From Atoms To Words #10: Summer-is-coming Edition ???
Arturo Robertazzi
CMEO & Co-Founder @ Quantistry | Quantum Chemist | Sales & Marketing Expert | Science Communicator | Published Novelist
Welcome to From Atoms to Words! With January behind us, summer is already on the horizon. ?? I know, I know. I am a hopeless optimist. Well, it might be raining outside but you know what? There's a world of discovery waiting for us! So, this month let's dive together into the heart of materials science, from quantum chemistry to machine learning. And speaking of simulations, we conclude with a masterpiece: all-atom molecular dynamics of the SARS-CoV-2 virus, featuring 305 million atoms. Are you ready? Grab your computational umbrellas and let's go.
Machine Learning in Materials Science: A Second Computational Revolution?
?? Let's get this straight: I'm an old-school computational chemist. There. I've said it.
I've done my time with semi-empirical calculations, Hartree-Fock, MP2, played around with DFT and molecular dynamics, even dabbled in docking – you get the drift.I'm no stranger to sweating over making calculations work, scouring odd forums to troubleshoot, and diving into a sea of articles to pick the right functionals and basis sets.
? And then, there's the waiting game – days, sometimes weeks, for DFT or molecular dynamics to churn out results. Headaches? Had my fair share, for sure.
But does it have to be this way? Perhaps it's time for a change.
?? I'm talking about weaving more machine learning into our computational materials science toolkit. Skeptical? I was too. But the more I read, the more I'm convinced.
?? So, here's a thought-provoking question for you: is machine learning in materials science kicking off the second computational revolution?
I'm usually not one for throwing around the word revolution, but in this case, yeah, I believe it's happening.
Sure, computational chemistry, quantum chemistry, and simulations will always be key players – they're the backbone for understanding and rationalizing, say, the quick responses of an AI. They're also indispensable for generating the rich datasets we need when experimental data is scarce.
?? But here's my take: the computational tools I grew to love during my PhD are bound to evolve and merge with the path of machine learning.
It's only the beginning, yet the impact is already clear – from the subtle art of predicting material properties to deciphering the complex map of crystal structures.
?? So, fellow computational enthusiasts, the choice is ours: Do we cling to the tried and true methods, or do we leap into the embrace of this second computational revolution?
I've made my choice. What's yours?
?? Get the full story at ?? Machine Learning in Materials Science: A Second Computational Revolution?
Quantum Chemistry of Molecule-Surface Adsorption: The 30-Year Struggle To Chemical Accuracy
?? As a student, I used to turn my nose up at the quantum chemistry and chemical simulations of materials, polymers, or molecule-surface adsorption.
My interest lay solely in the chemical processes of biological systems. I mean, there is something undeniably alluring about the mysteries of DNA. Don't you think? ??
Since I joined the team at Quantistry in 2022, my perspective has shifted. I've been exposed to a whole new world of materials, and, well, it's utterly captivating.
?? So, let's embark on a journey through the scientific history of how we have reached chemical accuracy in the prediction of the adsorption energy of carbon monoxide on magnesium oxides, or as the scientists like to call it: CO/MgO.
Just like the battle of theory vs. experiment to estimate the hydrogen dissociation energy, the 30-year long and controversial saga of attempts to predict and measure the CO adsorption energy on MgO surfaces is an epic tale of quantum chemistry, molecule-surface interactions, and the unyielding pursuit of scientific understanding.
??? It's a perfect example of how scientific insight grows in our modern era — with lots of twists, turns, and controversy along the way.
Through all of these challenges, we've learned that the pursuit of knowledge is never a straight path, but it rather emerges from the confrontation of theory and experiment, from different ideas and points of view clashing, merging, converging.
?? So, let's embrace the battle of theory vs. experiment, for it's a never-ending journey that ultimately leads to a better understanding of our world.
Curious about the historical perspective of molecule-surface adsorption? The teeny-tiny scientific details? The final computational setup that reached chemical accuracy?
?? Get the full story at??? Quantum Chemistry of Molecule-Surface Adsorption: The 30-Year Struggle To Chemical Accuracy
领英推荐
All-Atom Molecular Dynamics of SARS-CoV-2: The Computational Microscope's View of 305 Million Atoms
?? What are the limits of all-atom molecular dynamics these days? What model sizes are we hitting now? 1 million atoms? 50 million? Hundreds of millions?
The first time I used all-atom molecular dynamics, I, a quantum chemist, was completely in awe. Imagine thousands of atoms, moving around in what seems like chaos, eventually forming a structure that matches what we see in experiments.
?? This was right after my PhD, when I returned to Italy for a couple of years to do my first post-doc at the International School for Advanced Studies in the stunning city of Trieste.
?? Oh, how I adore that place! I remember those lunch breaks when I'd jump on my motorbike and head out for a quick swim. Then, returning to the lab, I'd be greeted by those amazing simulations of DNA fragments.
The whole simulation process was a painstaking affair; on a supercomputer, it would take weeks to reach significant dynamics of about tens of nanoseconds. That was almost two decades ago.
And today?
?? Now, we're at the forefront of molecular dynamics, running simulations on a colossal scale. The progress is astonishing:
? What model sizes and time scales can we reach with current all-atom molecular dynamics?
? How do these simulations complement experiments?
? What can we learn from these models?
Buckle up then, because we're diving into all-atom molecular dynamics of the whole viral envelope of SARS-CoV-2.
We're talking about a model of a whopping 305 million atoms. Let that sink in! 305 million atoms. My poor quantum chemist brain gets all on fire trying to process that amount of information.
What about you? Wanna give it a try?
?? Get the full story ?? All-Atom Molecular Dynamics of SARS-CoV-2: The Computational Microscope's View of 305 Million Atoms
+3 Bonus Stories
1?? Predicting The Hydrogen Dissociation Energy: The 100-Year Battle of Quantum Chemistry vs. Experiment
After a century of predictions on the hydrogen dissociation energy, is it now time to freak out and question the fundamental laws of physics? [Read the full story]
2?? Graphene to Wisdom: 10 Life Lessons Inspired by Geim’s Nobel Lecture
Andre Geim's Nobel lecture is a goldmine of science-inspired life lessons. Eager to dig deeper into the wisdom of he who discovered graphene? [Read the full story]
3?? Is Machine Learning Going to Replace Computational Chemists?
Machine learning is all over: drug discovery, material design, protein structure prediction. As computational chemists, should we be worried? [Read the full story]
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1 年Looks like a fascinating edition! Can't wait to read it!
IT Manager / CyberSecurity / Software Dev / IT Engineering Manager: Science, Engineering and Manufacturing
1 年If we had more 'old school' people, we'd be much further ahead of where we are, Arturo Robertazzi. We are what we make of our own lives, which means that we can make anything happen, especially from an optimist mind. When I first became interested in Materials Science, they were forging a path into composite materials. I landed work at an airport, researching and applying materials science to aircraft. Superb reading :}
It's raining outside. I better go read some of your stories ?