ATLAS

You are welcome to join a seminar on “A Data-driven Robotic AI-Chemist”, by Prof Jun Jiang from USTC China @JunJian07285897. Time : 1-2pm 11 November 2024. Location: ACEX 203 LT2, Dept of Chemical Engineering. @ImperialChemEng @impchemistry @aichemyhub @JelfsChem @linjiangchen

Final few days to register for our Symposium being held at Anfield Stadium. Threes days of exciting talks on AI, Robotics, and chemistry. Registration closes Friday 11th October, don't miss out. Register here: tinyurl.com/5fp7sbhw Agenda here: tinyurl.com/22kajnw6

Now Camille Petit @Petit_Group presents her autonomous lab studies into molecular systems using high throughout work flows

Congratulations Omar and great to see the ATLAS represented! 😊

🗓️ One week left to apply for a postdoctoral position in our group 👇🏻

Last chance to get your early-bird registrations in for the most anticipated #ChemicalEngineering event of the year, #ChemEngDayUK2024! ➡️Early-bird closes 14 March | 11.59pm(GMT) 🖇️tinyurl.com/ChemEngDay2024 @IChemE @ImpEngineering #HEevents #Engineering #STEM

📣 Postdoc position available in our group on automation & high throughput experimental development of advanced materials! 🥼🔬 Opportunity to join a project led by @titiricigroup, involving 3 universities & access to @AtlasFacility ! 👉🏻 bit.ly/4c6i9p4 @ImperialChemEng

Exciting news! @imperialcollege & @LivUni lead on AIChemy, a £31m research hub, aiming to develop AI for Chemistry & make the UK a global leader in Digital Chemistry discoveries. Thanks to @EPSRC, @UKRI _News & our partners. Read more ->  imperial.ac.uk/news/251214/im…

We're on the lookout for passionate individuals to join us as Project Officer and Hub Manager. Apply now to be part of groundbreaking projects and shape the future of chemistry! #ChemJobs #hiring Hub Manager: tinyurl.com/y34cdtkf Project Officer: tinyurl.com/4yhn3xxw

ABSTRACT SUBMISSION DEADLINE INCOMING ➡️ 15 January|5pm GMT: final abstract submission date for the much-anticipated #ChemEngDayUK2024, 25-26 April, at @imperialcollege. Have you submitted? 👇 #ChemicalEngineering #HEevents #STEM @IChemE @ImpEngineering imperial.ac.uk/chemical-engin…

Ready for the most anticipated #ChemicalEngineering event of 2024? Super pleased to have on board exemplary speakers from across #industry & #academia...& a provisional programme 👊🏽 ➡️ Abstract submission: 15 January ➡️ Early-bird reg: until 14 March 🔗tinyurl.com/ChemEngDay2024

in our paper in Digital Discovery, we [within a computer simulation] demonstrate multi-fidelity Bayesian optimization (MFBO) for reducing the cost of materials discovery and orchestrating "self-driving" labs. pubs.rsc.org/en/content/art… 🧪 problem setup we (1) wish to search a candidate set of materials for the one with the optimal property and (2) have access to two experimental protocols to measure this property: one cheap but low-fidelity, the other high-fidelity but expensive. what is the sequence of experiments (ie., material-fidelity pairs) that allows us to find the optimal material (according to the high-fidelity measurement) early in the search, incurring the least cost? 🔨 MFBO multi-fidelity Bayesian optimization (MFBO) combines: - supervised machine learning - uncertainty quantification - automated decision-making under uncertainty to iteratively pick experiments (material, fidelity pairs) towards finding the optimal material while expending the least cost. MFBO leverages the cheap, low-fidelity measurements to prune unpromising regions of materials space and focus the expensive, high-fidelity measurements on materials that lie in more promising regions. MFBO constitutes an experiment-update-plan feedback loop. 1⃣ experiment phase: we conduct a low- or high-fidelity measurement of a material. 2⃣ update phase: we construct a surrogate model, trained on all data gathered thus far, that inexpensively predicts, with quantified uncertainty, both low- and high-fidelity measurements of material properties based on structural and chemical features of the materials. 3⃣ plan phase: MFBO picks the material and fidelity for the next experiment by maximizing an acquisition function that scores each material-fidelity pair according to its appeal for the next experiment, balancing exploration, exploitation, and cost. 🖥️ MFBO of COFs for Xe/Kr separations we demonstrated MFBO by searching a database of ~600 covalent organic framework (COF) materials for the one with the highest adsorptive xenon/krypton selectivity, armed with (i) a high-fidelity binary grand-canonical Monte Carlo simulation of adsorption and (ii) a low-fidelity molecular simulation in the dilute regime to calculate the Henry coefficients of Xe and Kr in the COF. we found that MFBO acquires the optimal COF using only 30 low-fidelity and seven high-fidelity simulations, incurring only 2%, 4% on average, and 20% on average of the computational runtime of a single-[high-]fidelity exhaustive, random, and BO search, respectively. 🧠 the team a close collaboration with Nick Gantzler, @deshwal_aryan, and @janadoppa.

Interested in attending the much-anticipated #ChemEngDayUK2024? Early-bird registration & abstract submissions are now open 🙌 ➡️ 25-26 April '24 ➡️ @imperialcollege, South Kensington Campus ➡️ open to students, educators, industrialists Register ⏬️ imperial.ac.uk/chemical-engin…

@BeckyLGreenaway Congratulations Becky!

Have you checked out the @AtlasFacility 3D virtual tour? Includes details on the robots' specs 🤖➡️ tours.360virtualtours.co.uk/atlas-icl/ 🖇Find out more about the facility ➡️ tinyurl.com/mr25rmwh #Research #Engineering

Congratulations Omar! 😊

Proud moment as Omar wins best poster prize at #bpICAM annual conference! Well done! 🥳 @BeckyLGreenaway @ImperialChemEng @AtlasFacility

Some ATLAS work in action 👇🏻

Want to know how our ATLAS facility looks like? 👉🏻Check our 3D virtual tour 🎬 tours.360virtualtours.co.uk/atlas-icl/ 👉🏻Details on the robots’ specs available 🤖 Any questions, contact us imperial.ac.uk/automated-high… @ImperialDigiFAB @ImperialChemEng @BeckyLGreenaway @JYYHeng @TheoniGeo

😍 Exciting work Alice and great to see the platform in action!