When traditional R&D methods hit their limits, they impact scientific progress.
In Voices of SDLabs® Ep3, Mohammed Azzouzi (Application Engineer) shows how #SDLabs opens the AI black box – giving scientists tools and visibility to augment them in accelerating their discoveries.
We’re hiring in Lausanne 🇨🇭 & Boston 🇺🇸!
At Atinary, build and enable #SelfDrivingLabs to augment scientists with Physical AI and Robotics to accelerate R&D. 🚀
Open roles: AI for Chemistry • Full Stack Developer • Marketing • Lab Technician
Apply: https://t.co/hUP2f9MduB
(2/2) What AI-guided workflow could look like for optimizing Buchwald-Hartwig reactions:
1️⃣ Reaction Space Mapping
2️⃣ Active ML Parameter Screening
3️⃣ Catalyst & Precious Metal Optimization
Check out the Part2 video above! ☝️
Missed Part1? Catch up here: https://t.co/xxxioP5k06
From pharmaceuticals and crop protection agents to OLED displays on our smartphone screens, C-N bonds form the backbone of modern materials.
In 1994, Profs. Stephen Buchwald & John Hartwig introduced palladium catalysis, giving scientists a gentle way to form C-N bonds. 1/2
Why are Buchwald-Hartwig reactions hard to optimize? Millions of ligand, base & solvent combos mean trial-and-error takes months.
Think of #SelfDrivingLabs as GPS for chemistry: mapping reaction spaces, running tests & recalculating routes to reach breakthroughs faster! (1/2)
Software should accelerate science, not slow it down.
Episode 2 of Voices of SDLabs® is out! 🎬
Kim Andersen (Product Manager) shares how our scientist-first #SDLabs platform uses agentic AI & active learning ML to reduce R&D friction & augment scientists.
#SelfDrivingLabs
Incredible energy at @ForumEPFL 🚀
Thank you to everyone who visited our booth. Great discussions around our #SelfDrivingLabs mission to augment scientists with AI & robotics to accelerate scientific discovery.
Join us: https://t.co/hUP2f9LFF3
#SDLabs#ExponentialScience
With millions of parameter combinations, optimizing these reactions remains a bottleneck.
We’re proud to have Prof. Buchwald on our Scientific Advisory Board as we bring AI, #SDLabs & #SelfDrivingLabs technology to navigate these complex spaces faster! Stay tuned for Part 2! 2/2
From pharmaceuticals and crop protection agents to OLED displays on our smartphone screens, C-N bonds form the backbone of modern materials.
In 1994, Profs. Stephen Buchwald & John Hartwig introduced palladium catalysis, giving scientists a gentle way to form C-N bonds. 1/2
At Atinary, SDLabs® combines Physical AI & robotics to augment scientists & drive a Moore's Law of Discovery 🚀
Thank you at Ilmac, @BioAlps & @SwissChemistry for great discussions!
👉 Explore closed-loop R&D: [email protected]
3/3
Reflecting on 2 inspiring days @ Ilmac. Key themes on AI/ML in life sciences:
1. Real-time analytics + active ML shift workflows from passive monitoring to active guidance 💡
2. Predictive models make it realistic to test significantly more hypotheses within timeline+budget🎯
1/3
3. Closed-loop automation isn't about replacing human intuition. It's about trusted models that learn from every run 🔄
4. Real-time sample tracking, metadata capture & remote monitoring give teams total operational awareness to run reliable automated workflows ⚡
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Introducing #VoicesofSDLabs: Episode 1
No custom programming. No coding. Built for scientists to focus on science.
Cur Co-Founder & CTO @loic_roch explains how #SDLabs provides the infrastructure for autonomous discovery – augmenting scientists to accelerate R&D.
#SelfDrivingLabs
Join next week’s webinar with @Agilent on how we deploy #SelfDrivingLabs!
Experts from Agilent & Atinary reveal how the first fully integrated Self-Driving Lab for small molecule organic synthesis was designed & deployed to augment scientists.
Register: https://t.co/utcj6aWhxr
(2/3) Key takeaways from PyData & how we're translating them at Atinary:
🔹 Connect digital AI layers to closed-loop lab hardware
🔹 Shift toward conversational, agentic architectures
🔹 Simplifying core systems to maintain high velocity & stability without legacy tech debt
Back from @PyData Amsterdam! 🇳🇱
Our ML engineers shared how we serve personalized ML at scale in #SDLabs:
- Rayan Daod Nathoo on evolving runtimes for low-latency ML predictions
- Daniel Pacheco Gutierrez on the Stable Matching Problem (1/3)
(3/3) We build & enable #SelfDrivingLabs to augment scientists with #PhysicalAI & robotics to accelerate discovery 🚀
Want to solve hard engineering problems? Join our team in Lausanne 🇨🇭 (working alongside our Boston team 🇺🇸)!
Visit our Careers page: https://t.co/hUP2f9LFF3
Atinary kicked off @BiopoleLausanne's AI Exchange Forum! 🚀
Our team covered:
• R&D AI adoption & #SelfDrivingLabs
• Math & surrogate modeling in Bayesian Optimization • #SDLabs applications: cell line optimization & 7D spaces with 2.9B combos!
📸 Credit: @BiopoleLausanne