I don't get it. Aren't simulations of chemical reactions based on things you already KNOW about those reactions? How are they being used to find out new things about the reactions?
I haven't read anything in the link, but I guess that if the simulation system is of sufficient precision and that it is accurate enough for known chemical reactions, such a simulation system should be able to be pushed further and it may end up simulating events and phenomena that have not been observed in the real world before.
Bonus points if they observe real world reactions that agree with the model after the simulation has run.
1. You take a lot of empirical observations of known reactions, build a predictive model, then apply it to contexts you don't have empirical observations of.
2. You program in the low level rules of chemistry (e.g. quantum mechanics), and see how the scenario plays out at a higher level (molecular reactions).
I think in their work they are more number 1. A predictive model of proteins without having the simulate the (exceedingly expensive) low level details of QM
I'm only casually familiar with chemistry, but I used to do research of a similar type (only far worse, of course) related to electromagnetism.
You know the main mechanisms at work, but that does not mean you can study all the processes. Sometimes it's just that the sheer size of the mathematical model itself is basically impossible to study without a computer. Oftentimes, though, it's the inadequate formulation of a model that doesn't allow you to simulate it.
A good simulation is an immensely useful tool. There are phenomenons which you know and can simulate, but you cannot easily measure their parameters while they are happening (e.g. you risk disrupting the phenomenon because of your measurement installation). I imagine this is even more so for chemists.
Real-life example from my own research: we worked on tools that helped engineers who designed really high-frequency ICs (think tens of GHz) study things like cross-talk through the substrate. The mechanism itself is basically well-understood, but save for really, really simple structures that are nothing like those in an IC, you can't solve that by hand. Of course, ours were modest achievements, but the point is that this kind of research can open new gates and shouldn't be considered "second-rate".
There are programs which allow you to input desirable qualities (smell, color, density, /non/conductor, molecule mass, stereo orientation to name a few, very basic ones) and it will make computations and it will give you possible compounds.
Also software in chemistry can be used to simulate reactions and processes, which can take years to analyze with conventional methods(if at all possible), like folding protein chains.
Similarly, David Baker's lab has focused on producing the folded conformations of proteins in hours to days using simplified models that are designed for that task:
You are thinking about reactions as written out in a chemistry textbook. Those representations tell you about the reactants, products and maybe a few other details like catalysts, solvents and temperature. However, none of it explains the mechanism at the quantum or atomic level. Simulating the reaction with specialized software enables you to 'calculate' additional data points, such as, free energy, enthalpy and entropy among others, using 'molecular dynamics' (which 'solves' Classical and Quantum Mechanics equations for a given system and is extremely essential to understand movement of different parts of a molecule and their interaction with surrounding atoms to build a more complete picture of 'how' a reaction proceeds).
Today is a great day for computational chemistry. It is good to see the detailed Nobel announcement acknowledge other stalwarts of the field like Peter Kollman (who created AMBER, a package similar to CHARMm by Karplus et al). I have no doubt that Kollman would have received this prize had he been alive today.
"Chemistry answers that H2 and O2 can be joined to form water under certain conditions, but doesn't answer the whys".
Chemistry does answer the whys beyond product yields, why certain elements react is vastly covered throughout organic, inorganic & physical chemistry . It is the basis of these sub-fields, and how this is determined is looking at orbitals, energy states etc...
Ask yourself who invented the "Hubbard" (Pariser-Parr-Pople) model originally the next time you say that, or where the "Berry" (Mead & Truhlar/Longuet-Higgins) phase really came from. This notion that reductionism is a one way street is stupid.
"This notion that reductionism is a one way street is stupid."
I never said reductionism is a one way street.
But in simulations you usually work bottom-up (in abstractions). Of course, sometimes you can't do that, because it's overly complex.
My field (electrical engineering) has a lot of physics as well and I don't mind if those who invented the techniques I use were physicists or engineers (or something else), or if such and such things are in the "engineering" domain or the "physics" domain, I just use them. The point is moot.
You can simulate the reaction at a quantum-mechanical level at sub-nanosecond timesteps. That gives you a lot of information that you couldn't get from a test tube!
OTOH you can't get sufficient sampling on anything but the smallest systems to make any sort of dynamic prediction about it. Not to mention the approximations that were made during the choice of basis sets for the atomic orbitals and how one decides to handle the configuration interaction.
TLDR: Approximations are everywhere, make sure you work that into your conclusions.
I'll clarify that when he says sub-nanosecond timesteps, he means way sub-nanoscale timesteps. Femtosecond timesteps are used at the molecular dynamics level. Even smaller ones are used at the QM level.
Wow. I'm floored that computational structural biology work is getting a Nobel. I spent a lot of time reading their papers in grad school...they're totally foundational for the field, but the field is still really young:
Martin Karplus' group is behind CHARMM (http://www.charmm.org), which was the first(?) molecular dynamics package (you can think of it as a precursor to folding@home, though it's still under active development, so that isn't a totally fair statement).
Michael Levitt has done a bunch of things, with no one big software package, but he was one of the earliest people trying to do ab initio protein structure prediction. He was also one of the first people to really start categorizing protein structure in a way that allowed for computational modeling -- back in the 70s and early 80s.
Arieh Warshel is probably best known for bridging the gap between quantum mechanics and the (relatively quick-and-dirty) molecular mechanics work. He's done a whole bunch of work modeling enzymatic reactions, coming up with better electrostatic models, and other things where quantum mechanics does a good job, but is way too slow to be used on giant molecules like proteins.
I had a whole other paragraph about that, but took it out because I figured that most people on HN wouldn't even know what NMR is, let alone NMR structure prediction.
These guys have collectively done so much stuff that it's impossible to give a fair accounting of it all in a brief post.
Yes, but I am also not surprised given this is pretty fundamental work, even if it hasn't quite hit the sort of revolutionary goals many of us hoped for. If Peter Kollman had been alive, I suspect he would have shared this Nobel.
This Nobel is hits very close to home. Much of my PhD work was done in CHARMM and I come from the Karplus lineage, and at one point in time I had pretty much read every paper Levitt and Warshel had ever written.
The beauty of CHARMM (at the time) was that it was the first practical combination of a forcefield and MD engine, that made it practical to simulation proteins. While other packages have left it behind on perf and capability, the forcefield itself remains very high quality, and the breadth of functionality is still as good as it gets. Unfortunately the implementation could do with an overhaul since it still looked like something from the early 80s (I haven't seen the CHARMM code in 5 years)
> the forcefield itself remains very high quality, and the breadth of functionality is still as good as it gets.
It's kind of weird that forcefields haven't improved all that much. Implementation has improved drastically (LAMMPS, Gromacs, etc.) and QM level theory has too (FCIQMC, exploiting symmetries to eliminate the sign problem), but MD forcefields don't seem to have made a whole lot of progress from CHARMM, AMBER, and UFF. I suppose some of the reactive potentials (AIREBO and Tersoff) are a step up, but still...
Maybe that's true for biological compounds, but for the rest of the periodic table new potentials are constantly being developed (and some may even be improvements). Semiconductors, transition metals, heavy elements, ferromagnetics, you name it... its being worked on.
Actually even for biological molecules there are still advances being made. Water is an excellent example seeing how there is a new potential released once every 6 months or so. Its difficult to say whether these actually show a quantifiable improvement over old potentials, but its still an area of constant effort and progress.
Computational methods are powerful, but not respected enough in many fields. I think it's harder to publish computational work in a respectable journal, but rather easier when it's wet lab work. I think it was a good choice of the Nobel committee that may change scientists' view of computational work.
Since you seem to know about this field, any chance you can share you opinion on the working going at DE Shaw Research? Does developing custom hardware for molecular dynamics give them any sort of qualitative advantage? Are the systems they're simulating significantly larger or more useful than what other groups work on?
I know a thing or two about the field. Custom hardware for molecular dynamics allows one to simulate longer timescale biological processes, namely the folding of larger proteins. More generally, in the study of any size protein, countless molecular dynamics papers are simply plagued with statistical error and cannot be trusted. I think it's unanimous that DE Shaw Research is doing a great service and I'm excited to see the performance of their next supercomputer, Anton2, http://dl.acm.org/citation.cfm?id=2451175
But as in all things, Anton makes a controversial set of assumptions and approximations w/r to long-range electrostatics so it's anyone's guess whether their approach is valid or not.
All that aside, Anton is a wicked cool piece of hardware able to accelerate their approximation of traditional molecular dynamics by a factor of 50 over GPUs and up to a factor of 500 over CPU clusters, hitting milliseconds of simulated protein time in a single trajectory.
And since it's the only hardware capable of simulating a single monotonic trajectory out to that timescale, it's challenging to compare it to other approaches.
Agreed. DE Shaw's hardware work is very cool, but I'm in the camp that believes that the force fields suck (in general; not just theirs), and bashing through some multi-millisecond trajectories will only highlight their limitations.
You should check out Lane et al. 2013 Fig. 1: http://www.sciencedirect.com/science/article/pii/S0959440X12... All computational protein folding times match experimental results within 1 order of magnitude. If folding time isn't a good measure of forcefield accuracy then I don't know what is. Sampling error is a worse offender than forcefield accuracy in my opinion.
To be fair, the Pande Lab's data has involved a degree of human intervention during the generation of the multi-state trajectories upon which they're based. And that's a failing that has only recently been addressed. IMO it's indeterminate whether that human intervention could have unwittingly influenced those trajectories.
Similarly, a lot of the folded proteins were used to develop the same force fields now used to simulate their folding. I'm not dismissing this data, but I am saying I think the jury is still out on the models until we have a true test set/training set dichotomy, a separation that was an absolute revelation for ab initio structure prediction algorithms in the 1990s.
That said, I think we both agree that undersampling is the biggest offender. And it only gets worse with results published for larger systems simulated at the same timescale as much smaller ones.
" If folding time isn't a good measure of forcefield accuracy then I don't know what is."
Folding kinetics are surprisingly insensitive to detail. For example, it's been understood for a while that even ridiculously simple models can predict transition state energies for simple proteins:
But to answer your question, I'd say that "predicting the correct structure of a protein" is the gold-standard benchmark of forcefield accuracy, and MD forcefields are really bad at it.
(You could reasonably add other great measures, like: "does the simulation tend to fly apart without hacked-up pseudo-physical constraints?", but that feels like piling on.)
I'm biased since I do simulations but you're being too harsh on force fields. All the proteins in the paper I mentioned had their structure correctly predicted starting from an extended state. Correct structure and folding time! That's a serious accomplishment compared to the decade-old papers you're citing.
"That's a serious accomplishment compared to the decade-old papers you're citing."
What? It's not like the "decade-old" paper has become less correct over time. I mean, it's great that MD is finally close to predicting something that could be predicted a decade ago with much simpler methods, but that isn't saying a lot. Plus, as the other commenter pointed out, there's a complicated cross-validation problem that biases the results you're reporting.
In truly blind tests (i.e. CASP), MD force fields just aren't that good at predicting structure from sequence. The usual counter-argument is that they aren't meant to work with non-MD methods (fair enough, I guess), but you don't have to try very hard to find reasons not to trust them. MD simulations have always been very finicky things, requiring lots of manual intervention to get "right".
this is pretty awesome. in grad school in the 90s i was exploring a lot of this very early on, very few people around me were there to help and that's ultimately how i got involved using Linux and other FLOSS, then sysadmin, security work and now what i do in computer security research. very glad to see this prize granted for this line of work as it truly was a leap forward for the study of chemistry.
I'm somewhat surprised that Warshel was included instead of Berni Alder, as a more self-consistent group of scientists from a field point of view, and given Alder is largely seen as the father of MD.
Well then why not Ceperley too, as they helped straighten out some problems at the core of LDA? I think the Nobel committee wanted to keep the focus on biochem.
Agreed - that's the conclusion I came to too after posting. Still, I'd imagine Harold Scheraga and Andy McCammon are (justifiably) a little sore this morning...
health sector has more scope now to update their process and techniques. I think they should totally rethink their existing methodologies with the power of computation
But the previous generation of scientists came of age when Molecular Dynamics was a disappointing tool because of limitations in the Newtonian models and the lack of computational firepower to do sufficient sampling. The latter has been addressed by a combination of Moore's Law and the ongoing migration of molecular dynamics codebases to GPUs, but the former issue remains - a Newtonian approximation to quantum chemistry.
What's surprising is how much one can get out of these simple models despite these limitations, something that was echoed back in 1976 by one of Michael Levitt's papers that led to today's Nobel Prize:
There are very deep reasons for this - the Born-Oppenheimer approximation, a central simplifying assumption, imposes some constraints on the system that make it easier to model with (semi)classical dynamics, even when you marry it with modern techniques such as DFT.
This is so cool! This is the kind of research I do: molecular dynamics and quantum mechanical simulation. I'm not nearly as good at it as the winners of this prize though haha.
I'm glad to see computational research is becoming more popular and accepted. There's a (quickly diminishing) subset of scientists who think computational work is too theoretical, inaccurate, and inapplicable to real-life. This was the case when the field was developed, but it is no longer true.
The exponentially increasing computational power is allowing discoveries that simply can not be performed experimentally because laboratory technology just isn't advanced or capable enough. Who needs to actually study reality if you can just simulate what you need -- and the end result is the same?
I'm not sure many people know this, but our understanding of the laws of physics is advanced enough nowadays to describe almost perfectly everything we observe in everyday life. (Exceptions include things like quantum gravity which don't really matter [ducks to avoid physicists]).
The problem is that if you want to simulate these laws, it requires a lot of computational power. Brute-force approaches are simply ineffective and so simplifications and clever techniques must be developed to reduce the computational effort while giving increasingly accurate results. I think it's the combination of improving computational resources and improving simulation algorithms that are really driving this field.
hey Xcelerate - I'm looking for someone to do some delta-Hf calculations on some "theoretical molecules" for me. By theoretical molecules, I mean, molecules that are very likely able to be made, but needs an assessment of "whether or not it's worth it". Is that something you can do, and would be interested in putting on ArXiV?
What data do you have? Do you know the mechanisms?
We calculated heats of formation in our chemical thermodynamics course, but we were always given ample data, and we knew what we were trying to calculate.
Can this be explained simply? I took physical chemistry and it sounds related to calculating energy changes for ionization or electrons moving between orbitals.
you are using references for already measured energies and orbital systems. For example, if you take the measured energy change for, say, but-1-ene and the estimated heat of formation difference for a C-H vs. C-C and hydrogen, you will get a good estimate for {alpha,omega} octadiene.
If you think you can do the same thing for ethene -> butadiene, you'll find yourself to be horribly wrong, because of extended conjugation networks.
So while for some cases it works, it is not always simple to go from known empiricial results to more complex structures using tables and addition and subraction. And in the case of the molecules I care about, there is pretty good reason to believe that the simple linear methods will fail.
Hey! That sounds very interesting, but I'm already buried in a bunch of research projects as it is. Maybe if I get some free time at some point I'll contact you.
I love computational research more than most, seeing as I'm a scientist and a coder. However, I feel the need to weigh in with a counterpoint on this. I've spent the last eight years measuring a system that was well described by both theory and computational experiments. It was exactly one of those systems where the results could not be measured experimentally and simulation was the only path forward.
We then built an instrument which could measure the results experimentally. Since this system was so well described by simulation and theory, we used it as a calibration sample before doing real science. Except the results didn't match the computations at all.
I spent years tracking down problems in the instrument (some of which were real) before finally setting in on the fact that the theory and calculations were simply wrong. Just as you're always experimentally limited in what you can measure, you're resource constrained in what you can calculate. Sometimes, you need to drop down another physics abstraction level before you can get the right answer. However, experiments don't care about your abstraction level and get it right every time.
Performing physical experiments is a waste of time and money if you can just simulate the results. However, you don't know if you can simulate it until you've performed the experiment. Every scientist I've known has, at least once, run a simulation and later found that it wasn't even close to the experimental results. Sometimes it's a leaky abstraction in the simulation (e.g. Ignoring the anti-reflective coating on a mirror). Sometimes it's a leaky abstraction in the experiment (e.g. ignoring the pH differences between H2O and D2O). Sometimes it's new physics, but not very often. Sometimes it's exactly like you predicted. Until you leave the keyboard and enter the lab, you won't know.
"...our understanding of the laws of physics is advanced enough nowadays to describe almost perfectly everything we observe in everyday life." -- nope.
What's about those that we can't describe? Some of these effects are very computation-intensive, but doesn't mean their basis isn't well understood already.
This is a gross understatement and fallacious. Many of the problems being studied today would take millions/billions/trillions of years of computation in order to model... with approximations. Using even the largest clusters on the planet we can't even find the ground state of even the smallest protein (with ab-initio). DFT scales cubically with system size, and DFT is an approximation to actual first principles calculations that are impossibly enormous and can never be fully calculated.
The number of systems we can accurately model is minuscule, the number of open problems in computational chemistry and materials science is enormous.
> impossibly enormous and can never be fully calculated.
I wouldn't go so far as to say that.
A quantum computer can handle the fermion sign problem by scaling polynomially with the number of particles instead of exponentially. Estimates for if/when such a practical device will be created vary wildly but I would think the possibility of this, along with new techniques that take advantage of the redundancy inherent to certain categories of problems and efficiently diagonalize the Hamiltonian could accelerate the rate at which we can handle larger and larger systems. It's kind of hard to predict what breakthroughs will be made, but I'm staying optimistic.
EDIT: Then again, looking at your posts on here, I suspect you already know all that ;)
64 comments
[ 5.3 ms ] story [ 128 ms ] threadBonus points if they observe real world reactions that agree with the model after the simulation has run.
1. You take a lot of empirical observations of known reactions, build a predictive model, then apply it to contexts you don't have empirical observations of.
2. You program in the low level rules of chemistry (e.g. quantum mechanics), and see how the scenario plays out at a higher level (molecular reactions).
I think in their work they are more number 1. A predictive model of proteins without having the simulate the (exceedingly expensive) low level details of QM
You know the main mechanisms at work, but that does not mean you can study all the processes. Sometimes it's just that the sheer size of the mathematical model itself is basically impossible to study without a computer. Oftentimes, though, it's the inadequate formulation of a model that doesn't allow you to simulate it.
A good simulation is an immensely useful tool. There are phenomenons which you know and can simulate, but you cannot easily measure their parameters while they are happening (e.g. you risk disrupting the phenomenon because of your measurement installation). I imagine this is even more so for chemists.
Real-life example from my own research: we worked on tools that helped engineers who designed really high-frequency ICs (think tens of GHz) study things like cross-talk through the substrate. The mechanism itself is basically well-understood, but save for really, really simple structures that are nothing like those in an IC, you can't solve that by hand. Of course, ours were modest achievements, but the point is that this kind of research can open new gates and shouldn't be considered "second-rate".
Also software in chemistry can be used to simulate reactions and processes, which can take years to analyze with conventional methods(if at all possible), like folding protein chains.
http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
Similarly, David Baker's lab has focused on producing the folded conformations of proteins in hours to days using simplified models that are designed for that task:
http://boinc.bakerlab.org/rosetta/
Today is a great day for computational chemistry. It is good to see the detailed Nobel announcement acknowledge other stalwarts of the field like Peter Kollman (who created AMBER, a package similar to CHARMm by Karplus et al). I have no doubt that Kollman would have received this prize had he been alive today.
Chemistry is applied physics. Chemistry answers that H2 and O2 can be joined to form water under certain conditions, but doesn't answer the "Whys"
Physics answers that. And the answer goes around orbitals, energy states, electrons, etc.
Chemistry does answer the whys beyond product yields, why certain elements react is vastly covered throughout organic, inorganic & physical chemistry . It is the basis of these sub-fields, and how this is determined is looking at orbitals, energy states etc...
So, Pauli's exclusion principle is Physics or Chemistry? I'd say it's "both".
I never said reductionism is a one way street.
But in simulations you usually work bottom-up (in abstractions). Of course, sometimes you can't do that, because it's overly complex.
My field (electrical engineering) has a lot of physics as well and I don't mind if those who invented the techniques I use were physicists or engineers (or something else), or if such and such things are in the "engineering" domain or the "physics" domain, I just use them. The point is moot.
TLDR: Approximations are everywhere, make sure you work that into your conclusions.
Martin Karplus' group is behind CHARMM (http://www.charmm.org), which was the first(?) molecular dynamics package (you can think of it as a precursor to folding@home, though it's still under active development, so that isn't a totally fair statement).
Michael Levitt has done a bunch of things, with no one big software package, but he was one of the earliest people trying to do ab initio protein structure prediction. He was also one of the first people to really start categorizing protein structure in a way that allowed for computational modeling -- back in the 70s and early 80s.
Arieh Warshel is probably best known for bridging the gap between quantum mechanics and the (relatively quick-and-dirty) molecular mechanics work. He's done a whole bunch of work modeling enzymatic reactions, coming up with better electrostatic models, and other things where quantum mechanics does a good job, but is way too slow to be used on giant molecules like proteins.
These guys have collectively done so much stuff that it's impossible to give a fair accounting of it all in a brief post.
This Nobel is hits very close to home. Much of my PhD work was done in CHARMM and I come from the Karplus lineage, and at one point in time I had pretty much read every paper Levitt and Warshel had ever written.
It's kind of weird that forcefields haven't improved all that much. Implementation has improved drastically (LAMMPS, Gromacs, etc.) and QM level theory has too (FCIQMC, exploiting symmetries to eliminate the sign problem), but MD forcefields don't seem to have made a whole lot of progress from CHARMM, AMBER, and UFF. I suppose some of the reactive potentials (AIREBO and Tersoff) are a step up, but still...
Actually even for biological molecules there are still advances being made. Water is an excellent example seeing how there is a new potential released once every 6 months or so. Its difficult to say whether these actually show a quantifiable improvement over old potentials, but its still an area of constant effort and progress.
Yeah, that's what I was meaning with regard to force-fields in general. What are we, like TIP23P for water now? ;)
All that aside, Anton is a wicked cool piece of hardware able to accelerate their approximation of traditional molecular dynamics by a factor of 50 over GPUs and up to a factor of 500 over CPU clusters, hitting milliseconds of simulated protein time in a single trajectory.
And since it's the only hardware capable of simulating a single monotonic trajectory out to that timescale, it's challenging to compare it to other approaches.
Similarly, a lot of the folded proteins were used to develop the same force fields now used to simulate their folding. I'm not dismissing this data, but I am saying I think the jury is still out on the models until we have a true test set/training set dichotomy, a separation that was an absolute revelation for ab initio structure prediction algorithms in the 1990s.
That said, I think we both agree that undersampling is the biggest offender. And it only gets worse with results published for larger systems simulated at the same timescale as much smaller ones.
Folding kinetics are surprisingly insensitive to detail. For example, it's been understood for a while that even ridiculously simple models can predict transition state energies for simple proteins:
http://www.ncbi.nlm.nih.gov/pubmed/10322214?dopt=Abstract
http://www.ncbi.nlm.nih.gov/pubmed/10500172?dopt=Abstract
But to answer your question, I'd say that "predicting the correct structure of a protein" is the gold-standard benchmark of forcefield accuracy, and MD forcefields are really bad at it.
(You could reasonably add other great measures, like: "does the simulation tend to fly apart without hacked-up pseudo-physical constraints?", but that feels like piling on.)
What? It's not like the "decade-old" paper has become less correct over time. I mean, it's great that MD is finally close to predicting something that could be predicted a decade ago with much simpler methods, but that isn't saying a lot. Plus, as the other commenter pointed out, there's a complicated cross-validation problem that biases the results you're reporting.
In truly blind tests (i.e. CASP), MD force fields just aren't that good at predicting structure from sequence. The usual counter-argument is that they aren't meant to work with non-MD methods (fair enough, I guess), but you don't have to try very hard to find reasons not to trust them. MD simulations have always been very finicky things, requiring lots of manual intervention to get "right".
But the previous generation of scientists came of age when Molecular Dynamics was a disappointing tool because of limitations in the Newtonian models and the lack of computational firepower to do sufficient sampling. The latter has been addressed by a combination of Moore's Law and the ongoing migration of molecular dynamics codebases to GPUs, but the former issue remains - a Newtonian approximation to quantum chemistry.
What's surprising is how much one can get out of these simple models despite these limitations, something that was echoed back in 1976 by one of Michael Levitt's papers that led to today's Nobel Prize:
http://csb.stanford.edu/levitt/Levitt_JMB76_Simplified_repre...
Kieron Burke has a nice take on the matter.
http://www.tddft.org/TDDFT2008/talks/KB.pdf
see also http://www-fourier.ujf-grenoble.fr/~joye/simon2006.pdf, esp. its ref. 93
I'm glad to see computational research is becoming more popular and accepted. There's a (quickly diminishing) subset of scientists who think computational work is too theoretical, inaccurate, and inapplicable to real-life. This was the case when the field was developed, but it is no longer true.
The exponentially increasing computational power is allowing discoveries that simply can not be performed experimentally because laboratory technology just isn't advanced or capable enough. Who needs to actually study reality if you can just simulate what you need -- and the end result is the same?
I'm not sure many people know this, but our understanding of the laws of physics is advanced enough nowadays to describe almost perfectly everything we observe in everyday life. (Exceptions include things like quantum gravity which don't really matter [ducks to avoid physicists]).
The problem is that if you want to simulate these laws, it requires a lot of computational power. Brute-force approaches are simply ineffective and so simplifications and clever techniques must be developed to reduce the computational effort while giving increasingly accurate results. I think it's the combination of improving computational resources and improving simulation algorithms that are really driving this field.
We calculated heats of formation in our chemical thermodynamics course, but we were always given ample data, and we knew what we were trying to calculate.
If you think you can do the same thing for ethene -> butadiene, you'll find yourself to be horribly wrong, because of extended conjugation networks.
So while for some cases it works, it is not always simple to go from known empiricial results to more complex structures using tables and addition and subraction. And in the case of the molecules I care about, there is pretty good reason to believe that the simple linear methods will fail.
We then built an instrument which could measure the results experimentally. Since this system was so well described by simulation and theory, we used it as a calibration sample before doing real science. Except the results didn't match the computations at all.
I spent years tracking down problems in the instrument (some of which were real) before finally setting in on the fact that the theory and calculations were simply wrong. Just as you're always experimentally limited in what you can measure, you're resource constrained in what you can calculate. Sometimes, you need to drop down another physics abstraction level before you can get the right answer. However, experiments don't care about your abstraction level and get it right every time.
Performing physical experiments is a waste of time and money if you can just simulate the results. However, you don't know if you can simulate it until you've performed the experiment. Every scientist I've known has, at least once, run a simulation and later found that it wasn't even close to the experimental results. Sometimes it's a leaky abstraction in the simulation (e.g. Ignoring the anti-reflective coating on a mirror). Sometimes it's a leaky abstraction in the experiment (e.g. ignoring the pH differences between H2O and D2O). Sometimes it's new physics, but not very often. Sometimes it's exactly like you predicted. Until you leave the keyboard and enter the lab, you won't know.
(sorry for the sheepish comment but this thinking is dangerous; look into some of the current problems in http://en.wikipedia.org/wiki/Quantum_biology)
This is a gross understatement and fallacious. Many of the problems being studied today would take millions/billions/trillions of years of computation in order to model... with approximations. Using even the largest clusters on the planet we can't even find the ground state of even the smallest protein (with ab-initio). DFT scales cubically with system size, and DFT is an approximation to actual first principles calculations that are impossibly enormous and can never be fully calculated.
The number of systems we can accurately model is minuscule, the number of open problems in computational chemistry and materials science is enormous.
I wouldn't go so far as to say that.
A quantum computer can handle the fermion sign problem by scaling polynomially with the number of particles instead of exponentially. Estimates for if/when such a practical device will be created vary wildly but I would think the possibility of this, along with new techniques that take advantage of the redundancy inherent to certain categories of problems and efficiently diagonalize the Hamiltonian could accelerate the rate at which we can handle larger and larger systems. It's kind of hard to predict what breakthroughs will be made, but I'm staying optimistic.
EDIT: Then again, looking at your posts on here, I suspect you already know all that ;)