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The Materials Genome Initiative held a 2017 workshop, that led to this published report: de Pablo et al., "New frontiers for the materials initiative," npj Computational Materials 5, 41 (2019). In part, this report lists various successes of the initiative, due to complementary theoretical, computational, and experimental studies. Perhaps more interestingly, it also lists several challenges for future research. One particular challenge that gets listed in several contexts is related to computational modeling of synthesis:

Improving computational support for the synthesis of soft materials would be very valuable. Although significant steps have been made using machine learning to predict outcomes of simple organic reactions,36,37 extending this capability to include a larger range of chemical variety and macromolecular synthesis would democratize chemical synthesis and accelerate our validation of theories for new chemistries. Computational guidance in the iterative experimental synthesis process is also critical to future advances.38

Recent advances, particularly in x-ray diffraction at high temperatures, pressures, and inert environments could allow a more quantitative understanding of the thermodynamics of crystal synthesis. This could improve computational models of nucleation and crystal formation, help optimize the synthesis of known materials, as well as drive the discovery of new non-equilibrium compounds that are only stable under narrow conditions. Computational models of synthesis would be helpful to encourage feedback between theory and synthesis and enable calculations of optimal synthetic conditions.

Often synthesis is referred to as an art, which exudes respect for those active in the field, but may also signify that theory is lagging significantly behind the practice. Of course, synthesis typically occurs through rapid non-equilibrium processes, and sometimes at very elevated temperatures or in unusual atmospheres. This makes modeling such reactions beyond the input/output scope of simple reaction equations rather challenging, and a very different proposal to the calculation of equilibrium properties of some material post-synthesis. However, smart people have made progress on difficult problems before, and it seems clear that such modeling could potentially bring new insight and advance the art itself - and perhaps the design of novel materials.

Hence, I wonder:

  1. How well can we theoretically or computationally model the dynamics of synthesis today, whether of molecules or crystals? What is considered state-of-the art, and what are its limitations?
  2. Have there been any particularly striking successes or failures in this regard?
  3. Do such computations incorporate quantum physics?

Personally, I am particularly interested in the case of transition metal systems (e.g. oxides). However, I'd suspect there's been considerable advances on the side of molecular and organic chemistry, where there might be a large interest in such methods from the medical industry. In support of this hunch, I was able to find a List of computer-assisted organic synthesis software on Wikipedia, but no matching lists for the general or inorganic cases.

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    $\begingroup$ The answer to the title question is "good but it depends". But the answers to the questions in the body are too anecdotal. There is no right answer. I can give you multiple interesting examples of computations giving remarkably right answers and then also give you examples where they break down. $\endgroup$ – Cody Aldaz Jul 24 '20 at 16:43
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    $\begingroup$ @CodyAldaz Well, that's typical isn't it. I suppose I'm coming at this with a physicists perspective, looking for a sense of where the field is, and where it's going, rather than a long list of examples. I'm happy to strike question #2 if that'd help. $\endgroup$ – Anyon Jul 24 '20 at 16:52
  • $\begingroup$ +1. Thanks for contributing this very interesting question Anyon! I guess Susi has given some examples of the successes (for part #2) and Cody can give some examples of the failures? Maybe Question #2 does not need to be "removed" entirely, but it seems it's a big enough can of worms for a separate (related) question! $\endgroup$ – Nike Dattani Jul 24 '20 at 16:54
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Okay first some basics:

For most reactions we do not need to run molecular dynamics

Molecules are like springs and obey the principles of minimum energy. Therefore, normally the dynamics of a molecule will be distributed around the "minimum energy path" going from reactants to products.

enter image description here

The maximum along this minimum energy path is the transition state, and has energy $E_A$, which can be used to calculate the rate of the reaction with transition state theory

$k=Ae^{-E_A/RT}$

If you were to run dynamics simulations, first you would find that would not be easy to observe a reaction take place; the movement and vibrations of the individual particles occur on the $10^{-15}$ s timescale, but the reaction events are much rarer, and far between (e.g. seconds or longer) -- this is a consequence of the principle of minimum energy. Secondly, you would find that

  1. the reacting particles must collide with each other
  2. the reacting particles must have enough energy to break the old bonds
  3. the particles must have the proper orientation

In other words, they follow the minimum energy path! This theory is a workhorse in modern computational chemistry and is used by the Nature paper linked by Susi Lehtola.

Okay, now that that is in order, one apparent area where reaction modeling breaks down is when there is a bifurcation event after the transition state, for example see here

These DO require molecular dynamics because there is recrossing and also dynamical effects with a bifurcating potential energy surface.

However, if you're not aware of this then, then the products that you predict will be wildly off from experiments.

See for example, this excellent paper by Singleton et al. "Recrossing and Dynamic Matching Effects on Selectivity in a Diels-Alder Reaction", Angew Chem Int, Ed, 2009, 48, 9156 https://onlinelibrary.wiley.com/doi/epdf/10.1002/anie.200903293

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    $\begingroup$ This is interesting, thank you! Perhaps it's a naive question, but does the "Molecules are like springs" picture still apply when we start talking about crystal growth? $\endgroup$ – Anyon Jul 24 '20 at 17:22
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    $\begingroup$ I don't know much about crystal growth, it's a very different problem $\endgroup$ – Cody Aldaz Jul 24 '20 at 17:29
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    $\begingroup$ @Anyon I think that's an interesting question. Crystalline materials are often studied with non-linear optics (i.e. they can be considered non-linear materials), where the polarization density responds non-linearly to the electric field of lasers. A non-linear spring force (beyond -k*x) makes vibrations anharmonic, but I haven't worked enough in this field to feel confident with articulating a proper answer to your question. It might be a good question for the main site, where more crystal experts will see it. $\endgroup$ – Nike Dattani Jul 24 '20 at 17:52
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    $\begingroup$ @NikeDattani Nice! Susi and I answered within a few minutes of the question being posted. My answer comes in part from my thesis where I justify why reaction paths are used. $\endgroup$ – Cody Aldaz Jul 25 '20 at 3:15
  • $\begingroup$ Retweet: twitter.com/ndattani/status/1286853051907747842 $\endgroup$ – Nike Dattani Jul 25 '20 at 3:17
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The computations do include quantum physics, since the very bare minimum of electronic structure calculations involve the quantum mechanics of electrons.

There's been quite a lot of development in computational chemistry in synthesis. While computational chemistry was pivotal already in the late 1980s in developing ozone safe alternatives to cholorofluorocarbons, see Physics Today 61, 4, 58 (2008); nowadays computations are often leading the design effort of new syntheses since calculations are much faster to carry out than lab experiments, see Nature Communications volume 7, 10109 (2016), for example.

Cody briefly explained transition state theory below. I could also point out a recently published paper on a success of transition state theory: the H2S+Cl system where dynamical effects were though to be important is actually fully described by transition state theory, it's just that you need a very good quantum chemical description to achieve the necessary accuracy in the computations: J. Chem. Theory Comput. 2020, acs.jctc.0c00354

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    $\begingroup$ I think that Nature Communications paper is interesting both for its contents, and for highlighting how foreign the language of chemistry can be to me! It seems clear then that reaction pathways can be usefully predicted and modeled to guide synthesis efforts. $\endgroup$ – Anyon Jul 24 '20 at 17:03
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There is an initiative from IBM called RoboRXN. It is a free web service for predicting chemical reactions.

From the IBM Research Blog recent post:

The magic behind the RXN for Chemistry is a state-of-the-art neural machine learning translation method that can predict the most likely outcome of a chemical reaction using neural machine translation architectures. Similar to translating Italian to English, our method translates the language of chemistry converting reactants and reagents to products, using the SMILE representation to describe chemical entities.

Some features of RoboRXN are:

  • We provide a state-of-the-art trained artificial intelligence (AI) model that can be used in your daily research activities irrespective of the purpose
  • Use the prediction mode to open a project and invite collaborators to collectively plan complex synthesis.
  • Use the challenge mode to test your Organic Chemistry knowledge and prepare for class exams
  • Design your retrosynthesis either using the automatic or the interactive mode. In the interactive mode, IBM RXN for Chemistry –just like an assistant – recommends disconnections and you choose.
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