Successfully reported this slideshow.
Your SlideShare is downloading. ×

AI at Scale for Materials and Chemistry

Loading in …3

Check these out next

1 of 16 Ad

More Related Content

Slideshows for you (20)

Similar to AI at Scale for Materials and Chemistry (20)


More from Ian Foster (20)

Recently uploaded (20)


AI at Scale for Materials and Chemistry

  1. 1. AI at Scale for Materials (and Chemistry) IAN FOSTER Director, Data Science and Learning Division AI for Science Town Hall, Argonne National Laboratory, July 22, 2019
  2. 2. Materials innovation is central to meeting energy challenges Crosscuts the energy technology portfolio, from energy generation and storage to delivery and end use Foundation of every clean energy innovation: advanced batteries, solar cells, low-energy semiconductors, thermal storage, coatings, and catalysts for the conversion, capture, and use of CO2, etc.
  3. 3. Space of potential materials is inordinately large Key properties such as stability, synthesizability, and toxicity are hard to determine Quantum mechanics cannot be computed precisely and efficiently Both experiments and simulations are expensive Experiments generate large data that cannot easily be interpreted But materials innovation is difficult
  4. 4. A grand challenge for science: Building a Materials Acceleration Platform 4 “[In the clean energy context] perhaps of greatest interest to the theoretical physics, physical chemistry, and materials science communities that are working alongside the machine learning, robotics, and next-generation computing communities is the challenge of developing clean energy materials. The goal is to provide an integrated end-to-end materials innovation approach, or platform, to deliver the mix of solutions … [We must] spur the interest of the creative community in what is now the most urgent series of demands facing humanity. The opportunities are, simply, immense.” – Sir David King* * David King, “Global clean energy in 2017,” Science 355 (6321), 111, DOI: 10.1126/science.aam7088
  5. 5. Six elements of a Materials Acceleration Platform 1) “Self-driving laboratories” that design, perform, and interpret experiments in an automated way 2) The development of specific forms of AI for materials discovery 3) Modular materials robotics platforms that can be assemblies of modular building blocks for synthesis and characterization 4) Research into computational methods for inverse design 5) New methodologies for bridging the length and timescales associated with materials simulation 6) Sophisticated data infrastructure and interchange platforms 5Report: Materials Acceleration Platform, 2017 –
  6. 6. Example opportunities AI-assisted multi-fidelity scale bridging – Accelerate numerical simulation by using AI to learn low-cost models of finer-grained behavior AI assistant to understand and design complex measurements – Cross-modal image learning across scales, integrated/in-situ AI within experimental processes, automated anomaly detection AI-driven materials design – Integrate data at scales not feasible for human experts, to construct expert assistants for material designers—or even fully automated design pipelines DOI:10.1021/acs.jpcc.8b09917 DOI: 10.1126/sciadv.aaq1566
  7. 7. AI-assisted multi-fidelity scale bridging “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble. It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed.” -- Paul Dirac, 1929 7 Hierarchy of numerical approximations to Schrödinger’s equation. ~25% of NERSC time is DFT; another 20% is MD and QC.
  8. 8. Ultra-fast simulation of complex systems Use massive data, AI, and exascale computing to define task-specific approximations to quantum mechanics H. Chan et al., 2019, DOI:10.1021/acs.jpcc.8b09917
  9. 9. Kinetic model Sample N Y Fix model New sample Products Adjust model Match? Species detected Species predicted Pyrolysis reactor Credit: Kirill Prozument and AI Task Force AI-driven understanding of flame spray pyrolyis
  10. 10. 10 Human intuition Pure or applied problem Extant knowledge “Design a material that does X and that is synthesizable, cheap, non-toxic, stable, biodegradable, …” Materials science and chemistry as search (in a huge space) Design Synthesis Character- ization
  11. 11. 11 Expand knowledge: e.g., machine reading Automated navigation of chemical, functional, synthesis spaces Active/reinforcement learning to choose next experiment or simulation AI for materials characterization: choose measurement(s), infer properties Ren et al. Sci Adv. (2017) eaaq1566 A new era of discovery based on high-throughput, AI-guided search High-throughput experiment / simulation Eli Rotenberg,
  12. 12. Use exascale computing and automated experiments to identify promising candidates Designing new electrolytes Trillions of candidates Autoencoder to expand and navigate materials space Continuously updated predictive models of materials properties Supercomputer simulations Laboratory experiments Active learning used to direct simulation and experiment Important properties: redox potential, stability, toxicity, solubility, synthesizability, …
  13. 13. Materials data: diffuse, sparse, heterogeneous arXiv:1907.05644
  14. 14. 14 Chart Source and Method: Exponential growth in machine learning publications
  15. 15. • Data: Capture, organize, analyze, share move and deliver data • Compute: Accessible at many scales, types, and locations • Models: Discoverable, described, and portable to wherever data and/or computer are located • Workflows: Easily discovered, adapted, composed, scaled, and reused Needed: A cohesive infrastructure for AI-driven (materials) science Encompassing: Models and Workflows Data Computations & Experiments
  16. 16. These are just a few examples of the many opportunities for AI in materials and chemistry Progress requires much work in applications, learning systems, foundations, hardware. Resources: Materials Acceleration Platform workshop Computing Community Consortium AI Roadmap --

Editor's Notes

  • Discover new approximations to quantum mechanics suitable for problems of interest
  • Rotational spectra of multiple reaction products measured
    Artificial neural networks convert spectra to chemical species
    Kinetic model predicts the species
    Comparison of experiment and model guides discovery of missing reaction mechanisms and corrects rates in the model

    Rotational constants (moments of inertia) of reaction products
  • Characterization
  • Expand knowledge: e.g., machine reading
    Autoencoders to navigate chemical, functional, synthesis spaces
    Active/reinforcement learning to choose next experiment or simulation
    AI for materials characterization: choose measurement(s), infer properties