#include <CatLearnPot.h>

Inheritance diagram for CatLearnPot:

Public Member Functions
	CatLearnPot (const eonc::Parameters &a_params)
void	train_optimize (const MatrixXd &features, const MatrixXd &targets) override
void	force (long nAtoms, const double positions, const int atomicNrs, double forces, double energy, double variance, const double box) override
bool	isThreadSafe () const noexcept override
	Whether this potential's force() can be called from multiple threads on the SAME instance.
Public Member Functions inherited from eonc::SurrogatePotential
	SurrogatePotential (PotType a_ptype, const Parameters &a_params)
virtual	~SurrogatePotential ()=default
bool	isSurrogate () const noexcept override
	Whether this is a surrogate (GP) potential.
std::tuple< double, AtomMatrix, double >	get_ef_var (const AtomMatrix pos, const VectorXi atmnrs, const Matrix3d box)
Public Member Functions inherited from eonc::Potential
	Potential (PotType a_ptype)
	Production default: construction-scope registry, else PotRegistry::get().
	Potential (PotType a_ptype, IPotRegistry &registry)
	Test seam: injected registry, no process-default get() counters.
	Potential (PotType a_ptype, const Parameters &p)
	Potential (const Parameters &a_params)
virtual	~Potential ()
void	force (std::span< const double > positions, std::span< const int > atomicNrs, std::span< double > forces, double energy, double variance, std::span< const double > box)
	C++ call site: size-checked view over the raw FFI force().
virtual void	setFixedMask (long nAtoms, const double *isFixed)
	Optional frozen-atom mask (nAtoms*3, 1.0 = fixed).
std::tuple< double, AtomMatrix >	get_ef (const AtomMatrix &pos, const VectorXi &atmnrs, const Matrix3d &box)
PotType	getType () const
virtual double	finiteCutoff () const noexcept
	Finite interaction range in position length units.
virtual bool	requiresIsolatedMoleculeLayout () const noexcept
	True for molecular QM / non-PBC backends (NWChem socket, ASE ORCA/NWChem, …).
virtual unsigned	layoutFlags () const noexcept
virtual bool	isSharedInstanceThreadSafe () const noexcept
	Conservative gate for sharing one Potential instance across threads.
virtual bool	needsPerImageInstance () const noexcept
	Whether NEB should create separate Potential instances per image for true parallel force evaluation.
virtual std::shared_ptr< Potential >	clonePotential () const
	Independent instance that does not reload from disk.
virtual bool	supportsBatchEvaluation () const noexcept
	Whether this potential supports batched evaluation of N systems in a single call.
virtual bool	computesStress () const noexcept
	True when force() leaves a Cauchy stress that cauchyStress() can read until the next force() on this instance.
virtual Matrix3d	cauchyStress () const
	Cauchy stress in eV/Angstrom^3.
virtual void	forceBatchOwned (long nSystems, long nAtoms, const double const positions, const int const atomicNrs, double const forces, double energies, double variances, const double const boxes, const long *owners)
	Evaluate forces for N systems in a single call.
virtual void	forceBatch (long nSystems, long nAtoms, const double const positions, const int const atomicNrs, double const forces, double energies, double variances, const double const boxes)

Public Attributes
py::object	m_gpmod
Public Attributes inherited from eonc::Potential
std::atomic< size_t >	forceCallCounter

Additional Inherited Members
Public Types inherited from eonc::Potential
enum class	PotLayout : unsigned { InProcess = 1u << 0 , NeedsWorkingDirectory = 1u << 1 , Subprocess = 1u << 2 }
	How the pot is executed. Combine with bitwise or. More...
Protected Attributes inherited from eonc::Potential
PotType	ptype

Detailed Description

Definition at line 24 of file CatLearnPot.h.

Constructor & Destructor Documentation

◆ CatLearnPot()

CatLearnPot::CatLearnPot ( const eonc::Parameters & a_params )

Definition at line 17 of file CatLearnPot.cpp.

    : eonc::SurrogatePotential(eonc::PotType::CatLearn, a_params) {
  using namespace pybind11::literals;
  py::module_ sys = py::module_::import("sys");
  sys.attr("path").attr("insert")(0, a_params.catlearn_options().path);
 
  py::module_ gp_module = py::module_::import(
      "catlearn.regression.gaussianprocess.calculator.mlmodel");
 
  // Import the required modules
  // GP Model
  this->m_gpmod = gp_module.attr("get_default_model")(
      "model"_a = a_params.catlearn_options().model);
};

Member Function Documentation

◆ force()

void CatLearnPot::force	(	long	nAtoms,
		const double *	positions,
		const int *	atomicNrs,
		double *	forces,
		double *	energy,
		double *	variance,
		const double *	box )

overridevirtual

Implements eonc::Potential.

Definition at line 38 of file CatLearnPot.cpp.

                                                             {
  using namespace pybind11::literals;
  (void)atomicNrs;
  (void)box;
  py::gil_scoped_acquire gil;
  const Eigen::Map<const MatrixXd> features(positions, 1, nAtoms * 3);
  py::tuple ef_and_unc = (this->m_gpmod.attr("predict")(
      features, "get_variance"_a = true, "get_derivatives"_a = true));
  auto ef_dat = ef_and_unc[0].cast<MatrixXd>();
  auto vari = ef_and_unc[1].cast<MatrixXd>();
  auto gradients = ef_dat.block(0, 1, 1, nAtoms * 3);
  for (int idx = 0; idx < nAtoms; idx++) {
    forces[3 * idx] = gradients(0, 3 * idx) * -1;
    forces[3 * idx + 1] = gradients(0, 3 * idx + 1) * -1;
    forces[3 * idx + 2] = gradients(0, 3 * idx + 2) * -1;
  }
  if (variance != nullptr) {
    *variance = vari(0, 0); // energy variance only
  }
  *energy = ef_dat(0, 0);
  return;
}

◆ isThreadSafe()

bool CatLearnPot::isThreadSafe ( ) const

inlinenodiscardoverridevirtualnoexcept

Whether this potential's force() can be called from multiple threads on the SAME instance.

Python-based potentials return false. Potentials with internal mutex (MetatomicPotential) return true but serialize internally – use needsPerImageInstance() to check if separate instances would enable true parallelism.

Reimplemented from eonc::Potential.

Definition at line 35 of file CatLearnPot.h.

35{ return false; }

◆ train_optimize()

void CatLearnPot::train_optimize	(	const MatrixXd &	features,
		const MatrixXd &	targets )

overridevirtual

Implements eonc::SurrogatePotential.

Definition at line 32 of file CatLearnPot.cpp.

                                                          {
  m_gpmod.attr("optimize")(features, targets, py::arg("retrain") = true);
  return;
}

Member Data Documentation

◆ m_gpmod

py::object CatLearnPot::m_gpmod

Definition at line 36 of file CatLearnPot.h.

The documentation for this class was generated from the following files:

/home/runner/work/eOn/eOn/include/eon/potentials/CatLearnPot/CatLearnPot.h
/home/runner/work/eOn/eOn/client/potentials/CatLearnPot/CatLearnPot.cpp

Public Member Functions

Public Attributes

Additional Inherited Members