Wednesday, December 18, 2024

Quantum Chemistry


 AI addressed the most difficult challenges in quantum chemistry



Excited states are key in physics and chemistry, but calculating their properties accurately from first principles is still challenging. Recent progress in using deep learning for ground-state calculations shows that this method could also help solve the problem for excited states.

Scientists at Imperial College London and Google DeepMind have proposed a solution using AI to the tough challenge of modeling the states of molecules.
The team looked into how molecules shift between ‘excited states’ when they receive a lot of energy, like from light or heat. This energy can move their electrons into a temporary new arrangement called an excited state.

The total amount of energy absorbed and released during the transfer of molecules between states leaves a unique fingerprint for different molecules and materials. This affects many technologies, like solar panels, LEDs, semiconductors, and photocatalysts. It’s also crucial for biological processes involving light, such as photosynthesis and vision.

However, modeling this is tough because excited electrons behave according to quantum principles, meaning their exact positions are uncertain and only described by probabilities.

Lead researcher Dr David Pfau, from Google DeepMind and the Department of Physics at Imperial, said: “Representing the state of a quantum system is highly challenging. A probability has to be assigned to every possible configuration of electron positions.

“The space of all possible configurations is enormous — if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe. This is exactly where we thought deep neural networks could help.” Scientists computed the energy of atoms and molecules based on precise principles by developing and using a new mathematical approach with a neural network called FermiNet (Fermionic Neural Network).

The team tested their method on various examples and got promising results. For a small but complex molecule called the carbon dimer, they achieved a mean absolute error (MAE) of 4 meV (a tiny energy measure), which is five times more accurate than previous top methods that had an MAE of 20 meV.

Dr Pfau said: “We tested our method on some of the most challenging systems in computational chemistry, where two electrons are excited simultaneously, and found we were within around 0.1 eV of the most demanding, complex calculations done to date.

“Today, we’re making our latest work open source, and we hope the research community will build upon our methods to explore the unexpected ways matter interacts with light.”

About

Award Information - International Analytical Chemistry Awards

Welcome to the International Analytical Chemistry Awards - International Analytical Chemistry Awards, a premier event in the realm of International Analytical Chemistry Awards. Here's what you need to know about this exciting Award :

  • Theme: The theme for International Analytical Chemistry Awards is "Sustainable International Analytical Chemistry Awards for a Connected Future."
  • Hybrid Event: International Analytical Chemistry Awards is an innovative hybrid event, offering two dynamic ways to participate.



at

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)

  Advanced Mid-Infrared Sensors for Molecular Analysis Introduction to Mid-Infrared (Mid-IR) Sensing Mid-infrared (mid-IR) sensors, operati...