[Crib-list] SPEAKER: DR. STEVEN TORRISI (Harvard) / "Virtual" CRIBB Seminar / 12:00 PM - 1:00 PM / Friday, July 23, 2021 (fwd)

[Shirley Entzminger]

 		A    R E M I N D E R . . .


         COMPUTATIONAL RESEARCH in BOSTON and BEYOND SEMINAR
 			     (CRIBB)


   ZOOM meeting info...

 	https://mit.zoom.us/j/96155042770

 	Meeting ID: 961 5504 2770

====================================

DATE:	Friday, July 23, 2021

TIME:	12:00 PM - 1:00 PM


TITLE:	Which parts matter?
 	 Interpretable random forest models for X-Ray absorption spectra



SPEAKER:  DR. STEVEN TORRISI  (Physics Dept., Harvard Univ.)
 	   Research Scientist, Toyota Research Institute
 	   (Starting August 2021)



ABSTRACT:

X-ray absorption spectroscopy (XAS) produces a wealth of information about the 
local structure of materials, but interpretation of spectra often relies on 
easily accessible trends and prior assumptions about the structure. Recently, 
researchers have demonstrated that machine learning models can automate this 
process to model the environments of absorbing atoms from their XAS spectra. 
However, machine learning models are often difficult to interpret, making it 
challenging to determine when they are valid and whether they are consistent 
with physical theories. In this work, we present three main advances to the 
data-driven analysis of XAS spectra: we demonstrate the efficacy of random 
forests in solving two new property determination tasks (predicting Bader 
charge and mean nearest neighbor distance), we address how choices in data 
representation affect model interpretability and accuracy, and we show that 
multiscale featurization can elucidate the regions and trends in spectra that 
encode various local properties. The multiscale featurization transforms the 
spectrum into a vector of polynomial-fit features, and is contrasted with the 
commonly-used “pointwise” featurization that directly uses the entire spectrum 
as input. We find that across thousands of transition metal oxide spectra, the 
relative importance of features describing the curvature of the spectrum can be 
localized to individual energy ranges, and we can separate the importance of 
constant, linear, quadratic, and cubic trends, as well as the white line 
energy.

This work has the potential to assist rigorous theoretical interpretations, 
expedite experimental data collection, and automate analysis of XAS spectra, 
thus accelerating the discovery of new functional materials. We expect that 
this featurization strategy could be useful for broad domains of application, 
such as one-dimensional time-series analysis or other forms of spectroscopy.

Paper: https://www.nature.com/articles/s41524-020-00376-6

=======================================

For information about the "Computational Research in Boston and Beyond Seminar"
(CRIBB), please visit:

 		https://math.mit.edu/sites/crib/


=================

Shirley A. Entzminger
Administrative Assistant II
Department of Mathematics
Massachusetts Institute of Technology
77 Massachusetts Avenue
Building 2, Room 350A
Cambridge, MA 02139
PHONE: 	(617) 253-4994
E-mail:	daisymae at math.mit.edu
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