Monday, December 13, 2021

g-f(2)727 THE BIG PICTURE OF THE DIGITAL AGE (12/13/2021), nature, DeepMind AI tackles one of chemistry’s most valuable techniques




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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Artificial Intelligence, Predicting material properties (12/13/2021) 


OPPORTUNITY, nature 

Machine-learning algorithm predicts material properties using electron density.


  • A team led by scientists at the London-based artificial-intelligence company DeepMind has developed a machine-learning model that suggests a molecule’s characteristics by predicting the distribution of electrons within it. 
  • The approach, described in the 10 December issue of Science, can calculate the properties of some molecules more accurately than existing techniques.
  • “To make it as accurate as they have done is a feat,” says Anatole von Lilienfeld, a materials scientist at the University of Vienna.
  • The paper is “a solid piece of work”, says Katarzyna Pernal, a computational chemist at Lodz University of Technology in Poland. But she adds that the machine-learning model has a long way to go before it can be useful for computational chemists.
  • Since its beginnings in the 1960s, DFT has become one of the most widely used techniques in the physical sciences: an investigation by Nature’s news team in 2014 found that, of the top 100 most-cited papers, 12 were about DFT. Modern databases of materials’ properties, such as the Materials Project, consist to a large extent of DFT calculations.


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    References





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    Lessons learned, nature


    • In principle, the structure of materials and molecules is entirely determined by quantum mechanics, and specifically by the Schrödinger equation, which governs the behaviour of electron wavefunctions. These are the mathematical gadgets that describe the probability of finding a particular electron at a particular position in space. But because all the electrons interact with one another, calculating the structure or molecular orbitals from such first principles is a computational nightmare, and can be done only for the simplest molecules, such as benzene, says James Kirkpatrick, a physicist at DeepMind.
    • To get around this problem, researchers — from pharmacologists to battery engineers — whose work relies on discovering or developing new molecules have for decades relied on a set of techniques called density functional theory (DFT) to predict molecules’ physical properties. 
    • The DeepMind team has made probably the most ambitious attempt yet to deploy AI to calculate electron density, the end result of DFT calculations. “It’s sort of the ideal problem for machine learning: you know the answer, but not the formula you want to apply,” says Aron Cohen, a theoretical chemist who has long worked on DFT and who is now at DeepMind.


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    Lessons learned, nature 


    • The team trained an artificial neural network on data from 1,161 accurate solutions derived from the Schrödinger equations. To improve accuracy, they also hard-wired some of the known laws of physics into the network. They then tested the trained system on a set of molecules that are often used as a benchmark for DFT, and the results were impressive, says von Lilienfeld. “This is the best the community has managed to come up with, and they beat it by a margin,” he says.
    • Kirkpatrick and Cohen say that DeepMind is releasing their trained system for anyone to use. For now, the model applies mostly to molecules and not to the crystal structures of materials, but future versions could work for materials, too, the authors say.


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    References


    “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)727, Fernando Machuca, December 13, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.com Corporation.


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