Citrine Informatics. Success stories for AI-assisted materials design. Citrine Informatics. The data analytics platform for the physical world

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1 Citrine Informatics The data analytics platform for the physical world Success stories for AI-assisted materials design Chris Borg 5 February 2018

2 Introductions 1. Name, department, advisor/group 2. Topic of your current research project. 3. Do you think AI/ML could assist in your research?

3 Today s topics 1. What is the focus of MIDDMI? 2. What role does AI have in materials science? 3. Examples of AI-assisted research 4. Example of deep learning on Citrination

4 Today s topics 1. What is the focus of MIDDMI? 2. What role does AI have in materials science? 3. Examples of AI-assisted research 4. Example of deep learning on Citrination

5 MIDDMI Spring 2018 MIDDMI = Mines Initiative for Data-Driven Materials Innovation Collaboration between Mines and Citrine focused on using AI to assist in the design and processing of new materials. Support:

6 The core foundations of MIDDMI projects Some projects will focus on all four foundations Some projects will focus on a subset of foundations

7 Today s topics 1. What is the focus of MIDDMI? 2. What role does AI have in materials science? 3. Examples of AI-assisted research 4. Example of deep learning on Citrination

8 What AI Can and Cannot Do Tell me how to make aluminum alloys 3d-printable = Write a Nature paper for me Not realistic! Which alloy(s)? Which applications? What does 3d printable actually mean? Property targets? What are the constraints? Example: At what cost?

9 A Realistic Role for AI in Materials Science Should we abandon known theory and domain knowledge, and try to outsource scientific thinking to AI? Absolutely not! AI can be a very powerful copilot in the hands of domain experts, helping them much more rapidly answer difficult scientific questions by harnessing large volumes of data àcentaur materials science

10 Centaur Materials Science Human and Machine > Human or Machine

11 Today s topics 1. What is the focus of MIDDMI? 2. What role does AI have in materials science? 3. Examples of AI-assisted research 4. Example of deep learning on Citrination

12 Centaur Materials Science in Action Paper reports on targeted microstructure engineering 3d-printed unmodified Al 6061 After nanoparticle grain refinement

13 Al7075 powder with TiB 2 nanoparticles Schematic representation of epitaxial growth

14 Setting up an optimization problem Nanoparticle compositions targeted to each alloy were selected using [Citrination] that identifies matching crystallographic lattice spacing and density to provide a low-energy nucleation barrier on the basis of classical nucleation theory (Fig. 2f). The software analyzed more than 4,500 different powder and nanoparticle combinations corresponding to more than 11.5 million matching pairs. Potential matches were sorted by a combined set of constraints: minimized lattice misfit, similar atomic packing along matched crystallographic planes, thermodynamic stability in the desired alloy, and availability. JH Martin et al., Nature (2017)

15 Applying known theory is important Hypothesize Test Iterate Driven by experts domain knowledge We can solve the Al 3d printing problem with targeted grain refinement Classical nucleation theory gives us clear guidelines for the materials we need Driven by Citrination and data We need to evaluate millions of candidate grain refiner + alloy combinations using our guidelines from theory Evaluation involves predictive optimization through large quantity of crystallographic, thermodynamic, and economic data

16 Results Unmodified highstrength Al alloy Modified highstrength Al alloy Existing 3d-printable Al alloy Using informatics was key The way metallurgy used to be done was by farming the periodic table for alloying elements and testing mostly with trial and error. [Citrine s] big data analysis narrowed the field of available materials from hundreds of thousands to a select few. We went from a haystack to a handful of possible needles. -B Yahata, HRL Laboratories JH Martin et al., Nature (2017)

17 A prediction engine for Heusler compounds given enough examples (training data) and informative numerical representation of the materials (descriptors), a machine-learning algorithm can determine patterns to predict how unseen examples will behave. Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

18 Structure of Heusler compounds CsCl-type Pm3/m Heusler Fm3/m Inverse Heusler F4/3m Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

19 Good models consist of three components 1. High quality training data 2. Carefully chosen set of chemical descriptors 3. Machine learning algorithm

20 Importance of high-quality training data Training set: 1948 compounds 341 Heuslers 225 NaFeO2-type 94 unique structures Phase criteria: do not contain H, noble gases, or Z > 83 exact stoichiometry 3 components thermodynamically stable More complex patterns demand more examples. Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

21 Good models are dependent on material descriptors. Careful choice of a descriptor set takes advantage of prior knowledge to identify where the pattern is, allowing the algorithm to then determine what the pattern is. Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

22 Correct choice of machine learning algorithm the random forest algorithm incorporates the different trends found by each tree, resulting in a complex and robust model Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

23 Prediction engine successfully identified new Hesulers Scientific community discovers ~50 Heuslers/year 12 in one paper! Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

24 Models Discriminate Similar Structures Heusler vs other Heusler vs inverse Oliynyk et al., Chem. Mater., 2016, 28 (20), pp

25 Today s topics 1. What is the focus of MIDDMI? 2. What role does AI have in materials science? 3. Examples of AI-assisted research 4. Example of deep learning on Citrination

26 Deep Learning for Representing Microstructure SEM Image Deep Learning Featurization Vector of Textures Predictions 26

27 Example: Steel Quality Control We are trying to produce highly ductile steel in the spheroidite phase Occasionally our manufacturing process produces more brittle steel with some pearlite or widmanstatten phase We want to automatically detect the off-nominal batches via SEM imaging Nominal Off-Nominal

28 Deep learning requires large volumes of labeled data In order to train the machine learning algorithm, a dataset with known microstructures is required

29 Automated Microstructure Classification

30 Model Accuracy Actual Phase(s) widmanstätten + spheroidite spheroidite pearlite + spheroidite 84% classified correctly Most common mistake: predict spheroidite for W+S Predicted Phase(s)

31 Resources Demos on citrination.org - Sequential learning, polymers, Ni superalloys MIDDMI website - mines.edu/middmi bit.ly/middmi1 Chris s contact info: - cborg@citrine.io / cborg@mines.edu - Office: Brown W410I

32 Citrine Informatics The data analytics platform for the physical world Thank you