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Computational chemist here. The phys.org article is a pretty bad rendering of the original paper. The "theoretical interpretation" described in the headline is

Computational chemist here. The phys.org article is a pretty bad rendering of the original paper. The "theoretical interpretation" described in the headline is nothing new. The actual scientific advance described in the paper is an experimental (not theoretical) technique for measuring transition states of molecules, involving making them very cold so the transition states last longer. This lets us verify some quantum mechanics predictions, but we already had a lot of verification. Calculating small-molecule reaction paths via quantum mechanics has been a standard part of theoretical chemistry for decades. We can do it at a very high level of accuracy using millions of CPU hours, or at a passable level of accuracy using thousands of CPU hours. Big molecules are still a challenge because the scaling of the accurate methods is very bad (N^4 or worse, where N is the number of electrons!). These calculations are something that quantum computers could theoretically do much better than classical ones.

maxerickson 9y ago
phys.org just aggregates university press releases: https://news.unm.edu/news/unm-chemists-provide-theoretical-interpretation-to-understand-chemical-reactions https://news.unm.edu/news/unm-chemists-provide-theoretical-i... The author info from that link: Steve Carr Supervisor, Communications University of New Mexico
crocal 9y ago
The article is poorly written but your comment is very clear. This looks totally fascinating. Is there some kind of introductory book you would recommend (graduate level)? One burning question that I would like to ask you is if computational chemistry has ever predicted reactions that had not been discovered before? That would be more than awesome.
- tomjakubowski 9y ago
  Former/failed/flamed-out computational chemist right here. I can't give a recommendation, because I forgot which book I used way back when, but the answer depends somewhat on whether you're interested in "large" or "small" structures. Each will use different techniques. Molecular mechanics, which is interested in simulation of big biomolecules and the like, ignore quantum mechanical effects, or use a hybrid of quantum/classical mechanics, because the structures of interest are large enough the QM effects can be approximated away. Other subfields of computational chemistry use only QM, with some approximations to make the programs tractable.
- curlcntr 9y ago
  Quantum Chemistry, Ira N. Levine, is one of my favorites. Quite readable and covers all the basics.
- sseagull 9y ago
  Current computational chemist here (in QM). It depends on what specific areas you are looking at. Computational chemistry is, itself, a fairly large field with several subfields and specializations (as mentioned in a sibling). I can't think of a particular example for an unknown reaction being predicted first, then verified, but it wouldn't surprise me. However, there are cases of computations being used to guide experiment (who have to have an idea of where to look for something). Theory is generally used to explain, rather than predict. Ie, given a reaction, what is the most likely mechanism? Or helping explain the source of a particular property. With increases in computing power, databases, etc, we may see computational chemistry start to predict novel materials (via machine learning, for example).
tpfour 9y ago
Hijacking this comment to shamelessly plug my project. We're holding a survey at https://docs.google.com/forms/d/1NXMpmD3DkDuAPgaOpLM2baJRyCihhLsPadsHATt5u1M/ https://docs.google.com/forms/d/1NXMpmD3DkDuAPgaOpLM2baJRyCi.... Our goal is to build the best platform for Computational Chemistry. Still in development stage, but I'd be glad to answer any questions. Also very appreciative of anybody who completes/shares this survey!
- sseagull 9y ago
  I'm a bit interested to see where this project is headed. My main question is what do you mean by "platform for computational chemistry"? A new package or an interface to existing packages (like WebMO)?
  - tpfour 9y ago
    We're building an interface like WebMO, but modern. We are not re-writing any quantum chemical software for the moment, however we've written some as grad students. For now we are concentrating on replacing JMol and getting our API functional with NWChem.
    - sseagull 9y ago
      Is there an email/website I can use to get in touch? I'm part of an effort that touches on a lot of these areas.
      - tpfour 9y ago
        Sure! Reach us at info@quanta.software.
crawfordcomeaux 9y ago
This is what I though the article was about when I saw the headline. Curious about your thoughts on this framework. A Compositional Framework for Reaction Networks https://golem.ph.utexas.edu/category/2017/07/a_compositional_framework_for_2.html https://golem.ph.utexas.edu/category/2017/07/a_compositional...
- comicjk 9y ago
  I think that the application of information theory to large systems of chemical reactions is likely to be fruitful. As your source says, it will be important for biological systems, with their myriad interlocking reactions. From my perspective, an issue is that this calculation takes as input the rate constants of the reactions, whereas my research has been at the level below this: finding the rate constants (and similar properties). This can be quite difficult, so my mind boggles a bit at the idea of a calculation which requires large amounts of such data.
muthdra 9y ago
Dude, your job looks awesome. I have been thinking a lot about the ramifications of chemestry computation since last year. This comment https://news.ycombinator.com/item?id=16027737 https://news.ycombinator.com/item?id=16027737 about how to get around NP-hardness made me go "Oh, can you even ask such a question? Can you expect to solve getting around this kind of problem?" and so I went researching. I found the paper Complexity of Protein Folding https://www.gwern.net/docs/biology/1993-fraenkel.pdf https://www.gwern.net/docs/biology/1993-fraenkel.pdf and chapter 4 exposes two possible realities: either nature solves NP-hard problems in polynomial time or it functions in a classic Turing machine model. I was like "Welp I guess when you put it like this, talking about nature working within a machine model I suppose it sounds rather silly, yes. It may be that nature just does her thing in her own way somehow." The paper says a protein with 100 amino acids may assume 8^100 conformations and nature solves for that in 1 second. Also the spin glass https://en.wikipedia.org/wiki/Spin_glass https://en.wikipedia.org/wiki/Spin_glass model is apparently some other thing that nature solver eerly fast. Can we chain these natural occurances in order to calculate a function with inputs in record time?
- gilleain 9y ago
  > The paper says a protein with 100 amino acids may assume 8^100 conformations and nature solves for that in 1 second There are some problems with this - firstly that paper is quite old. Not sure what the current state of the art is but this might be helpful : http://folding.stanford.edu/dig-deeper http://folding.stanford.edu/dig-deeper Secondly, the idea that the protein might adopt any of 8^100 (or some large number) of combinations is not quite right. Consider a very simple model of just a chain of nodes connected together on a 2D grid. There are certainly a large number of possible ways to arrange this chain on the grid, but if you start off in one particular arrangement (call it 'unfolded') and then move towards a more compact one ('folded') then you will follow some particular path of arrangements. However, there are ideas around 'quantum biology' from Johnjoe Mcfadden (http://www.johnjoemcfadden.com/ http://www.johnjoemcfadden.com/) that might be interesting.
- dekhn 9y ago
  Many of the early computer science approaches to protein folding were laughably naive, and the assumption about folding described in the Fraenkel paper: "It is believed that the native folded three-dimensional conformation of a protein is its lowest free energy state, or one of its lowest. " is not factually correct. In reality, proteins never reach their lowest energy state unless they are very simple proteins, instead, they rapidly reach a state of kinetic interconversion between several accessible states. We can simulate proteins and measure those state transitions. Really, solving protein folding is about classical approximations and clever sampling.

maxerickson 9y ago

phys.org just aggregates university press releases: https://news.unm.edu/news/unm-chemists-provide-theoretical-interpretation-to-understand-chemical-reactions https://news.unm.edu/news/unm-chemists-provide-theoretical-i... The author info from that link: Steve Carr Supervisor, Communications University of New Mexico

crocal 9y ago

The article is poorly written but your comment is very clear. This looks totally fascinating. Is there some kind of introductory book you would recommend (graduate level)? One burning question that I would like to ask you is if computational chemistry has ever predicted reactions that had not been discovered before? That would be more than awesome.

tomjakubowski 9y ago
Former/failed/flamed-out computational chemist right here. I can't give a recommendation, because I forgot which book I used way back when, but the answer depends somewhat on whether you're interested in "large" or "small" structures. Each will use different techniques. Molecular mechanics, which is interested in simulation of big biomolecules and the like, ignore quantum mechanical effects, or use a hybrid of quantum/classical mechanics, because the structures of interest are large enough the QM effects can be approximated away. Other subfields of computational chemistry use only QM, with some approximations to make the programs tractable.
curlcntr 9y ago
Quantum Chemistry, Ira N. Levine, is one of my favorites. Quite readable and covers all the basics.
sseagull 9y ago
Current computational chemist here (in QM). It depends on what specific areas you are looking at. Computational chemistry is, itself, a fairly large field with several subfields and specializations (as mentioned in a sibling). I can't think of a particular example for an unknown reaction being predicted first, then verified, but it wouldn't surprise me. However, there are cases of computations being used to guide experiment (who have to have an idea of where to look for something). Theory is generally used to explain, rather than predict. Ie, given a reaction, what is the most likely mechanism? Or helping explain the source of a particular property. With increases in computing power, databases, etc, we may see computational chemistry start to predict novel materials (via machine learning, for example).

tpfour 9y ago

Hijacking this comment to shamelessly plug my project. We're holding a survey at https://docs.google.com/forms/d/1NXMpmD3DkDuAPgaOpLM2baJRyCihhLsPadsHATt5u1M/ https://docs.google.com/forms/d/1NXMpmD3DkDuAPgaOpLM2baJRyCi.... Our goal is to build the best platform for Computational Chemistry. Still in development stage, but I'd be glad to answer any questions. Also very appreciative of anybody who completes/shares this survey!

sseagull 9y ago
I'm a bit interested to see where this project is headed. My main question is what do you mean by "platform for computational chemistry"? A new package or an interface to existing packages (like WebMO)?
- tpfour 9y ago
  We're building an interface like WebMO, but modern. We are not re-writing any quantum chemical software for the moment, however we've written some as grad students. For now we are concentrating on replacing JMol and getting our API functional with NWChem.
  - sseagull 9y ago
    Is there an email/website I can use to get in touch? I'm part of an effort that touches on a lot of these areas.
    - tpfour 9y ago
      Sure! Reach us at info@quanta.software.

crawfordcomeaux 9y ago

This is what I though the article was about when I saw the headline. Curious about your thoughts on this framework. A Compositional Framework for Reaction Networks https://golem.ph.utexas.edu/category/2017/07/a_compositional_framework_for_2.html https://golem.ph.utexas.edu/category/2017/07/a_compositional...

comicjk 9y ago
I think that the application of information theory to large systems of chemical reactions is likely to be fruitful. As your source says, it will be important for biological systems, with their myriad interlocking reactions. From my perspective, an issue is that this calculation takes as input the rate constants of the reactions, whereas my research has been at the level below this: finding the rate constants (and similar properties). This can be quite difficult, so my mind boggles a bit at the idea of a calculation which requires large amounts of such data.

muthdra 9y ago

Dude, your job looks awesome. I have been thinking a lot about the ramifications of chemestry computation since last year. This comment https://news.ycombinator.com/item?id=16027737 https://news.ycombinator.com/item?id=16027737 about how to get around NP-hardness made me go "Oh, can you even ask such a question? Can you expect to solve getting around this kind of problem?" and so I went researching. I found the paper Complexity of Protein Folding https://www.gwern.net/docs/biology/1993-fraenkel.pdf https://www.gwern.net/docs/biology/1993-fraenkel.pdf and chapter 4 exposes two possible realities: either nature solves NP-hard problems in polynomial time or it functions in a classic Turing machine model. I was like "Welp I guess when you put it like this, talking about nature working within a machine model I suppose it sounds rather silly, yes. It may be that nature just does her thing in her own way somehow." The paper says a protein with 100 amino acids may assume 8^100 conformations and nature solves for that in 1 second. Also the spin glass https://en.wikipedia.org/wiki/Spin_glass https://en.wikipedia.org/wiki/Spin_glass model is apparently some other thing that nature solver eerly fast. Can we chain these natural occurances in order to calculate a function with inputs in record time?

gilleain 9y ago
> The paper says a protein with 100 amino acids may assume 8^100 conformations and nature solves for that in 1 second There are some problems with this - firstly that paper is quite old. Not sure what the current state of the art is but this might be helpful : http://folding.stanford.edu/dig-deeper http://folding.stanford.edu/dig-deeper Secondly, the idea that the protein might adopt any of 8^100 (or some large number) of combinations is not quite right. Consider a very simple model of just a chain of nodes connected together on a 2D grid. There are certainly a large number of possible ways to arrange this chain on the grid, but if you start off in one particular arrangement (call it 'unfolded') and then move towards a more compact one ('folded') then you will follow some particular path of arrangements. However, there are ideas around 'quantum biology' from Johnjoe Mcfadden (http://www.johnjoemcfadden.com/ http://www.johnjoemcfadden.com/) that might be interesting.
dekhn 9y ago
Many of the early computer science approaches to protein folding were laughably naive, and the assumption about folding described in the Fraenkel paper: "It is believed that the native folded three-dimensional conformation of a protein is its lowest free energy state, or one of its lowest. " is not factually correct. In reality, proteins never reach their lowest energy state unless they are very simple proteins, instead, they rapidly reach a state of kinetic interconversion between several accessible states. We can simulate proteins and measure those state transitions. Really, solving protein folding is about classical approximations and clever sampling.