Sandia National Laboratories is developing SNAP (Spectral Neighbor Analysis Potential) potentials for molecular dynamics simulations. SNAP potentials are fitted to quantum mechanical data using bispectrum components that describe the local atomic environments. SNAP potentials have been shown to accurately reproduce properties of tantalum, including liquid structure and screw dislocation behavior not included in the training data. Work is ongoing to develop multi-element SNAP potentials, including for tungsten-beryllium alloys relevant to modeling plasma-surface interactions in nuclear fusion reactors.
Perovskites-based Solar Cells: The challenge of material choice for p-i-n per...Akinola Oyedele
Perovskite-based PV have triggered widespread interest in the scientific community because these materials offer the attractive combinations of low cost and theoretically high efficiency. However, several challenges must be overcome for these relatively new PV materials. Among the many important challenges, one is the choice of materials to be used in thin film PV devices..
Based on fundamental principles of solar photovoltaics, this problem focuses on two aspects of the perovskite system:
1) Based on a planar p-i-n device structure, a potential list of p- and n-type charge collecting layers as well as the conductive contacts that could be used with a promising perovskite absorber material was identified, and a proper justification for the selection of each material in the device was given.
2) Three theoretical p-i-n type solar cells were made with the chosen materials and appropriate conductive contacts.
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
This presentation summarizes history and recent development of perovskite solar cells. If you have any questions or comments, you can reach me at agassifeng@gmail.com
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
Perovskites-based Solar Cells: The challenge of material choice for p-i-n per...Akinola Oyedele
Perovskite-based PV have triggered widespread interest in the scientific community because these materials offer the attractive combinations of low cost and theoretically high efficiency. However, several challenges must be overcome for these relatively new PV materials. Among the many important challenges, one is the choice of materials to be used in thin film PV devices..
Based on fundamental principles of solar photovoltaics, this problem focuses on two aspects of the perovskite system:
1) Based on a planar p-i-n device structure, a potential list of p- and n-type charge collecting layers as well as the conductive contacts that could be used with a promising perovskite absorber material was identified, and a proper justification for the selection of each material in the device was given.
2) Three theoretical p-i-n type solar cells were made with the chosen materials and appropriate conductive contacts.
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
This presentation summarizes history and recent development of perovskite solar cells. If you have any questions or comments, you can reach me at agassifeng@gmail.com
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
Real-Time Analysis of Streaming Synchotron Data: SCinet SC19 Technology Chall...Globus
This project, which involved streaming light source data from the SC19 show floor to Argonne’s Leadership Computing Facility (ALCF) outside Chicago, won the top prize at the inaugural SCinet Technology Challenge at SC19 in Denver, CO.
[Slides] Using deep recurrent neural network for direct beam solar irradiance...Maosi Chen
SPIE slides
If you are interested in the corresponding manuscript, please visit http://dx.doi.org/10.1117/12.2273364
or send me (maosi.chen@colostate.edu) an email to request a copy for personal use.
NMR Random Coil Index & Protein Dynamics. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, Mark Berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, web server, Wishart group, protein dynamics, NMR dynamics, protein flexibility, accessible surface area, RCI, random coil index, order parameter, bruker, jeol
(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.
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.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Automated Generation of High-accuracy Interatomic Potentials Using Quantum Data
1. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly
owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
SNAP: Automated Generation of High-accuracy
Interatomic Potentials Using Quantum Data
Aidan Thompson
Center for Computing Research,
Sandia National Laboratories,
Albuquerque, New Mexico
Approved for public release under SAND2018-2573 C, SAND2018-2067 C
3. Outline of This Talk
3
Introduction
• LAMMPS
• Interatomic Potentials
SNAP Potentials
• Structure
• Accuracy
• Computational Performance
• Future Work
Conclusions
4. What is Molecular Dynamics Simulation?
4
Distance
Time
Å m
10-15syears
QM
MD
MESO
Design
MD Engine
HNS
atoms,
positions,
velocities
interatomic potential
Positions, velocities
and forces at many
later times
• Continuum models require underlying
models of the materials behavior
• Quantum methods can provide very
complete description for 100s of atoms
• Molecular Dynamics acts as the “missing
link”
• Bridges between quantum and continuum
models
• Moreover, extends quantum accuracy to
continuum length scales; retaining atomistic
information
constraints
5. What is Molecular Dynamics Simulation?
Time
Å
10-15s
QM
MD
MESO
Design
Distance
6. What is Molecular Dynamics Simulation?
6
Distance
Time
Å m
10-15syears
QM
MD
MESO
Design
MD Engine
HNS
atoms,
positions,
velocities
interatomic potential
Positions, velocities
and forces at many
later times
• Continuum models require underlying
models of the materials behavior
• Quantum methods can provide very
complete description for 100s of atoms
• Molecular Dynamics acts as the “missing
link”
• Bridges between quantum and continuum
models
• Moreover, extends quantum accuracy to
continuum length scales; retaining atomistic
information
constraints
7. What is Molecular Dynamics Simulation?
7
Distance
Time
Å m
10-15syears
QM
MD
MESO
Design
• Continuum models require underlying
models of the materials behavior
• Quantum methods can provide very
complete description for 100s of atoms
• Molecular Dynamics acts as the “missing
link”
• Bridges between quantum and continuum
models
• Moreover, extends quantum accuracy to
continuum length scales; retaining atomistic
information
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Thanks to Aidan Thompson
F=ma
Large-scale Atomic/Molecular
Massively Parallel Simulator
•" Biomolecules
•" Polymers (soft
materials)
•" Materials science
(hard materials)
•" Mesoscale to
continuum
Mike
Chandross
8. Historical Development for Potentials
Moore’s Law for Interatomic Potentials
Plimpton and Thompson, MRS Bulletin (2012).
Moore’s Law for potentials
CHARMm
EAM
Stillinger-Weber
Tersoff
AIREBO
MEAM
ReaxFF eFF
COMB
EIM
BOP
GAP
REBO
SPC/E
1980 1990 2000 2010
Year Published
10
-6
10
-5
10
-4
10
-3
10
-2
Cost[core-sec/atom-timestep]
Old
SNAP
New
SNAP
8
Twobody (B.C.)
Lennard-Jones
Hard Sphere
Coulomb
Bonded
Manybody (1980s)
Stillinger-Weber
Tersoff
Embedded Atom Method
Advanced (90s-2000s)
REBO
BOP
COMB
ReaxFF
Big/Deep/Machine
Data/Learning (2010s)
GAP, SNAP, NN,…
Drivers
• Increased computer resources
• Application to Real Materials
• Quantum Methods
9. Historical Development for Potentials
9
Twobody (B.C.)
Lennard-Jones
Hard Sphere
Coulomb
Bonded
Manybody (1980s)
Stillinger-Weber
Tersoff
Embedded Atom Method
Advanced (90s-2000s)
REBO
BOP
COMB
ReaxFF
Big/Deep/Machine
Data/Learning (2010s)
GAP, SNAP, NN,…
Computational Cost
Error
LJ
EAM
MEAM
SNAP GAP
J.Chem.Phys. 148, 241401 (2018): Special Topic on
Data-Enabled Theoretical Chemistry
Guest edited by Rupp, von Lilienfeld, and Burke
10. Outline of This Talk
10
Introduction
• LAMMPS
• Interatomic Potentials
SNAP Potentials
• Structure
• Accuracy
• Performance
• Future Work
Conclusions
11. • GAP (Gaussian Approximation Potential): Bartok, Csanyi et al., Phys. Rev. Lett, 2010. Uses
3D neighbor density bispectrum and Gaussian process regression.
• SNAP (Spectral Neighbor Analysis Potential): Our SNAP approach uses GAP’s neighbor
bispectrum, but replaces Gaussian process with linear regression.
- More robust
- Lower computational cost
- Decouples MD speed from training set size
- Enables large training data sets, more bispectrum coefficients
- Straightforward sensitivity analysis
- Equivalent to Gaussian process or kernel ridge regression with dot-product kernel
ESNAP
= Ei
SNAP
i=1
N
∑ + φij
rep
rij( )
j<i
N
∑
Ei
SNAP
= β0 + βkBi
k
k∈ J<Jmax{ }
∑
Geometric
descriptors
of atomic
environments
Energy as a
function of
geometric
descriptors
SNAP: Spectral Neighbor Analysis Potentials
9
Bispectrum
components:
2-, 3-, 4-body
site features
12. SNAP Fitting Process
FitSnap.py
12
Dakota
optimization,
sensitivity
“Hyper-parameters”
• Cutoff distance
• Group Weights
• Number of Terms
• Etc.
fitsnap.py
Communicate with
LAMMPS; weighted
regression to obtain
SNAP coefficients
LAMMPS
Low/High
Throughput
DFT
Metrics
• Force residuals
• Energy residuals
• Elastic constants
• Etc.
Bispectrum
components &
derivatives,
reference potential
13. SNAP Tantalum
• Training data:
• Energy, force, stress
• ~5,000 data points
• Deformed crystals phases
• Generalized stacking faults
• Surfaces
• Liquid
• Excellent agreement with training
data, e.g. Liquid RDF
• Peierls barrier is the activation energy
to move a screw dislocation
• Not included in training data
• SNAP potential agrees well with DFT
calculations
A. P. Thompson , L.P. Swiler, C.R. Trott,
S.M. Foiles, and G.J. Tucker, J. Comp.
Phys., 285 316 (2015) .
13
14. 14
Training SNAP for Alloys
– Tungsten+Beryllium
Elastic Deformations
• ~5400 configurations
DFT-MD Trajectories
• ~3500 configurations
Amorphous
Liquids
Surfaces
• Plasma-surface interactions in ITER
• Tungsten planned divertor material
• Beryllium planned first wall material
• Plasma causes redeposition of Be into W
• The focus of the joint potential
has been on ordered phases of
WBe
• B2(WBe), L12(WBe3), C14(WBe2),
C15(WBe2), C36(WBe2) and
D2b(WBe2)
L12C14
C15
C36
15. 15
Tungsten Properties
Be Elastic Moduli Be Phase Stability Be Defect Formation
Training SNAP for Transferability – WBe
Candidate 18584:
Predicts the correct WBe intermetallic phases
(stable Laves phases, unstable B2 and L12)
Key drawbacks are Be-elastic and W-vacancy
properties.
16. 16
Be Implantation into W surfaces
Preliminary Results17
- MD simulations of 75 eV Be implantation in W at 1000 K in a 10 x 10 x 40
◦ Place Be randomly in x and y direction and run for 3 ps
◦ Output whether Be reflected or implanted and compare with SRIM
MD simulations of 75 eV Be implantation in W
1000 K in a 10 x 10 x 40 box
◦ Random position above surface, initial velocity normal to surface
◦ Run MD for 3 ps, collect statistics over 1000s of trials
◦ Capture rate, implantation depth
◦ Compare with SRIM
17. 0 10 20 30
k
0
1
2
3
4
5
<||∆j
Σβk
B
i
k
||>
B(j1
,j2
,j)
B(j1
,j1
,j)
B(j,0,j)
17
Effect of High-Order Bispectrum Components
• MD simulation of molten tantalum using SNAP Ta06A potential
• Magnitude of average force contributed by each bispectrum
component
18. 18
Effect of High-Order Bispectrum Components
0 5 10 15
Band Limit 2Jmax
0
100
200
300
400
500
600
Cij
[GPa]
C11
C12
C44
0 5 10 15
Band Limit 2Jmax
0
100
200
300
400
500
600
Cij
[GPa]
• Elastic constants for tantalum versus band limit
Linear SNAP Quadratic SNAP
19. 19
Adding Descriptors Increases Cost A Lot
10 100 1000
# SNAP Descriptors (K)
0.01
0.1
1
10
100
1000
Performance[10
3
atom-steps/s]
Intel Haswell
Intel KNL
AMD CPU
IBM PowerPC
y~x
-2
10 100 1000
# SNAP Descriptors (K)
0.01
0.1
1
10
100
1000
Performance[10
3
atom-steps/s]
NVIDIA K20X
NVIDIA P100
y~x
-2
y~x
-2
y~x
-2
y~x
-1
CPU GPU
• Benchmarks for Exascale Computing Project
• Short MD simulation of BCC tungsten @ 300K
• GPU and KNL use the LAMMPS Kokkos package
• 2000 atoms, 1 node
20. What About Adding Quadratic Terms?
• Linear terms are 4-body
• Quadratic terms are 7-body
• Number of linear coefficients grows as O(J3)
• Number of quadratic coefficients grows as = O(J6)
• Energy, force, stress remain linear in b and a
• Can still use linear least squares (SVD)
• Number of columns will increase from K to K(K+1)/2
Wood and Thompson,
J. Chem.Phys., 148 241721 (2018)
https://arxiv.org/abs/1711.11131
SNAP Tantalum
2 meV/atom
5 meV/A
21. What About Adding Quadratic Terms?
• Cross-validation analysis to control for overfitting
• Training and Testing errors for the GSF(110) subset of the DFT data
• All potentials fit to a large, diverse, set of DFT data for tantalum
• 2J=8
GSF(112) Energy GSF(112) Force
22. Can We Improve Multi-Element SNAP?
Etot = Eref +
NX
i=1
Ei
SNAP
Ei
SNAP = ↵ · Bi
, i is element ↵
=
X
{ }
KX
k=1
k,↵ B ,i
k
Fj
SNAP =
X
{ }
KX
k=1
k,↵
NX
i=1
@B ,i
k
@rj
ujmm0 = Ujmm0 (0, 0, 0) +
X
rii0 < Rcut
i0 2
fc(rii0 )w Ujmm0 (✓0, ✓, )
Bj1j2j =
j1X
m1,m0
1= j1
j2X
m2,m0
2= j2
j
X
m,m0= j
(ujmm0 )⇤
H
jmm0
j1m1m0
1
j2m2m0
2
uj1m1m0
1
uj2m2m0
2
1. No need to use w factors
2. For two elements, for each Bk, the 4 variants are BAAA
k , BAAB
k , BABB
k ,
BBBB
Etot = Eref +
NX
i=1
Ei
SNAP
Ei
SNAP = ↵ · Bi
, i is element ↵
=
X
{ }
KX
k=1
k,↵ B ,i
k
Fj
SNAP =
X
{ }
KX
k=1
k,↵
NX
i=1
@B ,i
k
@rj
ujmm0 = Ujmm0 (0, 0, 0) +
X
rii0 < Rcut
i0 2
fc(rii0 )w Ujmm0 (✓0, ✓, )
Bj1j2j =
j1X
m1,m0
1= j1
j2X
m2,m0
2= j2
j
X
m,m0= j
(ujmm0 )⇤
H
jmm0
j1m1m0
1
j2m2m0
2
uj1m1m0
1
uj2m2m0
2
1. No need to use w factors
2. For two elements, for each Bk, the 4 variants are BAAA
k , BAAB
k , BABB
k ,
BBBB
uj
m,m0 = Uj
m,m0 (0, 0, 0) +
X
rii0 <Rcut
fc(rii0 )wiUj
m,m0 (✓0, ✓, ) (4)
The expansion coe cients uj
m,m0 are complex-valued and they are not
directly useful as descriptors, because they are not invariant under rotation
of the polar coordinate frame. However, the following scalar triple products
of expansion coe cients can be shown to be real-valued and invariant under
rotation [7].
Bj1,j2,j =
j1X
m1,m0
1= j1
j2X
m2,m0
2= j2
j
X
m,m0= j
(uj
m,m0 )⇤
H
jmm0
j1m1m0
1
j2m2m0
2
uj1
m1,m0
1
uj2
m2,m0
2
(5)
The constants H
jmm0
j1m1m0
1
j2m2m0
2
are coupling coe cients, analogous to the Clebsch-
Gordan coe cients for rotations on the 2-sphere. These invariants are the
components of the bispectrum. They characterize the strength of density
correlations at three points on the 3-sphere. The lowest-order components
5
Current Multi-element SNAP
All elements lumped together in
density expansion
Proposed Multi-Element SNAP
Elemental
Weight
Elemental
Expansion
Coefficient
Three-Element
Bispectrum
Component
Ei
SNAP = ↵ · Bi
, i is element ↵
=
KX
k=1
k,↵Bi
k
Fj
SNAP =
KX
k=1
k,↵
NX
i=1
@Bi
k
@rj
ujmm0 = Ujmm0 (0, 0, 0) +
X
ri0 < Rcut
fc(ri0 )w Ujmm0 (✓0, ✓, )
Bj1j2j =
j1X
m1,m0
1= j1
j2X
m2,m0
2= j2
j
X
m,m0= j
(ujmm0 )⇤
H
jmm0
j1m1m0
1
j2m2m0
2
uj1m1m0
1
uj2m2m0
2
1. No need to use w factors
2. For two elements, for each Bk, the 4 variants are BAAA
k , BAAB
k , BABB
k ,
BBBB
k
3. For N elements, the number of variants is the number of ways of select-
ing 3 from N with repetition, also called the number of arrangements of
3 stars and N 1 bars, which is N+3 1
3
i.e. 1, 4, 10, 20 = Tetrahedral
number N(N + 1)(N + 2)/6
4. Enumeration pattern is increment rightmost position less than N and
repeat that value in all positions to the right: 111, 112, 113, 122, 123,
133, 222, 223, 333
23. 23
Fully-Automated Generation of SNAP
• Manage QM data
generation
• SimHyperParams (E, V,
N, R0)
• Bootstrapping
• Latin Hypercube
Sampling
• Some user input
required
Trajectory Farm
FitSNAP.py
• SNAP FitHyper
Params (rcut)
• Genetic Algorithm
• Training Errors
• Cross-validation
• Generates new QM data, returns SNAP potentials
24. Conclusions
§ Application needs are driving demand for more accurate potentials
§ SNAP ML potentials balance efficiency and accuracy
§ Lowest order bispectrum components (2Jmax<=6) are most important
§ Adding higher-order bispectrum descriptors does not help/hurt much
§ Quadratic terms improve accuracy in training and out-of-sample testing
§ Ongoing work: more automation and multi-element SNAP
§ Biggest challenge: "good" data, and lots of it
Acknowledgements:
Mitch Wood (SNAP Development)
Mary Alice Cusentino (SNAP Testing)
Steve Plimpton (LAMMPS)
24
26. SNAP GPU Performance
10
0
10
1
10
2
10
3
10
4
10
5
10
6
Number of Atoms
10
1
10
2
10
3
10
4
10
5
10
6
10
7
10
8
Speed(Atom-Timesteps/sec)
LAMMPS running on a CPU machine
1 node (36 x 2.1 GHz Intel Broadwell)
1.2 TFlops peak
50% efficiency
200 atoms/node
50% efficiency
50 atoms/GPU
ExaMiniMD running on a GPU machine
1 GPU (NVIDIA P100)
5.3 TFlops peak
27. 27
Pareto Optimal Potentials
• For material properties of interest within a set
of SNAP potentials
• Increase in accuracy w/ number of bispectrum
components used
• How about when comparing potentials?
• Resources are limited, which is your best choice?
Computational Cost
Errorw.r.t.DFT
LJ
EAM
MEAM
SNAP GAP
28. SNAP Data-Driven Interatomic Potentials for Materials
PI: Aidan Thompson, Mitch Wood (post-doc), many others
SNAP Fitting Process• Quantum (QM) materials calculations can handle 100s of atoms
• Classical molecular dynamics (MD) can handle millions of atoms
• Limited by accuracy of interatomic potentials (IAP)
• Simple potentials (LJ, EAM) good for qualitative behavior
• Machine-learning potentials can approximate QM
• SNAP balances accuracy and cost
• Current Applications:
• Fusion energy materials (EXAALT ECP project, with LANL)
• Phase change kinetics
• Shock mechanics
Distance
Time
Å m
10-15syears
QM
MD
MESO
Design
Computational Cost
Error
L
J
EAM
MEAM
SNAP GA
P
Qualitative
Properties
Near QM
Accuracy
MD simulation of helium bubble
formation near tungsten
surface
29. Two philosophical extremes in the
development of interatomic potential models
• Functional forms based on
fundamental understanding of
electronic origins of bonding
• Bond Order Potentials (BOP)
• Model Generalized Pseudopotential
Theory (MGPT)
• COMB
• ReaxFF
• …
• Gives confidence that it will
interpolate/extrapolate reasonably
• Empirical fit of a flexible functional
form
• Gaussian Approximation Potentials
(GAP)
• Spectral Neighbor Analysis Potential
(SNAP) - this work
• …
• Replaces the need for intuition/art
with extensive computation
• Automate the fitting process?
• Apply across multiple materials classes?
Luke: Is the dark side stronger?
Yoda: No, no no. Quicker, easier,
more seductive.
The Force!
Darth Vader: You underestimate the
power of the dark side!
The Dark Side!
Borrowed from
Stephen Foiles
and Lucasfilm 29