The document discusses using theory, computation, and machine learning to interpret experimental X-ray absorption spectroscopy data and determine local atomic structures. It presents examples of using density functional theory calculations of X-ray absorption near edge structure (XANES) spectra to benchmark predictions against experiments and develop machine learning models for structure classification. The models are able to classify local structures like tetrahedral, square pyramidal, and octahedral coordination with over 85% accuracy across different materials systems. This approach provides a way to solve the inverse problem of determining structures from spectroscopy measurements in real time.
Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fie...KAMAL CHOUDHARY
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.
The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.
Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fie...KAMAL CHOUDHARY
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.
The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.
Database of Topological Materials and Spin-orbit SpillageKAMAL CHOUDHARY
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov
Computational Database for 3D and 2D materials to accelerate discoveryKAMAL CHOUDHARY
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
I gave 1 hour seminar at ANSTO (Australian Nuclear Science and Technology Organization) to introduce my approach to magnetism. I see myself as an experimental physicist who is studying magnetism by using neutron scattering techniques. Throughout my career, I had learned local structure analysis (PDF), magnetic structural analysis, and inelastic neutron scattering technique to investigate superconductor, multiferroics, antiferromagnets, helimagnets, and frustrated magnets. I was trying to explain my approach to magnetism as an experiment physicist to both professional scientists and novices.
(PhD Dissertation Defense) Theoretical and Numerical Investigations on Crysta...James D.B. Wang, PhD
(I'm no longer in this academic field, and thus sharing my PhD dissertation slide here to anyone who would be interested in it)
=================================================
In order to prevent the spurious wave reflections and to improve the computational efficiency in nanomechanical simulation, this dissertation performs a series of theoretical/numerical studies on the crystalline nano material, including nanomechanics of monatomic lattice, isothermally non-reflecting boundary condition, fast updating of neighbor list, and the application/simulation in laser-assisted nano-imprinting.
Database of Topological Materials and Spin-orbit SpillageKAMAL CHOUDHARY
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov
Computational Database for 3D and 2D materials to accelerate discoveryKAMAL CHOUDHARY
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
I gave 1 hour seminar at ANSTO (Australian Nuclear Science and Technology Organization) to introduce my approach to magnetism. I see myself as an experimental physicist who is studying magnetism by using neutron scattering techniques. Throughout my career, I had learned local structure analysis (PDF), magnetic structural analysis, and inelastic neutron scattering technique to investigate superconductor, multiferroics, antiferromagnets, helimagnets, and frustrated magnets. I was trying to explain my approach to magnetism as an experiment physicist to both professional scientists and novices.
(PhD Dissertation Defense) Theoretical and Numerical Investigations on Crysta...James D.B. Wang, PhD
(I'm no longer in this academic field, and thus sharing my PhD dissertation slide here to anyone who would be interested in it)
=================================================
In order to prevent the spurious wave reflections and to improve the computational efficiency in nanomechanical simulation, this dissertation performs a series of theoretical/numerical studies on the crystalline nano material, including nanomechanics of monatomic lattice, isothermally non-reflecting boundary condition, fast updating of neighbor list, and the application/simulation in laser-assisted nano-imprinting.
Structural and Dielectric Studies of Cerium Substituted Nickel Ferrite Nano P...theijes
Cerium substituted Nickel ferrite nanoparticles with general formula NiCeXFe2-XO4 (x=0.0, 0.05, 0.1, 0.15) have been synthesized by using sol-gel method. The crystalline structure and grain size of these particles were analyzed by using XRD; the particle size ranged from 12.22nm to 17.60nm.The decrease in value of the lattice parameter with doping suggests that there is shrinkage in unit cell. The single-phase cubic spinal structure was clearly indicated by the XRD patterns of pure NiFe2O4.The XRD pattern also show that all the samples had formed the cubic single phase spinal structure. Dielectric properties have been studied in the frequency range of 1 kHz to 5 MHz. Permittivity and tangent loss (tanδ) decreases with the substitution of Ce3+ in parent crystal structure.
- Nanoparticles NiFe2-xTbxO4 (x=0.00, 0.04, 0.08,
0.12) ferrite was prepared by solgel combution method. The
samples were characterized with X-ray diffraction and TEM
measurements. The effect of Tb3+ cations substitution on
structure of prepared nanoparticles was investigated. From the
analysis, the system was found to be inverse spinel cubic
structure. The lattice parameter (a) changes increases with Tb
doping content. Room temperature DC electrical resistivity
decreases. Dielectric properties have been studied in the
frequency range of 1 kHz to 5 MHz. Permittivity and tangent
loss (tanδ) decreases with the substitution of Tb3+ in parent
crystal structure.
International Refereed Journal of Engineering and Science (IRJES)irjes
a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications.
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
Molecular dynamics (MD) simulations were carried out with a three-body Tersoff potential force field to predict the transversely isotropic elastic properties of pristine and defected BNNTs. This is accomplished by imposing uniaxial tension, twisting moment, in-aplane shear and in-plane biaxial tension to the BNNTs. Effects of various factors such as chirality and diameter of BNNTs, vacancy concentration, and distribution of vacancy pores along the length and circumference of BNNTs were critically examined. Our study reveals that the elastic coefficients of BNNTs decrease as their diameter increase, except axial Young’s modulus. Young’s modulus of BNNT increases with the diameter and reaches its maximum value when the tube diameter is ∼14 Å and then it starts decreasing. We also found that the axial Young’s modulus of a BNNT increases as its aspect ratio increases and stabilizes at a particular value of aspect ratio (L/D ∼ 15). The vacancies greatly affect the elastic properties of BNNTs; for instance, the vacancy concentration of 2% in (10, 10) BNNT reduce its axial Young’s, shear, plane strain bulk and in-plane shear moduli by 14%, 25%, 14% and 18%, respectively. Furthermore, we studied the electronic properties of pristine and defective BNNTs under four transversely isotropic loading conditions using the strain effective method. The results reveal that the electronic properties of BNNTs can be altered via different routes: loadings conditions, diameter and vacancy concentration. Our fundamental study highlights the critical role played by vacancy defected BNNTs in determining their elastic and electronic properties as they are vastly being used in multifarious applications such as nano-electronic devices and reinforcements in multifunctional nanocomposites
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Predicting local atomic structures from X-ray absorption spectroscopy using theory and machine learning
1. Predicting local atomic structures from X-ray absorption
spectroscopy using theory and machine learning
Deyu Lu
Center for Functional Nanomaterials, Brookhaven National Laboratory
AIMS Workshop at NIST
August 1, 2019
2. Experiments supplemented by theory and data analytics
Exp
measurement
Evolution of local
structures and
electronic states,
mechanisms
Structure models
properties
Prior knowledge
Simulations
energy
validation
Constructive
theory
interpretation
in situ exp
database ML workflow
Nat. Comm., 6, 7583 (2015)
• Time consuming
• Human labor
intensive
3. Smart experiments enabled by theory and data analytics
Exp
measurement
Evolution of local
structures and
electronic states,
mechanisms
Property
descriptors
Theory &
Computation
energy data analytics
validation
Real-time
characterization
interpretation
interactive high
throughput in
situ exp database ML workflow
feedback
optimization
automation
Nat. Comm., 6, 7583 (2015)
4. Motivation: Solve the inverse problem
Quantum
Mechanical Laws
Atomistic
Structure
Electronic
Structure
the forward problem
y = f (x)x y
Structure
Descriptors
Electronic
Structure
the inverse problem, often ill-conditioned
x' ⊂ x
x' = !f −1
(y')
y' ⊂ y
5. X-ray absorption spectrum
Fermi’s golden rule:
Dipole app. :
Single particle app. :
Phil. Trans. R. Soc. A 371.1995 (2013)
Core hole final state effect:
Ø valence electron SCF
relaxation (XSpectra)
Ø linear response of
valence electrons
(OCEAN, exciting)
− + −
−
− −
−
6. X-ray absorption spectra
§ Great time/energy resolution
§ Sensitive to electronic structure
§ Sensitive to local symmetry,
coordination number and charge state
Extended X-ray absorption fine structure
X-ray absorption
near edge structure
(XANES)
http://www.ati.ac.at/typo3temp/pics/617d72ef66.png
7. Decipher the spectra-structure relationship
Exp core-level
spectra
Weights of the ref.
spectra
Linear combination
fitting with exp refs.
Ti4+ with 4,5 and 6 CNs
energy
Phys. Rev. B, 56 1809, 1997
Nat. Comm., 6, 7583 (2015)
8. Empirical fingerprints
Phys. Rev. B, 56 1809, 1997
Ti K-edge
XANES
Fe K-edge
XANES
Geochim. Cosmochim. Acta 69, 4315, 2005
Spectral descriptor generation
• Requires prior knowledge
• Subject to human bias
• Strong emphasis on pre-edge
• Difficult to capture more subtle
features
Transferability
• Structure-spectral relationship
is system dependent
9. • We don’t have experimental references.
– Predictive, computational spectroscopy
Emerging Challenges
energy
11. Water splitting over ZnO nanowires
• Wide band gap (3.2 eV), high carrier
mobility
• Seed-mediated growth from aqueous
solution
• Supports water splitting
• Photocorrosion
J. Phys. Chem. C 2013, 117, 13396−13402
12. Ultrathin titania coating layer
• Can sustain photocorrosion
• Higher photo current and energy conversion efficiency
• Reduced the overall surface recombination rate by 40%
ZnO-agT
ZnO-APT
as grown ZnO nanowires
Mingzhao Liu
13. Structure characterization
An unknown structure
• XRD doesn’t show
identifiable peaks,
suggesting amorphous
structure
• Ti L-edge XANES suggests
that crystal domain size is
~ a few nanometers
• Ti K-edge shows very
different features from
rutile or anatase
• Can we learn anything
about its local structure of
amorphous TiO2?
15. Ti K-edge XANES basis sets
4970 4980 4990 5000
(nergy (e9)
Normalizedχµ(()
4c
4970 4980 4990 5000
(nergy (e9)
5c
4970 4980 4990 5000
(nergy (e9)
6c
• 334 site-specific Ti K-edge spectra calculated from structures in the
Materials Project using XSpectra
• Spectra clustered according to the local coordination number (4c: four
coordinated; 5c: five coordinated and 6c: six coordinated); each
cluster is represented by an average spectrum.
• A large number of Ti6c spectra are distinct essentially only in the pre-edge
region and cannot be separated in the clustering step; we choose
representative spectra for Ti6c sites by hand, considering experimentally
available structures.
• Total 11 Ti4+ K-edge XANES basis sets
17. Conclusion
• The quality of ALD titania coatings over ZnO nanowires is strongly
correlated to the post-processing procedures performed on the NWs,
including thermal annealing and plasma sputtering.
• Ti L-edge XANES studies suggest that the titania shell is highly amorphous
with crystalline domains limited to a size of 1 nm or smaller.
• Ti K-edge XANES studies at high spectral resolution indicate that the
titania shell over ZnO has a significantly different structure from those of
crystalline TiO2.
• Two different first-principle computational approaches to analyze the Ti
K-edge data arrive at the same conclusion that the experimental
spectrum can be satisfactorily fitted only by introducing a large fraction
(40~50%) of undercoordinated Ti atoms.
• Trends in the intensity ratio between the white line doublet in the Ti K-
edge spectra measured for different post-processing procedures point to a
lower oxidation state in the titania shell due to the plasma sputtering of
the ZnO cores.
D. Yan, M. Topsakal, S. Selcuk, J. L. Lyons, W. Zhang, Q. Wu, I. Waluyo, E. Stavitski, K. Attenkofer, S.
Yoo, M. S. Hybertsen, DL, D. J. Stacchiola and M. Liu,, Nano Lett. 19:6, 3457-3463 (2019)
18. energy
• We don’t have experimental references.
– Predictive, computational spectroscopy
• Vast chemical and configurational space.
– Database and machine learning
– Avoid human bias
Emerging Challenges
energy
19. 3D metal nanoparticle structure
determination from XANES
Anatoly Frenkel Janis Timoshenko
§ Challenge: None of the traditional methods
can determine MNP structures on-the-fly.
§ Can we find good structure descriptors?
Frenkel, J. Synch. Radiat. 6, 293 (1999)
Frenkel, Hills, and Nuzzo, J. Phys. Chem. B,
105, 12689 (2001)
Nano particle size can be determined from
average coordination numbers.
21. Training set using artificial NP structures
Physical cluster
(~20)
Artificial cluster
from mixed sites
(~C60
3=34,200)
Training Set
Nonequiv. Pt sites
(~60)
22. Determine average coordination
number from machine learning
Feature
site specific Pt
L3-edge XANES
Target
average (C1~C4)
artificial neural network Prediction
NP size/shape
24. Prediction of Pt NP structure
Sample !!
a
!!
a
!!
a
!!
a dTEM
a,b
(nm) Model NPs CNsc
Model NPs sizec
(nm)
Foil 11.6(2) 5.8(2) 23(1) 11.1(8) - {12, 6, 24, 12} ∞
A3 9.1(3) 4.3(3) 11(2) 8(1) 3(1) {9.4, 4.0, 14.4, 7.1} 2.8
S4 8.9(3) 4.2(4) 10(2) 7(1) 1.2(2) {8.5, 3.2, 11.5, 5.0} 1.2
S2 8.1(3) 3.7(4) 8(2) 4.5(8) 1.2(3) {7.8, 3.3, 9.6, 4.1} 1.1
S3 7.7(4) 3.8(4) 4(2) 3.9(9) 0.9(2) {7.7, 3.1, 9.2, 3.8} 1.1
S1 7.4(4) 2.0(3) 3(1) 6(1) 1.1(2) {7.4, 2.6, 8.0, 3.3} 1.2
A2 6.6(4) 2.3(4) 3(1) 5(1) 1.1(3) {6.6, 2.1, 6.0, 2.9} 1.4
A1 6.3(3) 1.5(3) 2(1) 5(1) 0.9(2) {6.2, 1.9, 5.1, 2.4} 1.1
J. Timoshenko, DL, Y. Lin and A. I. Frenkel,, J. Phys. Chem. Lett., 8, 5091, 2017
J. Timoshenko, A. Anspoks, A. Cintins, A. Kuzmin, J. Purans, and A. I. Frenkel, Phys. Rev. Lett.
120, 225502, 2018
25. Summary
Ø Real time interpretation of core-level spectra is an emerging
challenge in operando measurements that requires correlating
operando measured spectral features to key local structure motifs,
i.e. solving the inverse problem, since standard fingerprints do not
exist.
Ø Ab initio X-ray absorption near edge structure (XANES) modeling is
a good complement of extend X-ray absorption fine structure
(EXAFS), for structural refinement.
Ø Our method enables the inverse modeling, where the unknown
structural motifs are deciphered from the experimental spectra.
Ø We illustrate our approach by 3D structure determination of metal
nanoparticles using neural network.
Ø Combination of theory, database and data analytics tools can have
a huge impact on materials discovery.
26. Local structure classification
from XANES using ML
Phys. Rev. B, 56 1809, 1997
Ti K-edge
XANES
• Avoid human bias
• Go beyond pre-edge features
• Extract spectral features automatically
• Regulate the ill-condition of fitting
through the loss function
• Has better transferability over materials
classes
• Provide real time feedback
Matthew Carbone Mehmet Topsakal
31. Conclusion
• XANES encodes important information about the local chemical
environment of an absorbing atom (e.g. coordination number,
symmetry and oxidation state).
• Extract such information is akin to solving a challenging inverse
problem
• The robustness and fidelity of the machine learning method are
demonstrated by an average 86% accuracy across the wide
chemical space of oxides in eight 3d transition metal families using
FEFF spectra database
• Spectral features beyond the pre-edge region play an important
role in the local structure classification problem, especially for late
3d transition metal elements.
• This study is a precursor to a potentially very powerful tool for real
time structure refinement using experimental XANES.
Carbone, Yoo, Topsakal and D.L. Phys. Rev. Mater, 3, 033604 (2019). Editor’s Suggestion.
32. Acknowledgment
This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility,
at Brookhaven National Laboratory under Contract No. DE-SC0012704. MC is supported by Computational Sciences
Graduate Fellowship (DOE CSGF) under Grant No. DE-FG02-97ER25308.
Experiment
• Anatoly Frenkel (BNL / SBU)
• Mingzhao Liu (BNL)
• Dario Stacchiola (BNL)
• Klaus Attenkhofer (ALBA)
• Eli Stavitski (BNL)
Theory
• Matt Carbone (Columbia)
• Mehmet Topsakal (BNL)
• Sencer Selcuk (Google)
• Mark Hybertsen (BNL)
• Xiaohui Qu (BNL)
• John Vinson (NIST)
Machine Learning
• Shinjae Yoo (BNL)
• Yuewei Lin (BNL)
