DATA - DRIVEN DISCOVERYgjc29/talks/WebinarSeven...Incorporate uncertainty to design alloys with the...
Transcript of DATA - DRIVEN DISCOVERYgjc29/talks/WebinarSeven...Incorporate uncertainty to design alloys with the...
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DATA - DRIVEN DISCOVERY
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7 examples of how materials & chemicals companies innovate using Alchemite machine learning
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1. Alloys in aerospace2. Batteries3. Antimalarial drug discovery4. 3D printing
5. Data validation
6. Forging hammers
7. Alkanes for lubricants
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Alloys in aerospace
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Challenge
Alloys must have good performance at high temperatures
Commercial superalloys do not have the optimal balance of properties
Long and iterative research and development process is expensive
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Solution
Machine learning tool Alchemite
Augment experimental data with thermodynamic predictions
Incorporate uncertainty to design alloys with the highest likelihood to meet design targets
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Outcome
New nickel-base superalloy that met 11 targets
Properties were experimentally verified
Reduced development cost by £10m
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Batteries
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Challenge
Performance, cost, and safety of batteries will drive the development of electric vehicles
Materials and battery management system can both be improved by machine learning
Further research into battery chemistries requires more complicated battery dynamics
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Solution
Machine learning to design the cathode, anode, and electrolyte
Predictive models for battery management systems to alleviate range anxiety
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Outcome
Ran virtual experiments to focus the search for new materials
Reduced development and fabrication costs while improving key battery metrics
Predict remaining useful life, state of health, and state of charge
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Antimalarial drug discovery
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Challenge
Identify a new molecule against a novel antimalarial mechanism of action
Available data covers multiple assays, but only a small fraction have been measured
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Solution
In blind test Alchemite accurately predicted activity of compounds against novel mechanisms of action
Outperforms conventional QSAR and other machine learning models
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Outcome
Alchemite confidently predicted a compound generated automatically by Optibrium’s StarDrop™ software
Compound synthesised and tested by OSM group, only entry that demonstrated potency against the target
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3D printing
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Challenge
Additive manufacturing is a new process that requires new materials
Ability to print materials is poorly understood
Direct laser deposition (a 3D printing method) has only been applied to ten nickel alloys
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Solution
Alchemite exploited property-property relationships to capture of new insights
Juxtaposed historical welding and sparse direct laser deposition data
Optimized alloy for direct laser deposition and also fulfil other target properties
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Outcome
Alloy for direct laser deposition experimentally verified
Alchemite saved 15 years worth of development
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Data validation
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Challenge
Companies that sell curated databases must have efficiently identified and fixed all erroneous entries
Errors in central database misguides research programs and will lead to inaccurate machine learning
Triage data introduced to a central database
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Solution
Alchemite models all available data
Automatically identifies unexpected values to focus attention where additional investigation is most effective
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Outcome
Identified errors including typos, errors, or genuine outliers
Automate tool to triage submitted data
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Forging hammers
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Challenge
Forging hammers wear and degrade through repeated use, can they be made more durable?
Machine learning explores new alloys to improve strength at elevated temperature
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Solution
Augment experimental data with thermodynamic models
Incorporate uncertainty to propose molybdenum-base alloys with the highest likelihood to meet design targets
Alchemite suggests new precipitate chemistry with improved properties
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Outcome
New molybdenum-base alloys found with 5-fold reduction in experimental time and cost
Improve molybdenum-base hammers lifetime by 30%, reducing deployment cost and factory downtime
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Alkanes for lubricants
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Challenge
Lubricants need to be optimized for new applications such as electric vehicles
Lubricants contain hydrocarbon molecules
Predicting the properties of hydrocarbons facilitates the development of base oils
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Solution
Exploit property-property correlations augmented by computer simulations to predict physical properties
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Outcome
Alchemite accelerated the identification of optimal hydrocarbons tenfold
Alchemite estimated intractable properties including shear viscosity five-times more accurately than other methods
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Summary
Alchemite adds value to sparse data problems by exploiting property-property correlations
Juxtapose results from different methods
Significant saving in reduced cost and accelerated discovery
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Resources https://intellegens.ai/resources/
Email [email protected]
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