[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
-------------- next part --------------
A non-text attachment was scrubbed...
Name: CRIBB Seminar - Friday, July 23, 2021 - spk. Steven Torrisi	(Harvard).pdf
Type: application/pdf
Size: 197819 bytes
Desc: 
Url : http://mailman.mit.edu/pipermail/crib-list/attachments/20210723/c8a88787/attachment.pdf