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added stuff about the alchemical degree of freedom
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Andrey Poletayev
Andrey Poletayev
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Since this question was asked, several all-elements, or "universal", machine-learning force fields have been published. These include M3GNet, CHGNet, and MACE. Several semi-universal force fields encompassing a subset of elements are also published, such as ANI focusing on organics and more recently BAMBOO for liquids. Most of these are pre-trained neural networks of varying complexity and architecture. The field is lively enough that the list is probably not exhaustive.

A few general notes are perhaps worth mentioning with respect to these:

  • While they are trained on a specific DFT functional each, it is possible to fine-tune them to make a switch. For example, CHGNet is based on PBE/PBE+U, but it is possible to fine-tune at the meta-GGA level for a specific system of interest. However, architecture can not be changed within one framework and will likely limit errors within a framework even with perfect and abundant fine-tuning.
  • Like all neural nets, these are not universally stable. For example, CHGNet and MACE (at least) were trained on Materials Project data. They will still break if, for example, a molecular dynamics run takes them far enough out of their training data. Fine-tuning can help.
  • To my knowledge, none of these deal with magnetism; CHGNet can take magnetic moments as input, but includes an absolute value somewhere in the layers, so will not distinguish an antiferromagnetic configuration from a ferromagnetic one.
  • More to the point of the original question, none of these were trained with VASP's engine, which is based, to within reasonable approximation, on Gaussian process regression (GPR). This is because GPR does not scale well with the number or diversity of training structures: its runtime (inference) memory requirements grow with the pool of training structures. For neural nets, that is not a limitation since the weights are fixed for inference once training is completed and only the network size and structure determine inference speed. To my knowledge there has been no implementation, in VASP or elsewhere, of considering the chemical element as an "alchemical" degree of freedom in GPR.

Since this question was asked, several all-elements, or "universal", machine-learning force fields have been published. These include M3GNet, CHGNet, and MACE. Several semi-universal force fields encompassing a subset of elements are also published, such as ANI focusing on organics and more recently BAMBOO for liquids. Most of these are pre-trained neural networks of varying complexity and architecture. The field is lively enough that the list is probably not exhaustive.

A few general notes are perhaps worth mentioning with respect to these:

  • While they are trained on a specific DFT functional each, it is possible to fine-tune them to make a switch. For example, CHGNet is based on PBE/PBE+U, but it is possible to fine-tune at the meta-GGA level for a specific system of interest. However, architecture can not be changed within one framework and will likely limit errors within a framework even with perfect and abundant fine-tuning.
  • Like all neural nets, these are not universally stable. For example, CHGNet and MACE (at least) were trained on Materials Project data. They will still break if, for example, a molecular dynamics run takes them far enough out of their training data. Fine-tuning can help.
  • To my knowledge, none of these deal with magnetism; CHGNet can take magnetic moments as input, but includes an absolute value somewhere in the layers, so will not distinguish an antiferromagnetic configuration from a ferromagnetic one.
  • More to the point of the original question, none of these were trained with VASP's engine, which is based, to within reasonable approximation, on Gaussian process regression.

Since this question was asked, several all-elements, or "universal", machine-learning force fields have been published. These include M3GNet, CHGNet, and MACE. Several semi-universal force fields encompassing a subset of elements are also published, such as ANI focusing on organics and more recently BAMBOO for liquids. Most of these are pre-trained neural networks of varying complexity and architecture. The field is lively enough that the list is probably not exhaustive.

A few general notes are perhaps worth mentioning with respect to these:

  • While they are trained on a specific DFT functional each, it is possible to fine-tune them to make a switch. For example, CHGNet is based on PBE/PBE+U, but it is possible to fine-tune at the meta-GGA level for a specific system of interest. However, architecture can not be changed within one framework and will likely limit errors within a framework even with perfect and abundant fine-tuning.
  • Like all neural nets, these are not universally stable. For example, CHGNet and MACE (at least) were trained on Materials Project data. They will still break if, for example, a molecular dynamics run takes them far enough out of their training data. Fine-tuning can help.
  • To my knowledge, none of these deal with magnetism; CHGNet can take magnetic moments as input, but includes an absolute value somewhere in the layers, so will not distinguish an antiferromagnetic configuration from a ferromagnetic one.
  • More to the point of the original question, none of these were trained with VASP's engine, which is based, to within reasonable approximation, on Gaussian process regression (GPR). This is because GPR does not scale well with the number or diversity of training structures: its runtime (inference) memory requirements grow with the pool of training structures. For neural nets, that is not a limitation since the weights are fixed for inference once training is completed and only the network size and structure determine inference speed. To my knowledge there has been no implementation, in VASP or elsewhere, of considering the chemical element as an "alchemical" degree of freedom in GPR.
Source Link
Andrey Poletayev
Andrey Poletayev
  • 1.7k
  • 8
  • 29

Since this question was asked, several all-elements, or "universal", machine-learning force fields have been published. These include M3GNet, CHGNet, and MACE. Several semi-universal force fields encompassing a subset of elements are also published, such as ANI focusing on organics and more recently BAMBOO for liquids. Most of these are pre-trained neural networks of varying complexity and architecture. The field is lively enough that the list is probably not exhaustive.

A few general notes are perhaps worth mentioning with respect to these:

  • While they are trained on a specific DFT functional each, it is possible to fine-tune them to make a switch. For example, CHGNet is based on PBE/PBE+U, but it is possible to fine-tune at the meta-GGA level for a specific system of interest. However, architecture can not be changed within one framework and will likely limit errors within a framework even with perfect and abundant fine-tuning.
  • Like all neural nets, these are not universally stable. For example, CHGNet and MACE (at least) were trained on Materials Project data. They will still break if, for example, a molecular dynamics run takes them far enough out of their training data. Fine-tuning can help.
  • To my knowledge, none of these deal with magnetism; CHGNet can take magnetic moments as input, but includes an absolute value somewhere in the layers, so will not distinguish an antiferromagnetic configuration from a ferromagnetic one.
  • More to the point of the original question, none of these were trained with VASP's engine, which is based, to within reasonable approximation, on Gaussian process regression.