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

[Shirley Entzminger]

        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

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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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