CMSP Seminar (Atomistic Simulation Seminar Series) 17 July, 11:30hrs, by Michele Ceriotti

[CMSP Seminars Secretariat]

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CMSP Seminar (Atomistic Simulation Seminar Series)
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*Wednesday, 17 July 2024, 11:30 hrs*
*/Luigi Stasi Seminar Room (Leonardo Building, first floor)/*/
/
/Zoom registration link: 
https://zoom.us/meeting/register/tJEldumsqzgoG9RZjC7cKnsUSzL1DdN2MLdB<https://zoom.us/meeting/register/tJ0kd-6oqTkvHNLzzVnZxbk4Q6rrEkD2cyoH>
<https://zoom.us/meeting/register/tJEldumsqzgoG9RZjC7cKnsUSzL1DdN2MLdB> /

Speaker:*  Michele Ceriotti * (EPFL, Lausanne, Switzerland)

Title: *More than physics, more than data: integrated machine-learning 
models for materials *

Abstract:
Machine-learning techniques are often applied to perform "end-to-end" 
predictions, that is to make a black-box estimate of a property of 
interest using only a coarse description of the corresponding inputs.
In contrast, atomic-scale modeling of matter is most useful when it 
allows one to gather a mechanistic insight into the microscopic 
processes that underlie the behavior of molecules and materials.
In this talk I will provide an overview of the progress that has been 
made combining these two philosophies, using data-driven techniques to 
build surrogate models of the quantum mechanical behavior of atoms, 
enabling "bottom-up" simulations that reveal the behavior of matter in 
realistic conditions with uncompromising accuracy.
I will discuss two ways by which physical-chemical ideas can be 
integrated into a machine-learning framework.
One way involves using physical priors, such as smoothness or symmetry 
of the structure-property relations, to inform the mathematical 
structure of a generic ML approximation. The other entails a deeper 
level of integration, in which explicit physics-based models and 
approximations are built into the model architecture.
I will discuss several examples of the application of these ideas, from 
the calculation of electronic excitations to the design of solid-state 
electrolyte materials for batteries and high-entropy alloys for 
catalysis, emphasizing both the accuracy and the interpretability that 
can be achieved with a hybrid modeling approach, and providing an 
overview of the exciting research directions that are made available by 
these new modeling tools.


http://www.ictp.it/research/cmsp.aspx

The Abdus Salam International Centre for Theoretical Physics
https://www.ictp.it/

    
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