From Atoms To Words #12: Quantum-Celebration Edition 🎉
Arturo Robertazzi
CGO & Co-Founder @ Quantistry | Quantum Chemist | Sales & Marketing Expert | Science Communicator | Published Novelist
Greetings to you, curious minds! As March bids us farewell, we embark on an enlightening journey through a kaleidoscope of subjects, from reactive molecular dynamics simulations to the unexpected marvels of serendipity in the era of AI. True to our spirit, we traverse diverse terrains: the enigmatic world of quantum biology, the vast expanse of chemical space, and the pioneering role of AI in drug discovery. The thread linking this month’s stories? It's the awe-inspiring phenomena of nature and the way simulations, from quantum to AI, serve as powerful tools to unravel them. Now, before we delve deeper, let’s first take a moment to celebrate some remarkable news:
▸ Quantistry €3 Million Thank You: Let's Shape the Future of R&D from Quantum to AI
I began my adventure with Quantistry a couple of years ago. They were on the lookout for someone with a blend of scientific acumen and marketing savvy.
🧞 And where else could you find someone who meshes sales & marketing with a deep-rooted understanding of quantum chemistry and simulations?
I had a hunch this wouldn't be a fleeting chapter. And sure enough, Quantistry’s journey became entangled with my own.
🍾 I am now the Chief Growth Officer and Co-Founder! And just when I thought it couldn’t get any better, it did.
With our significant milestone, Quantistry and I edge closer to a dream far bigger than any one of us: reshaping the future of chemical and material R&D.
➡️ Curious? Learn more
▸ The From Atoms To Words Newsletter Reaches 3,000+ Subscribers!
And for this, I owe each of you a heartfelt thank you. ❤️
This achievement, alongside our milestone with Quantistry, symbolizes a perfect synergy of my personal passions and professional endeavors: the art of creative writing and science communication beautifully intertwined with my role at Quantistry.
So, dear reader, I invite you to continue the From Atoms To Words journey with me. There are endless pools of stories, characters, and scientific mysteries to explore, all revolving around simulations, quantum, and AI.
Here’s to you. 🥂 Your companionship makes every discovery all the more exciting.
🚀 Pretty cool, right? Now, let's dive back into the topics we've lined up for this month:
ReaxFF Molecular Dynamics: Simulating Complexity Beyond Quantum Chemistry
🔨 To a quantum chemist, every science problem looks like a quantum chemistry problem. Is there a better way?
Let’s say you want to explore reactivity in complex systems—be it within a battery cell, an enzyme, or nucleobases in RNA.
Where do you start?
⚛️ Quantum chemistry may be your first thought, with our friend DFT leading the charge. It’s a logical choice that offers the potential for not only accuracy but predictive insights as well.
But to study a system with thousands or more atoms at a quantum chemistry level means one thing: approximations.
We've touched on this when we discussed the cluster approach for enzymatic reactions and the hybrid quantum mechanics/molecular mechanics method for surface adsorption.
But what happens when these approximations fall short, especially when the system's dynamics, or the impact of solvents and the environment, play a pivotal role?
🫨 You might lean towards ab initio molecular dynamics, a technique we've also encountered when we talked about multiscale simulations of DNA. However, this path, too, is fraught with limitations—computational, to be precise.
There is no doubt. Quantum chemistry's allure has grown, thanks to the proliferation of user-friendly software (like QuantistryLab!) that has made sophisticated quantum mechanical calculations more accessible.
Yet, the brilliance of quantum chemistry casts long shadows. The very atomistic insight that makes quantum chemistry invaluable also demands a steep computational toll.
🐌 This limitation is more than a mere technicality; it represents a bottleneck in our quest to understand and predict the behavior of complex systems.
So, what's next?🤞🏼
You might consider turning to all-atom molecular dynamics. Undeniably powerful, but for the chemist keen on capturing chemical reactivity, there are significant limitations.
Is the cytosine in your DNA oxidizing? Is your amino acid being protonated? Is your electrolyte degrading?
✂️ Classical molecular dynamics maintains the initial structure throughout; it can model dynamics and non-covalent interactions but doesn’t quite cut it when it comes to breaking/forming covalent bonds.
So, shall we turn off our supercomputer in frustration? Of course not.
🗽 Introducing reactive force fields, or ReaxFF for short—a beacon of hope for chemists and material scientists alike.
🧐 What exactly is ReaxFF molecular dynamics? How does it work? What can you do with it?
📚 Get the full story at ➡️ ReaxFF Molecular Dynamics: Simulating Complexity Beyond Quantum Chemistry
Serendipity in Science: What’s its Fate in the Age of AI?
They call it by different names: happy accidents, unexpected discoveries, lucky breakthroughs—Serendipity. And I've experienced it firsthand. No, I haven't stumbled upon penicillin or graphene, but I did "discover" a DFT functional that could mimic dispersion forces in systems of biological relevance. Back then, it was a big deal.
I'd wager that every scientist, whether tinkering in labs or poring over simulations, from DNA sequences to polymer chains, has brushed up against serendipity.
💡 The accidental discovery of x-ray emissions from peeling tape in a vacuum, the famous random walk to graphene, or the realization of cisplatin's potential as an anticancer agent—each reveals how unexpected findings have revolutionized scientific discovery time and again.
You dive deep into one question, obsessed with untangling your scientific yarn, only to come across an entirely different knot.
Has serendipity ever surprised you like this? 🧐
Think of the roots of string theory. It set out to explain the strong nuclear force tying quarks together within protons and neutrons. Yet, gravity kept weaving itself into the equations, turning string theory into a potential theory of everything.
🤯 Mind-blowing, right?
Now, with all my writing about quantum chemistry, simulations, and AI-driven discovery, I've found myself deep in thought about the nature of serendipity.
So, I set out to craft a story that might shed some light, offering up answers both to you and to myself. Instead, I've landed in a place teeming with more doubts than I began with:
📌 Is serendipity just a fancy way of acknowledging our luck, or does it serve as a crucial spark in the engine of scientific discovery?
📌 Is it a feature of human creativity or a measure of our ignorance?
📌 How essential is it to design AI environments that foster rather than stifle serendipitous discoveries?
📚 Get the full picture at ➡️ Serendipity in Science: What’s its Fate in the Age of AI?
+5 Bonus Stories
1️⃣ Quantum Biology: The Fuzzy Connection Between Quantum Mechanics and Living Things
When you really get down to it, all living things, including humans, are just excitations of the quantum fields. Ergo quantum biology. [Read the full article]
2️⃣ Digital Alchemy: Computers in Chemistry and the Future of Scientific Discovery
From today's computers in chemistry to four future scenarios of scientific discovery: A post-vacation musing. [Read the full article]
3️⃣ 7 Noncovalent Interactions in Proteins: The Hidden Architects of Structures and Functions
Noncovalent interactions in proteins: the flexible wonders that bestow proteins the magic of three-dimensionality. [Read the full article]
4️⃣ AI in Drug Discovery: Chasing Dreams, Facing Realities
Is AI the real deal in drug discovery? And if it is, how exactly is it going to disrupt the drug discovery process? [Read the full article]
5️⃣ Chemical Space to Material Discovery: Simulations and Machine Learning Leading the Way
How astronomical is chemical space? Immensely so. How do we then discover & design the materials of tomorrow? Computation can give us a hand. [Read the full article]
Did you find this newsletter helpful or insightful?
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Full Professor of Chemistry by Alma Mater Studiorum - Università di Bologna
8moI like your approach: "But to study a system with thousands or more atoms at a quantum chemistry level means one thing: approximations." Approximation is a useful tool for finding acceptable solutions in complicated systems. However, approximation requires a deep knowledge of the domain of existence of the unknown variables, to have proper confidence in the error committed. Here the role of us empiricists becomes crucial in validating a theoretical model. More than twenty years ago I was researching the solution structure of proteins through nuclear magnetic resonance spectroscopy. The first models appeared in databases (i.e., pdb), based on molecular mechanics or dynamics algorithms, starting from approximate alignments on template structures with high sequences homology. As the empirical structures increased, the calculated models became increasingly closer to the real proteins because the amount of approximation decreased more and more