6 ms·

Learning Quantum Mechanics: Machines vs. Humans [video]

38 points by bendyBus 12y ago

bendyBus 12y ago
There is a lot of hype surrounding Deep Neural Networks which seem to be able to solve extremely challenging machine learning problems with almost no human tuning of parameters. 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.
Xcelerate 12y ago
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 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.104.136403 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.
- bendyBus 12y ago
  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 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.87.184115 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!
  - Xcelerate 12y ago
    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?
b3njamin 12y ago
Has this video been restricted lately? It keeps showing the message: Sorry Because of its privacy settings, this video cannot be played here.
- theoengland 12y ago
  Hey, it should be OK. You have to sign up as a Skills Matter member, it's free.
  - b3njamin 12y ago
    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.

38 points by bendyBus 12y ago

There is a lot of hype surrounding Deep Neural Networks which seem to be able to solve extremely challenging machine learning problems with almost no human tuning of parameters. 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.

Xcelerate 12y ago

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 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.104.136403 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.

bendyBus 12y ago
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 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.87.184115 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!
- Xcelerate 12y ago
  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?

b3njamin 12y ago

Has this video been restricted lately? It keeps showing the message: Sorry Because of its privacy settings, this video cannot be played here.

theoengland 12y ago
Hey, it should be OK. You have to sign up as a Skills Matter member, it's free.
- b3njamin 12y ago
  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.