Tuning Materials with Light

Compute what a laser pulse does to a real material, with electrons and lattice vibrations treated together, in a computationally tractable way over long time scales.

Problem 04OpenUpdated Nov 2026

Many-body theoryCondensed matter physicsUltrafast dynamics

Written with inputs from

  • Prof. Felipe H. da JornadaAssistant Professor of Materials Science and Engineering, Stanford University

Light Meets Matter: What Happens?

Light can change what a material is. Femtosecond pulses have been reported to switch materials into transient superconducting, magnetic and hidden phases, to melt insulators into metals, and to steer chemistry faster than heat can spread.

Physicists have a good theoretical framework for how electrons and ions move when a material is calm and at rest. Hit it with a laser pulse and all three kinds of pushing, electrons on electrons, electrons on ions and ions on ions, happen at once and change from moment to moment. The exact equations for this regime, the Kadanoff–Baym equations of non-equilibrium Green’s function (NEGF) theory, exist but are so expensive that a computer can follow only a handful of sites for a short time.

The goal of this grand challenge is a new method that is orders of magnitude faster and works for real materials rather than simplified model Hamiltonians: verified on a model, judged on materials.

One cycle of what the method must compute: the pulse arrives, electrons absorb it and scatter among themselves, their energy drains into lattice vibrations through electron–phonon coupling, and the vibrations redistribute and decay through phonon–phonon scattering.

The problem

Develop a method that captures the properties of light-driven materials. An ideal method captures the total energy, the quasiparticle excitations, and the superconducting Tc and pair correlation function of materials out of equilibrium:

  • Under continuous or pulsed light excitation.
  • Under weak or strong external drives.
  • Driven by light, directly or through phonons.
  • Capturing the dynamics and the transients.
  • Including electron–electron, electron–phonon and phonon–phonon interactions simultaneously, ideally on the same footing.
  • Orders of magnitude faster than a pure NEGF approach.
  • Valid in the high-density limit, for example many free carriers or many excitons.

Target properties to predict from the drive

  • Total energyThrough the drive and the relaxation that follows
  • Quasiparticle excitationsExcitation energies and populations out of equilibrium
  • Tc and pair correlationsThe pair correlation function, in and out of equilibrium

Four results to reproduce

One model system and three materials. These are specific reproduction targets, not interchangeable illustrations. The model-system tests verify the equations; the three material cases test physical transfer.

Electronic correlations in Hubbard chains and ladders

Schroedter, Bonitz and Joost (2026), Figs. 2–3: density imbalance and correlated double occupancy of the 11-site Hubbard chain at interaction-to-hopping ratios U/J = 5 and 15 up to tJ/ħ = 50, plus the U/J = 0–20 sweep. Figs. 8–9: equilibrium and laser-driven spin correlations of the 6×2 ladder. Fig. 10: optical-conductivity thresholds and line shapes at U/J = 3, 6 and 12. Compared against converged DMRG, exact diagonalization and Bethe-ansatz references.

11
sites · U/J = 5, 15
tJ/ħ = 50
time window
6×2
ladder · spin correlations
U/J = 3, 6, 12
σ(ω) thresholds

Reference to reproduce: Schroedter, Bonitz and Joost, arXiv:2606.10773 (2026).

WSe₂: exciton formation and scattering

Madéo et al. (2020), Figs. 2–4: energy- and momentum-resolved photoemission at 90 K under resonant 1.72 eV and above-gap 2.48 eV excitation. Reproduce the bright and dark exciton energies and the full population traces, including the roughly 400 fs dark-exciton buildup and the late-time bright/dark population ratio near 0.5 under resonant excitation.

1.72 eV
resonant pump
2.48 eV
above-gap pump
~400 fs
dark-exciton buildup
≈0.5
bright/dark, late time

Reference to reproduce: Madéo et al., Science 370, 1199 (2020).

Graphite: electron-lattice energy flow and anharmonic relaxation

René de Cotret et al. (2019), Figs. 2, 6–7: response to 35 fs, 800 nm, 12 mJ/cm² pumping. Reproduce the diffuse-scattering maps and the inferred optical and acoustic phonon populations at 0.5, 1.5, 5 and 100 ps, plus the rise and decay of the K-point A₁′ optical mode. Predict the energy transfer into the acoustic branches, not just an average lattice temperature.

35 fs
800 nm · 12 mJ/cm²
0.5 · 1.5 · 5 · 100 ps
snapshots
A₁′ at K
rise and decay

Reference to reproduce: René de Cotret et al., Physical Review B 100, 214115 (2019).

MgB₂: equilibrium pairing and driven superconducting response

Katsumi et al. (2025), Figs. 2–4 and Supplemental Material Fig. S3: the sample’s Tc = 38 K, the measured lower-gap temperature curve, the nonlinear peak near 1 THz at 7 K, and the first- and third-harmonic responses under 0.63 THz driving. Reproduce their temperature and field dependence, including the monotonic normalized first-harmonic response on cooling. For strong excitation, reproduce the complex conductivity at 2 ps after a 150 kV/cm pump, rather than assuming the low-field response remains valid.

38 K
Tc
0.63 THz
drive · 1st and 3rd harmonic
~1 THz
nonlinear peak at 7 K
150 kV/cm
σ(ω) at 2 ps

Reference to reproduce: Katsumi et al., Physical Review Letters 135, 036902 (2025).

Submissions

We evaluate submissions on their ability to reproduce published and preprint results that we collate: the Hubbard chain and ladder benchmarks of Schroedter, Bonitz and Joost (2026) against their exact references, which verifies the equations, and the three materials above, WSe₂ (Madéo et al., 2020), graphite (René de Cotret et al., 2019) and MgB₂ (Katsumi et al., 2025), against the published time-resolved data.

Submissions should include:

  • Method: A description of the proposed computational approach and its underlying scientific principles.
  • Implementation: Working code and instructions to run it on a driven material.

Evaluation also considers:

  • Predictive accuracy: Agreement between theoretical predictions and experimental measurements.
  • Speed and computational effort: The time it takes to simulate a drive.
  • Recovering known limits: Weak drives and equilibrium systems.

Next: 05 · Predicting Heat Transport across Solid Interfaces

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