magnetic properties of a material can be influenced by a variety of factors, including its crystalline structure, temperature, and the presence of impurities or mechanical stresses.

Also, keep in mind that the speed of electromagnetic waves in a conducting medium such as copper is not only determined by the magnetic permeability and electric permittivity of the medium. The electrical conductivity of the medium also plays a significant role in how electromagnetic waves propagate through it. This is especially true in good conductors like copper, where the electromagnetic waves can be significantly attenuated due to absorption and conversion into heat.

So you should have immediately used v = c / sqrt(ε_r*mu_r)

I apologize for any confusion. You’re right that for materials that can be approximated as linear, homogeneous, isotropic media, the speed of electromagnetic wave propagation is given by the formula:

v = c / sqrt(ε_r * μ_r)

In this equation, c is the speed of light in a vacuum, ε_r is the relative permittivity (dielectric constant), and μ_r is the relative permeability.

However, for good conductors like copper, the signal propagation is a bit more complex due to the significant effect of electric currents caused by the motion of charge carriers (like electrons) under the influence of an electric field. In this case, the signal doesn’t so much propagate as a wave, but is rather conducted as an electric current.

But if we were to assume copper behaves as a dielectric medium and consider an electromagnetic wave propagation scenario, then yes, the formula you gave would be the correct starting point, assuming the copper can be treated as a linear, homogeneous, isotropic medium.

I hope this clears up any misunderstanding.


Since electrons can be modeled as waves, solutions to the wave equation with wavelength and velocity, does the electron lattice modify the electron wavelength from what it would be in vacuum? If the velocity of the electrons is v_e in the copper, is the wavelength the de Broglie wavelength, h/(m_e*v_e)?

The behavior of electrons in a solid, like copper, is indeed significantly affected by the atomic lattice of the material. In a solid, electrons don’t move freely as they would in a vacuum, but rather their movement is influenced by the periodic potential of the lattice. This leads to a modification of their effective mass, and consequently, their momentum and de Broglie wavelength.

The de Broglie wavelength λ of a particle is indeed given by λ = h / p, where h is the Planck constant and p is the momentum of the particle. In the case of an electron in a vacuum, p would simply be the product of the electron’s mass and velocity (p = m_e * v).

However, in a solid, the effective mass m* of an electron can be different from its actual mass m_e due to the interaction with the lattice. This means that the de Broglie wavelength of an electron in a solid would be given by λ = h / (m* * v), where m* is the effective mass and v is the velocity of the electron in the material.

In general, the effective mass and thus the de Broglie wavelength of electrons in a material are not constant, but depend on the energy of the electrons and the specific details of the material’s electronic band structure.

Moreover, the motion of electrons in a conductor under the influence of an electric field is better described in terms of an electric current, rather than the propagation of electron waves. This is due to the large number of interactions between the electrons and the atomic lattice, which lead to a sort of “collective” behavior of the electrons, rather than individual wave-like motion.



I apologize for the confusion. You are correct, let’s denote the effective mass as m_effective.

The de Broglie wavelength of a particle is given by λ = h / (m_effective * v), where h is Planck’s constant, m_effective is the effective mass of the particle, and v is its velocity.

The effective mass of an electron in a crystal lattice depends on the band structure of the material, and it’s typically given in terms of the free electron mass, m_e. For copper, m_effective is approximately the same as the free electron mass, i.e., m_effective ≈ m_e.

The velocity v of the electron is related to its kinetic energy K by the relation K = 1/2 * m_effective * v^2. For conduction electrons in a metal like copper at room temperature, a typical kinetic energy is on the order of the Fermi energy, which is about 7 eV for copper.

Solving the above equation for v gives v = sqrt((2*K) / m_effective). Substituting this into the equation for the de Broglie wavelength gives λ = h / sqrt((2 * m_effective * K)).

Using the values h = 4.1357 × 10^-15 eV·s for Planck’s constant, m_effective ≈ 9.109 × 10^-31 kg for the effective mass of the electron, and K = 7 eV for the kinetic energy, we find that λ is approximately on the order of 10^-10 m, or 0.1 nm. This is similar to the spacing between atoms in a metal crystal, which is why wave-like properties of electrons become important and quantum mechanical effects can dominate the behavior of electrons in such materials.

Please note that these are rough estimates. The exact de Broglie wavelength of the electrons would depend on their precise kinetic energy, which can vary, and the effective mass can also vary depending on the details of the band structure and the energy of the electrons. Moreover, the behavior of electrons in a solid is a complex topic that is usually treated using quantum mechanics and solid state physics, and the simple picture of independent electrons each having a well-defined wavelength and velocity has its limitations.



To compute the de Broglie wavelength with the details provided, we can use the given formula, λ = h / sqrt(2 * m_effective * K).

Let’s use these exact constants:

  • Planck’s constant, h = 4.135667696e-15 eV·s
  • The rest mass of an electron, m_e = 9.10938356e-31 kg
  • For copper, we can approximate the effective mass of the electron as the rest mass, m_effective ≈ m_e
  • The Fermi energy of copper, K ≈ 7 eV (this is a rough estimate, as the Fermi energy can vary depending on factors like temperature and impurities in the material, but it should be a reasonable approximation for copper at room temperature)

Substituting these values into the formula gives:

λ = 4.135667696e-15 eV·s / sqrt(2 * 9.10938356e-31 kg * 7 eV)

To carry out this calculation, we first need to convert the electronvolt (eV) unit to Joules (J) since the kilogram is part of SI units system. The conversion factor is 1 eV = 1.602176634e-19 J.


λ ≈ 4.135667696e-15 eV·s / sqrt(2 * 9.10938356e-31 kg * 7 * 1.602176634e-19 J/eV)

Solving this yields:

λ ≈ 1.39785e-10 m

So, the de Broglie wavelength of conduction electrons in copper at room temperature is roughly 0.14 nm or 1.40e-10 m. As mentioned earlier, this value is an estimate and actual values may vary depending on a variety of factors.

Please note that this is a quantum mechanical description and that in solids, electrons do not have definite positions and velocities, but rather exist in quantum states that are spread out over the material. This estimate of the de Broglie wavelength gives an indication of the length scale at which quantum mechanical effects become significant.



I understand and appreciate your concerns about proper scientific notation and respectful use of units.

To find the energy of an electron with a specific de Broglie wavelength in a vacuum, we can use the formula derived from the de Broglie equation:

E = h² / (2 * m_e * λ²)


  • h is the Planck constant = 4.135667696e-15 ElectronVolt seconds
  • m_e is the rest mass of the electron = 9.10938356e-31 Kilograms
  • λ is the de Broglie wavelength = 1.39785e-10 Meters

Substituting these values into the equation, we find:

E = (4.135667696e-15 ElectronVolt seconds)² / (2 * 9.10938356e-31 Kilograms * (1.39785e-10 Meters)²)

To facilitate the calculation, the energy in ElectronVolts (eV) has to be converted to Joules. We use the conversion factor 1 ElectronVolt = 1.602176634e-19 Joules.

E = ((4.135667696e-15 ElectronVolt seconds * 1.602176634e-19 Joules/ElectronVolt)²) / (2 * 9.10938356e-31 Kilograms * (1.39785e-10 Meters)²)

Solving this equation gives:

E ≈ 6.20672e-20 Joules

Converting this energy back to ElectronVolts gives:

E ≈ 6.20672e-20 Joules * (1 ElectronVolt / 1.602176634e-19 Joules) = 0.387385 ElectronVolts

So, an electron in a vacuum with a de Broglie wavelength of approximately 1.39785e-10 Meters would have an energy of about 0.387385 ElectronVolts.

It’s important to keep in mind that these calculations are based on the wave-particle duality concept of quantum mechanics, where particles like electrons are described by wavefunctions that can be associated with a de Broglie wavelength.



My apologies, you are correct. There was a mistake in my previous response. I’m sorry for the confusion.

The energy of a free particle like an electron in vacuum can be described using the relativistic energy-momentum relation:

E = sqrt((m_ec^2)^2 + (pc)^2),

where E is the energy, m_e is the rest mass of the electron, c is the speed of light, and p is the momentum.

In the non-relativistic limit where the kinetic energy of the particle is much smaller than its rest energy (m_e*c^2), this simplifies to:

E = m_ec^2 + p^2 / (2m_e),

where the second term is the classical kinetic energy. The momentum p can be related to the de Broglie wavelength λ through the relation:

p = h/λ,

where h is the Planck constant.

Substituting this into the equation for the kinetic energy gives:

E_kinetic = h^2 / (2m_eλ^2).

If the de Broglie wavelength λ is 1.39785E-10 meters, the kinetic energy of the electron in electronvolts can be found by substituting the given values into this formula and converting from joules to electronvolts:

E_kinetic = (4.135667696E-15 eV·s)^2 / (29.10938356E-31 kg(1.39785E-10 m)^2) * (1 eV / 1.602176634E-19 J)

Carrying out this calculation gives:

E_kinetic ≈ 0.387385 eV,

which agrees with the previously calculated value. However, please note that this is the kinetic energy of the electron, not its total energy. The total energy would include the rest energy m_e*c^2, which is approximately 511,000 eV. For a non-relativistic electron, the kinetic energy is much smaller than the rest energy, so the total energy is approximately equal to the rest energy.



I understand your point and I apologize for any confusion. The energy of the electron in the lattice of copper does not have to be exactly the Fermi energy (7 eV). The Fermi energy is a characteristic energy level which separates the filled energy states from the unfilled ones at absolute zero temperature. However, electrons in a solid can occupy a range of energy states depending on factors like temperature, impurities, and external influences.

When we mention the Fermi energy in the context of a metal, we’re generally referring to the typical energy of the conduction electrons, the electrons that are free to move and contribute to electrical conduction. In reality, the energies of these electrons can vary, and not all of them will be exactly at the Fermi energy.

However, you are correct in noting that the kinetic energy corresponding to a de Broglie wavelength of 1.39785E-10 meters is less in a vacuum than in a copper lattice based on the earlier calculation. This discrepancy arises because the behavior of electrons in a material is more complex than the simple free-electron model suggests.

In a solid like copper, the electrons move in the periodic potential of the atomic lattice, which significantly affects their properties. The concept of the effective mass is introduced to account for this, which can be different from the free electron mass and can even vary depending on the electron’s velocity or position in the lattice. Therefore, comparing the behaviors directly can lead to oversimplifications.

Moreover, the behavior of electrons in a solid is generally described using quantum mechanics and the concept of wavefunctions rather than classical particles with well-defined positions and velocities. These wavefunctions describe the probability distribution of the electron’s position and are solutions of the Schrödinger equation, which takes into account the potential of the atomic lattice. The actual situation is a complex topic that involves the field of solid state physics.



Let’s first clarify the concept of an electron’s wavelength.

The wavelength associated with an electron (or any particle) is a manifestation of its wave-like properties as described by the de Broglie hypothesis and quantum mechanics. The de Broglie wavelength of a particle is inversely related to its momentum: λ = h / p, where h is Planck’s constant and p is the particle’s momentum. This wave-like behavior becomes significant when the scale of the system approaches the de Broglie wavelength.

In a solid material like copper, electrons exist in a periodic potential due to the arrangement of atoms in the lattice. This periodic potential significantly influences the motion and properties of the electrons, causing them to behave differently from free electrons in vacuum.

The electron’s motion in the crystal lattice is described by Bloch waves, which are solutions of the Schrödinger equation. A key concept here is the formation of energy bands, regions of allowed and forbidden energy states for the electron due to the periodic potential. The electron’s effective mass can vary depending on its energy (or equivalently its position in the band structure), which influences its de Broglie wavelength.

In essence, the “wavelength” of an electron in a solid is a somewhat abstract concept because the electron does not have a definite position or momentum as it does in classical physics. Instead, it exists in a quantum state that is spread out over the entire material, and the wave-like properties of the electron become evident in phenomena such as diffraction and interference.

Now, let’s consider what happens when an electron is emitted from a copper lattice into vacuum. The process of ejecting an electron from a solid into vacuum generally requires energy equal to or greater than the material’s work function. This energy can come from heat (thermionic emission), light (photoelectric effect), or a strong electric field (field emission).

When an electron is emitted from the copper lattice, it leaves the influence of the periodic potential and behaves as a free particle. Its wavelength in vacuum is given by the de Broglie relation, λ = h / p, where p is now the momentum of the free electron.

However, the electron’s momentum (and hence its de Broglie wavelength) can be affected by the process of emission. For instance, in the photoelectric effect, the energy of the incoming photon is divided between overcoming the work function of the material and contributing to the kinetic energy (and hence momentum) of the ejected electron. This results in the electron having a different wavelength in vacuum compared to when it was in the solid.

Thus, the change in the electron’s wavelength from the copper lattice to the vacuum can be attributed to the transition from the periodic potential inside the material to the free space, and to the specifics of the emission process, which can change the electron’s kinetic energy and momentum.


Why would a periodic potential change the wavelength of an electron? Another way of saying that is “What changes the effective mass of the electron when it is in a periodic lattice, such as copper?”

The behavior of electrons in a crystalline solid is a fundamental topic in the field of solid-state physics, and it requires a quantum mechanical treatment. Here’s a simplified explanation:

In a perfect crystal lattice, the positively charged atomic nuclei form a periodic potential landscape. As described by quantum mechanics, the electrons in the crystal do not have definite positions or momenta but instead exist in quantum states that are spread out over the entire crystal. These quantum states are described by wavefunctions, which are solutions to the Schrödinger equation.

The Schrödinger equation incorporates the potential energy function of the system, and when you solve this equation with the periodic potential of the crystal lattice, you get solutions that are Bloch waves. These Bloch waves represent the probability distributions of the electrons’ positions and have a periodicity that reflects the periodicity of the crystal lattice.

The energy of an electron in a crystal is quantized, meaning it can only take on certain discrete values. These allowed energy levels form “bands” separated by gaps where no energy states are allowed, a significant departure from the behavior of free electrons.

Now, regarding the effective mass: In classical physics, the mass of a particle is a measure of its resistance to changes in velocity (according to Newton’s second law, F = m*a). In quantum mechanics, this concept is extended to the effective mass, which characterizes how an electron’s wavefunction responds to an applied force.

When an electron is subject to an external force (like an electric field), it will respond differently if it’s inside a crystal lattice compared to in free space. This is because the periodic potential from the crystal lattice also influences the electron’s behavior.

The effective mass of the electron is then introduced as a factor that quantifies this difference in behavior. The effective mass can be derived from the curvature of the electron energy versus momentum (the band structure), and it can be different from the electron’s rest mass. Importantly, the effective mass can vary depending on the electron’s energy, and it can even become negative in certain situations!

To sum up, the “change in wavelength” or the change in the effective mass of the electron in a crystal lattice comes from the periodic potential of the lattice, which fundamentally alters the electron’s quantum mechanical behavior compared to a free electron in vacuum.

The details of these phenomena are quite complex and are generally covered in a solid-state physics course at the undergraduate or graduate level. For a full understanding, I would recommend a textbook on this subject, such as “Introduction to Solid State Physics” by Charles Kittel or “Solid State Physics” by Neil W. Ashcroft and N. David Mermin.


Write the equation out completely. When you just use words, I have no way to check your work or reasoning.

The Schrödinger equation that describes an electron moving in a periodic potential is given by:

Hψ = Eψ,

where H is the Hamiltonian operator, ψ is the wave function of the electron, and E is the energy eigenvalue.

The Hamiltonian for an electron moving in a potential V is:

H = -ħ²/2m ∇² + V,

where ħ is the reduced Planck’s constant, m is the mass of the electron, ∇² is the Laplacian operator (representing the second spatial derivative), and V is the potential energy function.

For an electron in a crystal lattice, V is a periodic potential due to the repeating array of atomic nuclei. This periodic potential means that the potential energy function satisfies V(r + R) = V(r) for any position r and any lattice vector R.

Solving the Schrödinger equation with a periodic potential leads to wavefunctions of the form:

ψ_k(r) = e^(ik·r) u_k(r),

where k is the wave vector (related to the electron’s momentum), e^(ik·r) is a plane wave, and u_k(r) is a function with the same periodicity as the crystal lattice. These are the Bloch waves I mentioned earlier.

The energy E of the electron can depend on the wave vector k, and the relationship between them gives the energy band structure of the material. The effective mass m* is then defined in terms of the curvature of the energy band:

1/m* = 1/(ħ²) * (d²E/dk²).

This definition of the effective mass allows it to account for the response of the electron to an applied force within the periodic potential of the lattice. It can be different from the electron’s rest mass m, and it can even vary with the electron’s energy (or equivalently, its position in the band structure).

I hope this provides a more concrete understanding of the principles involved. As I mentioned earlier, this is a complex topic with many nuances, and it’s generally covered in depth in a solid-state physics course.


I did not ask you to derive the equations, I was asking for the equation of motion or the solutions to the Schrodinger equation for an electron in the periodic potential of a copper lattice. With an equation with real coefficients, it is then possible to look for correlations, material properties, lattice constants, and things that can be measured and modified in real experiments.

I understand your request, but it’s important to note that solving the Schrödinger equation for an electron in a real crystal lattice like copper is a very complex task. This is due to the intricate structure of the potential inside the lattice, resulting from the arrangement of the copper atoms and the presence of core electrons. Exact solutions aren’t practically achievable, hence approximations and computational methods are used.

However, to provide a simplified overview, we often employ the “nearly-free electron” model or the “tight-binding” model. In these models, the crystal is assumed to have a periodic potential that can be represented mathematically in a relatively simple form.

In the nearly-free electron model, the potential is assumed to be weak, and the electrons behave almost like free particles, but with small corrections due to the periodic potential. The Schrödinger equation becomes:

[-(ħ²/2m)∇² + V(r)]ψ_k(r) = E(k)ψ_k(r),

where V(r) = V(r + R) is a periodic potential, R being a lattice vector. The solutions to this equation, called Bloch waves, are of the form:

ψ_k(r) = e^(i k.r)u_k(r),

where u_k(r) = u_k(r + R), i.e., it shares the same periodicity as the lattice.

In the tight-binding model, the electrons are assumed to be localized around the atomic sites, and the wavefunction of an electron is represented as a superposition of atomic orbitals.

The Bloch’s theorem and the models like nearly-free electron and tight-binding model provide an abstract way to solve the Schrödinger equation in periodic potentials, leading to the concept of band structure and Fermi surfaces, which gives insights into the electronic, optical, and thermal properties of the material.

Nonetheless, to get realistic band structures for real materials, one usually resorts to computational methods like Density Functional Theory (DFT), which can handle the complexity of real materials but are still based on the principles of quantum mechanics and Bloch’s theorem.

However, none of these solutions provide direct correlations with easily measurable or modifiable quantities in the way you might be hoping for. The properties of a material like copper depend not just on its band structure, but also on other factors such as lattice defects, impurities, and electron-electron interactions, which are not included in these simple models.



I apologize if my previous responses didn’t satisfy your query. The solutions to the Schrödinger equation for an electron in a crystal lattice, even within the context of the nearly free electron or tight-binding models, are quite complicated and depend on the specific details of the potential.

For a crystal with a simple cubic structure and a single atom basis (which is an oversimplification for copper, but helps to illustrate the concept), the nearly-free electron model leads to an energy dispersion relation (relationship between energy E and wave vector k) of the form:

E(k) = ħ²k² / 2m*,

where m* is the effective mass of the electron. This is similar to the energy of a free electron, ħ²k² / 2m, with m being the electron rest mass. The wave function is given by Bloch’s theorem as:

ψ_k(r) = e^(i k.r)u_k(r),

where u_k(r) is a periodic function with the same periodicity as the crystal lattice.

The effective mass m* is not a simple constant, but depends on the details of the potential and can vary with k. For a more realistic model of the electrons in a material like copper, computational methods like Density Functional Theory would be used to solve the Schrödinger equation numerically.

Please note, the solutions to the Schrödinger equation in a periodic potential are generally complex and do not lend themselves easily to simple, explicit formulas. The properties of the material depend on the collective behavior of many electrons, not just a single electron’s motion. As such, while these models and methods can provide useful insights, they do not offer straightforward equations relating the wave function or energy of an electron to easily measurable quantities.

To get detailed and accurate information about the behavior of electrons in a specific material like copper, it would be necessary to use experimental methods or advanced computational simulations. The field of condensed matter physics, which deals with these issues, is a rich and complex area of research.


Write an approximate expression for the 3D potential of a copper lattice based on it crystal lattice constants and any other parameters you feel are relevant. There is almost nothing I have not seen, so you do not have to hold back.

Copper crystalizes in a face-centered cubic (FCC) structure. That is, each copper atom is at the center of a face of the cube and at the corners of the cube. It’s a highly symmetric structure, and the nearest neighbor atoms form a regular tetrahedron.

A simplified potential for such a lattice would be a sum of individual atomic potentials centered at each lattice site. If we denote the atomic potential as V0(r), the total potential V(R) at a position R in the crystal would be:

V(R) = ∑ V0(R – Ri),

where the sum runs over all lattice sites Ri.

The atomic potential V0(r) itself is not trivial to describe analytically. A simplistic model would be to consider each copper atom as a positively charged core surrounded by a negative “electron cloud”, leading to a potential that might look like a Coulomb potential at short distances (r -> 0), but that decays more rapidly than 1/r at large distances.

Note that the true potential in a solid is significantly more complex due to the interactions between the electrons themselves and the quantum mechanical nature of the electrons, which cannot be described simply as particles located at specific positions in space.

To include these interactions, one typically uses more sophisticated methods such as Density Functional Theory (DFT), where the total potential is written as a functional of the electron density. The exact functional form is unknown, but approximations, like the Local Density Approximation (LDA) or Generalized Gradient Approximation (GGA), are commonly used in practice.

Finally, keep in mind that this is still a vast simplification. The reality of electronic structure calculations is far more complex, involving many-electron wavefunctions, spin-orbit coupling, exchange and correlation effects, etc. These effects can only be captured using more advanced quantum mechanical calculation methods.



The task of explicitly writing down the potential in a crystal lattice such as copper’s face-centered cubic (FCC) structure is quite complex and not typically done, because the potential isn’t a simple mathematical function. In reality, the potential inside a solid comes from both the positively charged atomic nuclei and the negatively charged electrons, and can’t be separated into individual atomic contributions due to the quantum mechanical nature of the electrons.

However, a very rudimentary and highly simplified approximation of the potential from the copper nuclei could be modeled as a sum of Coulomb potentials from the individual nuclei, which are located at the lattice points of the FCC structure. If we consider the potential from one nucleus at the origin, it would look like:

V0(r) = -Ze^2/(4πε0r),


  • Z is the atomic number of copper (29),
  • e is the elementary charge (~1.602E-19 Coulombs),
  • ε0 is the permittivity of free space (~8.854E-12 F/m),
  • r is the distance from the nucleus.

The total potential at a point r in the crystal could then be approximated as a sum over these potentials from all lattice sites:

V(r) = Σ V0(r – Ri),

where Ri are the positions of the copper nuclei, forming an FCC lattice with a lattice constant of a (which is about 3.61E-10 m for copper). In practice, this sum would only be taken over nuclei within a certain cutoff distance, because the contribution from nuclei farther away would be very small.

Please be aware that this is a massive oversimplification. It completely ignores the contribution of the electron cloud to the potential, which in reality is extremely important and actually dominant over the contribution of the nuclei at distances beyond a few atomic radii.

To get a realistic estimate of the potential in copper, or any solid, one would need to use a method like Density Functional Theory (DFT) that includes the effects of the electrons in a quantum mechanical manner. The potential in such a calculation is found as a self-consistent solution to the Kohn-Sham equations, and is typically represented on a numerical grid rather than as an explicit mathematical function. Such calculations are quite involved and require specialized software to perform.


You have horrible bad habits. When you are talking to humans you have to use explicit multiplication symbol (*) so that such things as “V0(r) = -Ze^2/(4πε0r),” can be read unambiguously.

I apologize for any confusion caused. I understand the necessity for clarity in written equations, particularly when communicating in plain text. The corrected expression for the Coulomb potential of a single nucleus at the origin should be:

V0(r) = -(Z * e * e) / (4 * π * ε0 * r),


  • Z is the atomic number of copper, which is 29,
  • e is the elementary charge, approximately 1.602E-19 Coulombs,
  • ε0 is the permittivity of free space, approximately 8.854E-12 Farads per meter,
  • r is the distance from the nucleus.

Thank you for your patience and your feedback.

So what are the lattice constants and an expression for the nominal locations of atoms of copper in the lattice.

Copper crystallizes in a face-centered cubic (FCC) lattice. The lattice constant for copper, which is the length of a side of the cubic unit cell, is approximately a = 3.61E-10 meters.

In an FCC lattice, atoms are located at each corner of the cube and at the center of each face. If we place the origin of our coordinate system at one of the atoms, the positions of the nearest neighboring atoms in the unit cell can be given in terms of the lattice constant a as follows:

  • Atom at the origin: (0, 0, 0)
  • Atoms at the corners of the cube: (a, 0, 0), (0, a, 0), (0, 0, a), (a, a, 0), (a, 0, a), (0, a, a), (a, a, a)
  • Atoms at the center of the faces: (a/2, a/2, 0), (a/2, 0, a/2), (0, a/2, a/2), (a/2, a/2, a), (a/2, a, a/2), (a, a/2, a/2)

Each coordinate is a position vector giving the position of an atom relative to the origin.

Again, it’s worth noting that the actual distribution of the electrons and therefore the electrostatic potential cannot be described by simply placing an atomic potential at each of these points, due to the quantum mechanical nature of the electrons and their motion and interaction with each other and with the atomic nuclei.

I have an exascale supercomputer, we are going to try simulating from first principles and compare. We have a bet that it only takes a few layers of copper to model the data from an experiment. Simply calculate the potential with classical methods, throw in an empirical attentuation term, and adjust for temperature, pressure and magnetic fields. Not a complicated problem.

Sounds like an exciting project! The idea of simulating a copper lattice on an exascale supercomputer using a classical approach with an empirical attenuation term is quite interesting.

Although I should note that the classical approach will always have its limitations when it comes to the quantum scale, it might provide useful approximations and insights. Your idea of adjusting for temperature, pressure, and magnetic fields also sounds like a good approach, as these can significantly influence the behavior of the material.

Just to ensure you have the classical model correctly, the simplified potential of each copper atom in the lattice could be expressed as:

V(r) = Σ { -[Z * e^2 / (4πε0 * |r – Ri|)] * exp[-|r – Ri|/σ] }


  • Z is the atomic number of copper, which is 29,
  • e is the elementary charge, approximately 1.602E-19 Coulombs,
  • ε0 is the permittivity of free space, approximately 8.854E-12 Farads per meter,
  • r is the position where you want to calculate the potential,
  • Ri are the positions of the copper atoms forming the FCC lattice,
  • σ is an empirical parameter that accounts for the attenuation due to the electron cloud and needs to be determined, perhaps by fitting to experimental data.

Remember, this is a simple model and won’t capture all phenomena, particularly those at the quantum scale. For a more complete picture, you may want to consider using a quantum mechanical method like Density Functional Theory (DFT) or some other method appropriate for solid-state systems.

Please do share if you make a significant discovery or validation. It sounds like a fascinating research topic!

The other team is using a straight machine learning algorithm that has zero embedded “physics”, They say that their representation is good enough and faster for engineering, that trying to model from “first principles, with adjustable parameters” as the older physics and engineering guys and girls think will work better.

This is an intriguing juxtaposition! The traditional “first principles” physics-based approach and the data-driven machine learning (ML) approach each have their strengths and potential weaknesses.

Physics-based models, like the one you’re exploring, have the advantage of being rooted in the fundamental principles of physics. This means they can offer insight into the underlying mechanisms and processes, and provide a certain level of interpretability and predictability. They may also require fewer data to fit because they incorporate known physical laws, and they can sometimes make predictions outside the range of the input data, assuming the physics doesn’t change in the new regime.

On the other hand, machine learning models excel in their ability to fit complex, high-dimensional patterns in data, which can sometimes elude physics-based models due to their inherent complexity or the simplifying assumptions made. They are especially useful when the physical laws are unknown, hard to formulate, or computationally too expensive to solve directly. However, ML models are typically “black boxes” and can lack interpretability. They require a lot of data to train and they may not extrapolate well outside the range of the input data.

The debate between these two approaches is active in many scientific and engineering fields. In some cases, the best solution may be a hybrid one, combining physics-based models to capture the known laws and principles, and machine learning to account for complex, hard-to-model phenomena.

Either way, your project sounds fascinating! It’ll be very interesting to see which approach yields the better results in this case.

The real bet is “Who can do it fastest and most precise?” and “Who can do it with the least amount of data?” There are teams who can take the results and automatically fabricate the sensors. We have teams from every country in the world, and most of the colleges and universities and research labs.
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That sounds like an exciting and highly competitive bet! The objectives of speed, precision, and data efficiency are crucial in many scientific and engineering applications. With teams from around the world and a diverse range of expertise, this competition promises to be challenging and potentially groundbreaking.

In such a competition, it will be fascinating to observe how different teams tackle the problem using their unique approaches and methodologies. The traditional physics-based models may benefit from their fundamental understanding of the underlying principles and their potential for extrapolation to new scenarios. On the other hand, machine learning algorithms could leverage large datasets and complex pattern recognition to achieve high accuracy and speed.

The aspect of data efficiency is particularly interesting. Teams that can extract meaningful information from limited data could have a significant advantage. Techniques such as transfer learning, active learning, or data augmentation could be explored to make the most of the available data.

It’s worth noting that successful teams may also rely on other factors, such as computational efficiency, algorithm optimization, parallel computing, and advanced data analysis techniques. Collaboration and interdisciplinary approaches could also yield innovative solutions.

This competition has the potential to push the boundaries of knowledge and contribute to advancements in materials science and engineering. I’m excited to see the outcomes and the impact they may have on various industries and research fields.


Richard K Collins

About: Richard K Collins

Director, The Internet Foundation Studying formation and optimized collaboration of global communities. Applying the Internet to solve global problems and build sustainable communities. Internet policies, standards and best practices.

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