Learning Quantum Mechanics: Machines vs. Humans [video](skillsmatter.com)
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Learning Quantum Mechanics: Machines vs. Humans [video]
https://skillsmatter.com/skillscasts/6109-learning-quantum-mechanics-machines-versus-humans
3 comments
Wow, this is interesting. I came up with an idea similar to this about two years ago, but never really worked on it. I guess I should have! Within the past year this kind of thing has really taken off. This concept is also being used in molecular dynamics. The LAMMPS developers have recently added a new "SNAP" potential that performs a Gaussian approximation of the bispectrum of common atom neighborhood configurations (force field generation on the fly). See http://lammps.sandia.gov/doc/pair_snap.html. The paper on their implementation is available online, but it isn't even published yet. In 2014 alone, the number of papers on machine learning for QM and MD tasks has increased by a HUGE number. I get the impression this kind of thing has turned into a race.
I believe this is the paper that started it all: Bartók 2010 http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.104...
The goal was to remove translational, rotational, and permutative degrees of freedom (DOF) from a collection of atoms, something I had been attempting unsuccessfully on my own for a while. The radial distribution function has traditionally been used a lot in MD, but it only captures a small portion of the total degrees of freedom for a collection of atoms (which is also the reason it's difficult to develop structural molecular models from neutron scattering data alone). The bispectrum on the other hand captures almost all of the DOF in a way that removes the angular dependence. It's ingenious really. It describes the probability distribution of atoms as a projection onto the surface of a 4D sphere, and the locations of the atoms are given by 4D spherical harmonic basis functions. New configurations are then smoothly interpolated from DFT calculations. This even includes the effect of electron correlation in MD! (Well, so far as the functional used in DFT is successful at that task).
The funny thing is that the concept of the bispectrum has been around for a very long time. The Bartók paper cites a 1991 paper on its usage for signal processing. It's always interesting to me how useful cross-discipline research is.
I believe this is the paper that started it all: Bartók 2010 http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.104...
The goal was to remove translational, rotational, and permutative degrees of freedom (DOF) from a collection of atoms, something I had been attempting unsuccessfully on my own for a while. The radial distribution function has traditionally been used a lot in MD, but it only captures a small portion of the total degrees of freedom for a collection of atoms (which is also the reason it's difficult to develop structural molecular models from neutron scattering data alone). The bispectrum on the other hand captures almost all of the DOF in a way that removes the angular dependence. It's ingenious really. It describes the probability distribution of atoms as a projection onto the surface of a 4D sphere, and the locations of the atoms are given by 4D spherical harmonic basis functions. New configurations are then smoothly interpolated from DFT calculations. This even includes the effect of electron correlation in MD! (Well, so far as the functional used in DFT is successful at that task).
The funny thing is that the concept of the bispectrum has been around for a very long time. The Bartók paper cites a 1991 paper on its usage for signal processing. It's always interesting to me how useful cross-discipline research is.
I'm not sure if I would agree with categorising the field as a race. I absolutely agree that the number of people using Machine Learning within the atomistic simulation community is skyrocketing. But everyone is just exploring the range of possibilities and trying to see what the essential elements are for it to be successful. I think having more people working on it is a great thing!
Regarding LAMMPS, actually the GAP code is now also easily usable there with this plugin : https://github.com/libAtoms/QUIPforLAMMPS
The bispectrum is indeed a very powerful tool, but is not the ideal feature vector for representing the atomic environment. You should have a read of Bartok's more recent paper on this: http://journals.aps.org/prb/abstract/10.1103/PhysRevB.87.184... . One of the issues is that the bispectrum starts with an approximation of the neighbourhood atomic density as a sum of delta functions. Trying to represent such sharp features in a basis set expansion is actually very slowly converging. So the idea behind SOAP is to build a covariance kernel by directly comparing a smooth measure of the similarity of environments, which is also invariant to all physically relevant symmetry operations.
I would also like to add that in addition to GAP and SNAP, there are people like Jörg Behler doing this with Neural Networks and Francesco Paesani/Greg Medders with a different regression schemes. But in addition to making potential energy surfaces there are people like Paul Popelier `learning' atomic charges for building force fields and people in Vijay Pande's group doing machine learning on MD trajectories, which is something that excites me a great deal and I would love to understand in more detail.
It's a very exciting time to be in this field!
Regarding LAMMPS, actually the GAP code is now also easily usable there with this plugin : https://github.com/libAtoms/QUIPforLAMMPS
The bispectrum is indeed a very powerful tool, but is not the ideal feature vector for representing the atomic environment. You should have a read of Bartok's more recent paper on this: http://journals.aps.org/prb/abstract/10.1103/PhysRevB.87.184... . One of the issues is that the bispectrum starts with an approximation of the neighbourhood atomic density as a sum of delta functions. Trying to represent such sharp features in a basis set expansion is actually very slowly converging. So the idea behind SOAP is to build a covariance kernel by directly comparing a smooth measure of the similarity of environments, which is also invariant to all physically relevant symmetry operations.
I would also like to add that in addition to GAP and SNAP, there are people like Jörg Behler doing this with Neural Networks and Francesco Paesani/Greg Medders with a different regression schemes. But in addition to making potential energy surfaces there are people like Paul Popelier `learning' atomic charges for building force fields and people in Vijay Pande's group doing machine learning on MD trajectories, which is something that excites me a great deal and I would love to understand in more detail.
It's a very exciting time to be in this field!
Thanks for the reply! This is very useful, particularly the paper you linked. With my research right now, I'm trying to come up with a visual representation of a "characteristic" atomic neighborhood around ions at different energy levels. More specifically, how the distribution of atoms surrounding a high vs low energy ion are different. But visually, there is no easily discernible difference, even though the radial distribution functions are very different for each energy level. That's why I'm studying other forms of distribution representations. Are you a member of one of the groups you listed, or just learning about it for your own research?
Yes, I'm part of the GAP research group (with Albert Bartok Partay and Gabor Csanyi). Representing environments is definitely still an ongoing research project. Other things I've played with in the past include identifying crystal structures at finite temperature (i.e. a classification rather than regression task), or differentiating between amorphous phases (since e.g. water has two amorphous solid phases with a 1st order transition in between, but there is no way in hell you would be able to tell one from the other visually.
We're currently working on a really ambitious new way to represent environments, but it's really preliminary at the moment.
Regarding your ion issue, what about the angular components? The radial functions really only tell you so much...
But more fundamentally, what do you mean by ion energy levels? I'm presuming you mean a metallic nucleus+core electrons, in a condensed phase at finite temperature. But of course that `atomic energy' -insofar as it exists- is a continuous function of position and not quantised, so I'm unsure what you mean by energy levels in this context.
We're currently working on a really ambitious new way to represent environments, but it's really preliminary at the moment.
Regarding your ion issue, what about the angular components? The radial functions really only tell you so much...
But more fundamentally, what do you mean by ion energy levels? I'm presuming you mean a metallic nucleus+core electrons, in a condensed phase at finite temperature. But of course that `atomic energy' -insofar as it exists- is a continuous function of position and not quantised, so I'm unsure what you mean by energy levels in this context.
That sounds like really exciting research. I guess the ultimate goal in representing environments would be to construct a function that captures all the statistically relevant information of a particular "type" of atomic neighborhood. It would reduce the degrees of freedom to the bare minimum necessary to recreate a similar environment that correctly reproduces any property of interest (kind of like data compression for materials). Would that be right?
The deal with the ions is that we are studying the transport and storage of lithium in a new type of carbon anode. The anode consists of small crystalline domains distributed throughout an amorphous carbon matrix. In order to capture all of the features of this material, we end up with a system of almost a million atoms. At the end of an equilibration run, the lithium ions can be found at different locations within the carbon matrix. It turns out that the potential energy of the lithium ions (as computed from the reactive potential the simulation was performed with) has a wide range of values. So we can sort these ions into bins of a histogram (this is what I meant by "energy levels"). And because there are so many samples, the radial distribution functions (RDFs) can be computed for all Li-C pairs in each separate energy bin. (The computed RDFs are useful because the results can be compared to the experimental RDFs obtained from neutron scattering.)
However, zooming in on ions of different potential energies reveals very little visual difference in the local environment. Yet we know there is definitely a difference in the structure because of the RDFs, but we cannot get a good understanding of it or provide a good representation of it. So that's when I discovered the 2010 paper by Bartók. As you mentioned, I want to figure out how the entire local atomic neighborhood affects the energy (as opposed to only the radial component), and I also want to create a 3D graphic that provides a good visualization of the differences between the atomic neighborhoods. In that sense, I need something that would compare the atomic neighborhoods without regard to translation, rotation, reflection, or permutation of identical atoms.
The deal with the ions is that we are studying the transport and storage of lithium in a new type of carbon anode. The anode consists of small crystalline domains distributed throughout an amorphous carbon matrix. In order to capture all of the features of this material, we end up with a system of almost a million atoms. At the end of an equilibration run, the lithium ions can be found at different locations within the carbon matrix. It turns out that the potential energy of the lithium ions (as computed from the reactive potential the simulation was performed with) has a wide range of values. So we can sort these ions into bins of a histogram (this is what I meant by "energy levels"). And because there are so many samples, the radial distribution functions (RDFs) can be computed for all Li-C pairs in each separate energy bin. (The computed RDFs are useful because the results can be compared to the experimental RDFs obtained from neutron scattering.)
However, zooming in on ions of different potential energies reveals very little visual difference in the local environment. Yet we know there is definitely a difference in the structure because of the RDFs, but we cannot get a good understanding of it or provide a good representation of it. So that's when I discovered the 2010 paper by Bartók. As you mentioned, I want to figure out how the entire local atomic neighborhood affects the energy (as opposed to only the radial component), and I also want to create a 3D graphic that provides a good visualization of the differences between the atomic neighborhoods. In that sense, I need something that would compare the atomic neighborhoods without regard to translation, rotation, reflection, or permutation of identical atoms.
Has this video been restricted lately? It keeps showing the message:
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Hey, it should be OK. You have to sign up as a Skills Matter member, it's free.
Thank you theoengland. I registered with skillsmatter now, but the message keeps showing. I'll take it to SM in order not to waste HN. Thank you for your help.
This is an informal talk at the big-O meetup in London discussing the application of machine learning to quantum mechanical simulations of atoms and molecules. This is a particularly demanding application of machine learning techniques. The requirements for regression accuracy are very high, and in addition a number of physical laws need to be obeyed by the learning algorithm. The point of the talk is to invite discussion about the relative merits of domain expertise versus general-purpose algorithms for high-performance machine learning.