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Materials Discovery Precursor to Progress in Society
Materials Changed Societies and Enabled new Technology:
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Stone → Bronze → Iron
Google
→ … → Silicon Age
wikimedia.org,
images
| 14 Grand Challenges for Humanity in the 21st Century 3 www.engineeringchallenges.org
How are Materials Discovered?
By Luck / Accident:Stainless steel, vulcanized rubber (car tires), Teflon, Play-doh, Saccharin, Super Glue,…
Edisonian (Trial and Error) Approach:
He tested over 6,000 plant materials to discover the final light bulb filament
wikimedia.org,
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Google images
Materials-Discovery over Time
Of all (solid state) materials that we know of today, how many were discovered in the last 10 years?
pollev.com/peterschindler
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Materials-Discovery over Time
Materials in ICSD
Top 500 HPC
Doubles every ~22 yrs
1st : Empirical Science
~33%
2nd: Modelbased Science
Doubles every ~1.3 yrs
3rd: Computational Science
4th: Data-driven Science
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Experiments
Physical Laws
DFT, MD
ML, Clustering
The 3rd Paradigm: Computational Discovery
Bulk modulus
Stress tensor
Surface
Work function
Surface/cleavage energy
Adsorption energy
Magnetic
Magnetic ordering
Magnetic moment
Dielectric constant
Absorption spectra
Density of states
Band structure
Thermodynamic
Vibrational entropy
Phase stability (Hull diagram)
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Bond length 7 2 XC nuc ( ()d()( [()] 2 ) ( ) ) iii j Er r rrrr rrr VE m − −+++= Mechanical
Structural Lattice constants
/ Electrical
Optical
σij “ab
”
-inito
Time Required for Experiment vs. Computation
Experiments (Synthesis) ~ weeks to months (to a Ph.D.)
First principles calculations ~ hours to days (to weeks)
Still too long to screen >100,000 candidates
Discovery Cluster
Over 20,000 CPU Cores and Over 200 GPUs
Hosted at MGHPCC (90,000-square-foot facility)
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The 4th Paradigm: Data-Driven Discovery
Derived or measured properties
Physical & Chemical insight/intuition
* = fingerprint = feature (vector)
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*
Surrogate ML model
Hyperparameter optimization
Adapted from L. Himanen, et al. Comp. Phys. Comm., 247, (2020).
Database
High-Quality Data?
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Types of Materials Data
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11 Text Tshitoyan, V., et al. Nature 571, 95–98 (2019) NLP and LLMs Scientific Literature
Types of Materials Data
XRD, TEM, etc.
NLP and LLMs
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Spectra, Images Experimental Text Micrographs
Oviedo, F., et al. Comput Mater 5, 60 (2019). Ziatdinov, M., et al. Nat Mach Intell 4, (2022).
Scientific Literature
Types of Materials Data
Atomistic simulations
Materials properties
High-throughput computations of properties
Micrographs
XRD, TEM, etc.
Scientific Literature
NLP and LLMs
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Big Data Spectra, Images Experimental Computational Small Data Text
ML Paradigms in Materials Science
Model-centric AI
How change the model/architecture to improve performance?
Shallow ML + feature engineering
Experimental Computational Small Data
Big Data Spectra, Images
Active Learning
Data-centric AI
How systematically change data (x/y) to improve performance?
Big Data “Good Data”
Deep Learning
Experimental Computational Small Data
Big Data Spectra, Images
Experimental Computational Small Data
Big Data Spectra, Images
Transfer Learning
Experimental Computational Small Data
Big Data Spectra, Images
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Todorović, et al. npj Comput Mater 5, 35 (2019) Choudhary & DeCost, npj Comput Mater 7, 185 (2021)
Materials Descriptors
Examples of Data-Driven Discovery
Industrial Perspective
15
Crystal Structures vs. Molecules
Crystal
Coordinates
Atom types
Lattice vectors
Molecule
Coordinates
Atom types
Periodic
Space group symmetry
Non-periodic
Point group symmetry
E(3) invariant
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Requirements for an Ideal Materials Descriptor
i. Meaningful and Universal (and fixed in number)
ii. Compact and Cheap(er) to Compute
iii. Invariant Under Crystal Symmetries (and atom permutations)
iv. Continuous (small change in atomic structure = small change in descriptor)
v. Reversible
vi. Unique
vii. Additive
viii. Uncorrelated
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Musil et al., Chem. Rev. 2021
Hierarchy of Materials Descriptors
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CGCNN
Crystal Graph Convolutional NN
ALIGNN
Atomistic Line Graph NN
Others: M3GNet, SchNet, PointNet, PAINN, DimeNet++, …
Invariant to E(3)
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Graph Convolutional NNs
T. Xie and J. C. Grossman, Phys. Rev. Lett. 120 (2018)
Choudhary, K. and DeCost, B. npj Comput Mater 7 (2021)
E(3) Equivariant GNNs
Requires data augmentation
(inefficient & not physical)
No additional data required
Improved transferability and data efficiency
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E(3) Equivariant GNNs
Interested in the math/CS details?
→ Prof. Robin Walters at Northeastern (Khoury College)
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Batzner, S., et al. Nat. Comm. 13 (2022)
Prof. Boris Kozinsky and Dr. Simon Batzner
“Affordable Accuracy”
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HIV Capsid with 44 Million Atoms
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Dr. Simon Batzner
24 Data-driven Discovery of Ultra-low Work Function Materials Data-driven Discovery of High-Brightness Photocathodes E.R. Antoniuk, Y. Yue, Y. Zhou, P. Schindler, et al. Physical Review B, 101 (2020) E.R. Antoniuk, P. Schindler, et al. Advanced Materials, 33, 44 (2021) Materials Descriptors Examples of Data-Driven Discovery Industrial Perspective P. Schindler, et al. (in preparation), preprint: arXiv:2011.10905
Majority of Energy Goes to Waste(-Heat)
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Thermionic Energy Converter (TEC)
Vacuum gap
Heat Input
• No moving parts
• Power output scales with area
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Cathode Anode Load
TEC Efficiency
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High-Throughput DFT Workflow
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P. Schindler, et al. (under preparation)
Work Function Database
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P. Schindler, et al. (in preparation)
Model Performance with Physics-motivated Descriptors
Elemental features
200 features
15 features
~105 faster than DFT
Structural features
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χ 1 / r Eionization nmendeleev
P. Schindler, et al. (in preparation)
Promising New Low Work Function Surfaces
After ionic relaxation: Discovery of metallic surfaces with WF < 1.5 eV:
CsScCl3, Hexagonal, (100) Surface
BaX [X=Si, Sn, Ge], Orthorhombic, (110) Surface
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P. Schindler, et al. (in preparation)
32 Data-driven Discovery of Ultra-low Work Function Materials Data-driven Discovery of High-Brightness Photocathodes E.R. Antoniuk, Y. Yue, Y. Zhou, P. Schindler, et al. Physical Review B, 101 (2020) E.R. Antoniuk, P. Schindler, et al. Advanced Materials, 33, 44 (2021) Materials Descriptors Examples of Data-Driven Discovery Industrial Perspective P. Schindler, et al. (in preparation), preprint: arXiv:2011.10905
Discovery of New High-Brightness Photocathodes for XFEL
Electron emission from Photocathodes Depends on Work Function
Work Function
Photocathode Brightness ∝ 1 / spread in transverse momentum of electrons
Intrinsic Emittance
Physical Review B, 101 (2020).
E.R.Antoniuk, Y. Yue, Y. Zhou, P. Schindler, W.A. Schroeder, B. Dunham, P. Pianetta, T. Vecchione, & E.J. Reed. Generalizable DFT-based photoemission model for the accelerated development of photocathodes and other photoemissive devices
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Ab-initio Photoemission Model
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Physical Review B, 101 (2020). E.R.Antoniuk, Y. Yue, Y. Zhou, P. Schindler, W.A. Schroeder, B. Dunham, P. Pianetta, T. Vecchione, & E.J. Reed.
Novel Ultra-bright and Air-Stable Photocathodes
Discovered through ML/DFT Driven Screening
11 materials with intrinsic emittance < 0.3 µm/mm
+ 3 air stable low intrinsic emittance materials M2O (M = Na, K, Rb)
Advanced Materials, 33, 44 (2021)
E. R. Antoniuk, P. Schindler, W. A. Schroeder, B. Dunham, P. Pianetta, T. Vecchione, and E. J. Reed
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Materials Descriptors
Examples of Data-Driven Discovery
Industrial Perspective
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“Materials Informatics” in Industry?
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Industry Perspective: Aionics Inc.
Structure generator leveraging AI potentials to construct crystals, surfaces, molecules, etc.
Database of 10B+ candidates, searchable by physical properties, safety, supply chain, price, etc.
Synthesize New Formulations
AI platform to incorporate latest data, train new model, and guide next selection
Cloud-based DFT to compute properties of candidate
Source at Production Scale
Co-innovation partnerships with electrochemical materials manufacturers:
Aionics tools used internally to lead client’s in-house R&D
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Physics-informed ML & ML-informed Physics
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| Acknowledgments 40 Curious? Write me: p.schindler@northeastern.edu www.d2r2group.com The late Prof. Evan Reed • The D2R2 Group members • Reed Group at Stanford • Aionics Inc. • Mentors and collaborators Prof. Ricardo Baeza-Yates, Liz Roderick, and EAI Team!
Thank you for Listening! Questions? 41 Publications and Contact Info: d2r2group.com