From Atoms To Words #4: Multiscale Simulations From DNA to Electrification

From Atoms To Words #4: Multiscale Simulations From DNA to Electrification

Welcome to?From Atoms to Words, where this month we ride on a summer breeze, from DNA to electrification. After a sunny detour into the impressive abilities of large language models, we go deep into multiscale simulations of DNA and the design of next-gen batteries. Let's go!

Multiscale simulations of DNA: From Quantum Effects to Mesoscopic Processes

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Representative DNA fragments bound to cisplatin, a metal-based anticancer agent. The thinner lines indicate the classical force-field layer, while the thicker lines depict the quantum layer.

?? Did you know that if you took all the?DNA from a single human cell?and stretched it out, it would be a whopping?1,80 meters long? In fact, there is enough DNA in an average human to stretch it from the sun to pluto and back. About 11 times.

?? Now, how do you simulate that? More specifically, how do you simulate the DNA?

The study of DNA is like the ultimate melting pot of biology, physics, and chemistry. And theoretical methods have been especially helpful in trying to understand its structure and functions, but let’s face it, this stuff is no joke. ?? It’s like trying to navigate through a maze that ranges from the smallest of small details to the grandest of scale.

So, simulating DNA is challenging. Why?

?? Imagine this: Although with DNA you can travel the solar system, the distance between individual base pairs is in the minuscule angstrom range. Some changes to DNA occur over the course of years, while others, like chromatin reorganization during the cell cycle, take place within a single day. Meanwhile, the local movements of nucleobases happen in mere milliseconds, while electronic rearrangements take place in a mind-bending sub-femtosecond time-scale.

And that’s why simulating DNA is a challenge of epic proportions: because of the wide range of time and spatial scales involved in its processes. For this reason, multiscale simulations of DNA are needed, from quantum chemistry to coarse-graining.

Curious yet?

In my article ?? Multiscale simulations of DNA: From Quantum Effects to Mesoscopic Processes ?? you will read about:

?? Quantum chemistry to investigate base pairs

?? QM/MM and ab initio molecular dynamics for DNA reactivity and dynamics

?? Classical molecular dynamics - because of course, force fields rock

?? And if that is not enough, a glimpse on the world of coarse graining for mega DNA structures

?? Read further: ?? Multiscale Simulations of DNA: From Quantum Effects to Mesoscopic Processes |?#FromAtomsToWords


Large Language Models for Chemistry: Is the Beginning of a New Era?

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?? Will large language models ever become better chemists than us humans?

Whether you are computational chemist, an experimental researcher, or someone who wouldn’t know a pipette from a pineapple, chances are you’ve?heard?whispers of the mystical beings known as?large language models.

Oh, but of course you have! You certainly encountered, used, loved, or perhaps hated, chatGPT.

We’re talking about language models that can?answer questions,?summarize texts,?convert files, and?do all sorts of phenomenal stuff.

You just need to ask nicely.

?? ChatGPT and its cohorts are like the rock stars of natural language processing, using cutting-edge machine learning to generate text that’s so darn good, it’s hard to tell it apart from human writing.

Hello there, Mr. Turing!

From a scientific standpoint, the implications are nothing short of revolutionary.

These language models can whip up?abstracts for scientific articles?with a flick of their digital wrists. They can?craft lines of code?tailored to specific?programming?tasks like a virtuoso on a twelve-string. And that’s not all—they can even take on challenges they were never explicitly trained for, like some kind of?machine-learning sorcerer. ??

It’s as if they possess an innate adaptability, an insatiable hunger for tackling fresh obstacles. A chilling thought, if you think about it.

Now, if these large language models can handle tasks?they weren’t initially designed for, could they also?hold the answers?to the scientific questions that have plagued us for centuries?

Take chemistry, for example. Just imagine being able to ask these language models questions like:

?? If I swap the?metal in my metal-organic framework, will it be moisture-stable?

?? What’s the free energy landscape of that?DNA transition?

?? What is the?role of hydrogen bonding?in that biological process?

Effortlessly, these language models may present the answers to our burning questions in the blink of an eye, leaving us mortals to wonder:

?? Are these responses to be trusted??Is this the beginning of a new era for chemistry?

?? Discover it all in ???Large Language Models for Chemistry: Is the Beginning of a New Era? | #FromAtomsToWords


Computer-Aided Next-Generation Battery Design: From Edisonian Trial-and-Error to Atomistic Simulations

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Lithium diffusion through a silicon-based anode. QuantistryLab view

?? If you told my younger self that I’d end up?working?on simulations for next-generation battery design, he would have laughed at you.

?? I mean, come on, I was all about DNAs and proteins –?the little heroes that keep our bodies ticking?against almighty entropy.

But here’s the thing: life is full of surprises. And today, alongside my colleagues at? Quantistry , we’re collaborating with some of the world’s largest players to push the boundaries of next-generation battery design and create a brighter,?more sustainable future.

?? So, what can atomistic simulations do for our electrical future? And why should R&D players bother learning about it?

In my article ?? Computer-Aided Next-Generation Battery Design ?? you will read about:

?? Why should you care about batteries?

?? Next-gen battery design: why is that a combinatorial minefield?

?? What R&D challenges can you solve with atomistic simulations?

?? Plus, you will discover 3 success stories of computer-aided battery design.

Curious to learn more?

?? Visit ???Computer-Aided Next-Generation Battery Design: From Edisonian Trial-and-Error to Atomistic Simulations |?#FromAtomsToWords


+3 Bonus Stories

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?? 7 Noncovalent Interactions in Proteins: The Hidden Architects of Structures and Functions [Read more]

?? Let’s Fight Climate Change with the Computational Design of Metal-Organic Frameworks [Read more]

?? Quantum Nanoreactor Simulations of the Early Universe: The Dawn of Interstellar Chemistry [Read more]


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Did you find this newsletter helpful or insightful?

Subscribe?to?#FromAtomsToWords?to receive future stories about quantum chemistry and the world around it. Let me know your comments or suggestions below, and thank you for reading!

???Read previous issues of From Atoms To Words


#QuantistryLab #researchanddevelopment #ComputationalChemistry #QuantumChemistry #AtomisticSimulations #MolecularDynamics #DNA ?#NextGenBattery?#EnergyStorage #AI #LargeLanguageModels?#MachineLearning?#NaturalLanguageProcessing?#ChatGPT?#InverseDesign ?#QMMM?#CarParrinelloMolecularDynamics?#CoarseGraining?#ForceField?#epigenetics?#Nucleosome?#HydrogenBonding?



Hanna Fedchyshyn

Climate & Healthcare Innovation | Research Manager at Ananda Ventures

1 年
回复
Jose Brandao-Neto

Senior Operations Manager at Diamond Light Source

1 年

Hi Roberto, in your cisplatin picture, I take you mean in the model from the classical field the bases would be further away from the Platin atom. Are those 4 models frames in a time series? More questions ?? : how do you account for electron delocalisation along the DNA strand when modelling DNA fragments?

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