From Atoms To Words #11: Quantum-AI Edition ???
Arturo Robertazzi
CGO & Co-Founder @ Quantistry | Quantum Chemist | Sales & Marketing Expert | Science Communicator | Published Novelist
Greetings to you, curious minds! As we waved goodbye to February, we navigated through a sea of topics. We marvelled at water's chemistry — its anomalies, its hydrogen bonds — and we delved into the application of quantum chemistry in the study of enzymatic reactions. Our journey also took us through the promising land of machine learning, pondering its potential to disrupt not just the prediction of RNA and protein structures but also to impact materials science and computational chemistry.
?? For this very juicy edition of From Atoms To Words, I weave a fil rouge for you, tracing a thread that starts in the roaring '20s, with the giants of quantum mechanics laying down the foundations, through the first forays into quantum chemistry, to today’s hopes on tomorrow's quantum computers and AI. And yes, we'll touch upon the uneasy question hanging over us today: is machine learning going to replace us? ??
The Evolution of Quantum Chemistry: From Pencil and Paper to Quantum Computing
When I was in high school, I didn't even know that something like quantum chemistry existed. Quantum was not a buzzword. I just wanted to become a scientist. But when I actually enrolled in an undergraduate chemistry course, I discovered that I wasn't exactly a natural in the lab.
?? My experiments would always turn out differently than everyone else's. Their solutions would be yellow, while mine would be brown. They'd find aluminum in their samples, while I'd find iron. And then one day my tutor sat me down and said: "Boy, you need to switch to theory."
And switch I did. I graduated with a Ph.D. in quantum chemistry from the University of Cardiff.
?? But then, after years of academic research, life took me in an unexpected direction, and I traveled the globe as a sales & marketing manager. Only recently I returned to the world of simulations. It's been more than a year now with?Quantistry.
Why?
?? The field has entered an exciting phase, with quantum chemistry, simulations, and AI becoming indispensable tools in industries that are committed to saving the planet.
From the days in Arosa, when Schr?dinger wrote down his equation, there has been an incredible progress. Plus, with the advent of quantum computing, the prospects for quantum chemistry have never been brighter. (Or are they?) ????
Now, this is not the first time that the field experiences a resurgence due to advances in computing power.
?? It all started with last-century genius Paul Dirac suggesting that practical “ab initio calculations” for molecular systems are basically impossible by hand. Then, for decades, pioneers like Hartree, Slater, Coulson, Hund, Pople, L?wdin, tried to devise strategies to overcome the inherent difficulties of using quantum mechanics to explain chemical phenomena until finally,?digital computers?came along to save the day.
30 years after Schr?dinger,?Scherr, a graduate student of future Nobel laureate?Robert S. Mulliken,?used computers to perform the first-ever all-electron ab initio calculation of a molecule larger than the hydrogen molecule.
?? It took Scherr two years (!) to complete those calculations, which included only two Nitrogen nuclei and 14 electrons! Nowadays, we can run the same calculations in a matter of seconds.
So, how did we go from years to mere seconds? Are you curious about the historical perspective?
?? Get the full scoop at ?? The Evolution of Quantum Chemistry: from Pencil and Paper to Quantum Computing
Do We Really Need Quantum Computing in Chemical R&D?
So, quantum computers... But What are they good for? "For now, absolutely nothing."
Shocking, uh? (Well, keep scrolling.)
Amidst all the noisy hype, a spicy but fair voice emerges: Michael Brooks on Nature.
It aligns well with our conversations about quantum computing in chemical R&D.
Michael Brooks continues:
Even with 2 million qubits, some quantum chemistry calculations might take a century [but] the world’s largest quantum computer in terms of qubits is IBM’s Osprey, which has 433.
Now, the intriguing viewpoint:
The short-term hype is a bit high, but the long-term hype is nowhere near enough. Whatever the quantum sweet spot turns out to be, it could be more spectacular than anything we can imagine today.
So, after months of writing about atomistic simulations, AI, and quantum computing for chemical and materials R&D, what's my takeaway?
1?? Atomistic simulations are powerful today. They do have limitations. Quantum computing might be just what we need to take us to the next frontier. The problem is, we're not sure when this will happen.
2?? There is hope. Consulting companies are recommending that R&D players get involved with the quantum community and start building a competitive edge for the future. Why? Because once we achieve quantum advantage, quantum chemistry will likely be the first application of quantum computing to profit from.
3?? If you're an R&D player, don't sit on the sidelines and wait for the future to arrive. Read, research, follow me ??, and why not? Start simulating today.
?? What about you? What's your take on the future of quantum computing in chemical R&D?
Looking for inspiration? ?? Get the full story at??? Do We Really Need Quantum Computing in Chemical R&D?
Is Machine Learning Going to Replace Computational Chemists?
领英推荐
If you've been reading?From Atoms To Words the blog, you'll know we often talk about quantum chemistry, computational chemistry, and simulations. I can't hide it — I'm absolutely passionate about it.
I've spent years immersing myself in the painful ins and outs of the field, and it's thrilling to see how it has evolved over the decades from abstract equations to a pivotal tool in several branches of chemistry.
In our previous stories, we've explored how computational chemistry allows you to, for example:
1?? Investigate chemical reactivity
2?? Quantify molecular interactions in complex systems
3?? Support and rationalize experimental work
4?? Nail down the whys and hows of chemical mechanisms
5?? Predict key descriptors for molecular design
But (and there's always a but), let's not get too carried away. As much as I adore computational chemistry, it's time to play devil's advocate for a moment. ??
Let’s put it this way. Sometimes, computational chemistry is seen more as a flawed tool than a solid scientific discipline.
Would you agree? ??
At times, it might even seem like we're just fitting data to get the right results, but for the wrong reasons.
Now, beyond the intrinsic challenges, there are broader issues, in my opinion, that make computational chemistry a bit of a tough sell in the industrial world:
1?? Computational Demand: Despite progress, computations can still be considered as too demanding. Think about running a million calculations, each taking ten minutes — well, it adds up!
2?? High-level Expertise: The computational know-how required for calculations and simulations is a jungle, man. You still need a PhD to run this show. (Or do you?)
These challenges can make the practical application of computational chemistry in industrial R&D less viable.
?? So, how do we support and evolve computational chemistry to overcome these hurdles? Is machine learning the way forward?
?? Discover it all at??? Is Machine Learning Going to Replace Computational Chemists?
+5 Bonus Stories
1?? Enzymatic Reactions: Quantum Chemistry Modeling of Life's Catalysts
Can we really investigate enzymatic reactions and estimate their activation energies with quantum chemistry? [Read the full article]
2?? When Will RNA Structure Prediction Get Its AlphaFold Breakthrough?
We've mastered predicting protein structures from their sequences. Can we now replicate this success with RNA structure prediction? [Read the full article]
3?? Water's Hydrogen Bonds: What Makes Them Vital for Life As We Know It?
How can water's hydrogen bonds have such a colossal impact on the existence of life on Earth and, potentially, everywhere else? [Read the full article]
4?? 60 Years in the Making: AlphaFold's Historical Breakthrough in Protein Structure Prediction
After decades of scientific pursuit, machine-learning-based AlphaFold has revolutionized protein structure prediction. [Read the full article]
5?? Machine Learning in Materials Science: A Second Computational Revolution?
The journey of machine learning in materials science is just at the beginning. Yet, its impact is already clear. [Read the full article]
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IT Manager / CyberSecurity / Software Dev / IT Engineering Manager: Science, Engineering and Manufacturing
7 个月While you adventured the world of marketing, Arturo Robertazzi, I embarked on an adventure of flying and buying, in Canada's north. Beavers, Otters, and assorted bush pilot's aircraft, were my daily transports. My ground transport was a manual shift Ford F100, which took me a day of driving, to stop jack-rabbiting while trying to start each stop. For a while it was nice, but freezing temperatures, and working 60 to 70 hours a week, meant that my mind wasn't getting any alone time. So, back into the world of Science and Engineering, for me. I've always been a deep thinker, or a reserved intellectual as some call it. Theory is our mind's way of informing us that we live in an interconnected world, and in order to progress, we need to understand the details and complexities. What I came to understand is that we are all individuals, and require the environment to be successful. During our lives, we go through stages and phases, maturing each one as we move onto the next. Which means that we need to outgrow each environment to move forward from it. This happens both as an individual and/or within a team. I'm very curious about machine learning, although I was raised with a workshop with a library full of interesting adventures :}
? Infrastructure Engineer ? DevOps ? SRE ? MLOps ? AIOps ? Helping companies scale their platforms to an enterprise grade level
7 个月Can't wait to dive into this fascinating edition! ??