MRS Spring & Fall Virtual Meeting 2020
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The document discusses using machine learning to design point defects in 2D materials for applications in quantum information science and nonvolatile memory. It describes using deep transfer learning on over 100,000 defect structures to predict defect properties like formation energy and electronic structure. This high-throughput screening identified over 100 quantum emitters and 15 atomically thin memristors. The document concludes that layered metal chalcogenides, hexagonal nitrides, and metal halides are promising materials for defects that could enable quantum emitters and nonvolatile resistive memory.
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MRS Spring & Fall Virtual Meeting 2020
- 1. Machine Learning-Enabled Design of Point Defects in 2D Materials Nathan C. Frey Shenoy Group, Materials Science & Engineering University of Pennsylvania 2020 MRS Spring/Fall Meeting n.frey@seas.upenn.edu
- 2. How do we engineer defect states that are electrically, magnetically, and optically addressable in solid- state devices?
- 3. Why layered materials? 3(EPFL/G.Pizzi) Tunable electronic, magnetic, topological properties Low-power, ultra-compact, flexible Heterostructure and integrated solid- state devices n.frey@seas.upenn.edu
- 4. Point defects in 2D materials 4 Atomically thin resistive memory (atomristors) Deji Akinwande (UT Austin); Pawel Latawiec (Harvard University) Quantum emitters n.frey@seas.upenn.edu
- 5. Deep transfer learning in 2D 5 Pretrained Model ~104 – 105 Bulk Crystals ~103 2D Materials 𝐸𝑓 𝑚𝑎𝑡 , 𝐸 𝑏𝑔, 𝐸 𝐹𝑒𝑟𝑚𝑖 Deep Transfer Learning 2D Structure Graphs B N Mo S 𝑅2 = 0.98 𝑀𝐴𝐸 = 0.06 Predicted Ef mat (eV/atom) TrueEf mat(eV/atom) n.frey@seas.upenn.edu
- 6. High-throughput screening 6 150 materials x 70 defects ≈ 10,000 defect structures Ni Cu Pd Ag Al Au Electronic Structure Machine Learning Engineered Point Defects ~1,000 Band Structures ~100 A BB B B MTarget 1 Formation/binding energy 𝐸 𝒌 𝐸 𝑏𝑔Target 2 Defect level position n.frey@seas.upenn.edu
- 7. Machine learning defect properties 7 Conduction Band Valence Band Δ𝐶𝐵 > 𝑘 𝐵 𝑇 Δ𝑉𝐵 > 𝑘 𝐵 𝑇 Target 1 Target 2 ⋅⋅⋅ Random Forest Model Host Material Properties Defect Properties Average All Predictions Tree 1 Tree n Prediction 1 Prediction n Prediction i 𝑟𝑆𝑛 ≫ 𝑟𝑆𝑒 𝜒 𝑆𝑛 ≪ 𝜒 𝑆𝑒 Sn Se n.frey@seas.upenn.edu
- 8. Minimal physical model of defects 8 M 𝜖 1 𝑟 e- 𝐸 𝐸𝜖 Strain terms Electrostatic terms 𝜖 𝑞2/𝑟 0.67 eV MAE for defect formation energy 0.93 F1 score for shallow vs deep defect level classifier n.frey@seas.upenn.edu
- 9. Quantum emitter defect score 9 𝐸 𝑏𝑔, Stability SOC, 𝐸𝑓 0.7 1 PAl, GaAl, NAl, AsAl, SbAl AlN SnGe, SeS GeS AsI MgI2 n.frey@seas.upenn.edu
- 10. Nonvolatile resistive memory score 10 High stability Low switching voltage 100+ quantum emitters 15 atomically thin memristors n.frey@seas.upenn.edu
- 11. Takeaways 11 1 Point defects in 2D materials are platforms for quantum information science and nonvolatile memory. 2 Layered metal chalcogenides, hexagonal nitrides, and metal halides including GeS, h-AlN, and MgI2 are promising, unexplored systems. n.frey@seas.upenn.edu
- 12. Relevant publications 12 http://bit.ly/ncfreygs [1] N.C. Frey, D. Akinwande, D. Jariwala, and V.B. Shenoy, ACS Nano (2020) https://doi.org/10.1021/acsnano.0c05267. n.frey@seas.upenn.edu
- 13. Thank you! 13 nc_frey ncfrey ncfrey n.frey@seas.upenn.edu* ContactContact Shenoy Group Deep Jariwala Deji Akinwande Collaborators Funding