v2.0.0-beta.2

New features:

• Add subcommand and python interface to calculate model-deviation (#715)

Enhancements

• Use fmod to wrap the coord of atoms. UT for force/virial ops (#741)
• UT for model devi C++ interface (#731)
• add CUDA/ROCM buidling documents (#739)
• add op unittests for prod_force, prod_virial, prod_force_grad and prod_virial_grad (#703)

Bug fixings:

• fix bug of adding int to a None random seed (#705)
• reuse the zero layer rather than building a new one (#714)
• fix CI (#739)

v2.0.0-beta.1

New hardware support:

• ROCM support

Enhancement:

• Document and examples for the C++ interface
• Instructions for the i-pi
• Document for the network size and sel

Bug fixing:

• Illegal nlist #680
• Bug in prod_virial_grad that causes wrong results when training with virials #685
• Uniform random seed #691

v2.0.0-beta.0

Increment to v2.0.0-alpha:

New features:

• Atom type embedding
• Model deviation for virial

Enhancement:

• Improved documentation
• Better support for dipole and polarizability learning
• bit operations to encode neighbor information
• MPI support for atomic model deviation #628
• UT for GPU code #569
• UT for model compression #586
• Test Lammps build #600

Bug fixings

• cuda memory access error #566
• relative force model deviation is not copied back at single precision #599
• correct way of allocating memory in float precision #612
• fix TB logdir remove bug #617
• Append out_file when lammps restarts #640

v2.0.0-alpha.1

What's new to v2.0.0-alpha.0

• Training and inference the dipole (vector).
• Split of training and validation dataset.

Enhancement:

• Strict argument check in the input script.
• Update readme for v2.0
• Auto conversion of input file to v2.0 compatibility

Bug fixings:

• Fix bugs of broken examples.

v2.0.0-alpha.0

The very first alpha release of deepmd-kit version 2.0.0. It includes the following new features

• Model compression
• New descriptor: three body embedding
• Hybridization of descriptors
• Long-range modification
• Type embedding (under development)
• Training and inference the dipole (vector) and polarizability (matrix). (under development)
• Split of training and validation dataset. (under development)
• ROCm device support (under development)

Enhancements

• More efficient training: all customized OPs are implemented with GPU.
• Parallel training with multiple GPU support (under development)

Improvement of the code for developers

• Supports version of the model. Easily check model compatability
• Clear and pythonic python interface
• C++ API that can be tested independently
• OP supports multi-device.

Bug fixings:

• remove using namespace std. Solves compiling compatability problem.
• added deepmd namespace for the C++ API

v1.3.3

Bug fixing:

• Fix lammps memory leak when initialized pair_style deepmd for multiple times. #392
• Fix GPU memory issues. #393 #407 #424

v1.3.2

Improvements:

• Compiling with Cuda 11.0 and 11.1 #390

Bug fixings:

• Fix stress tensor bug of ASE interface #338
• Fix neighbor list bug that may cause inconsistent results between CPU and GPU #334

v1.2.4

Bug fixings

• Fix several compiling issues of v1.2.3

v1.2.3

New feature:

• added tool to convert a v1.2 compatible model to v1.3 compatible model.

Bug fixing:

• neighbor list bug in the GPU implementation.

v1.3.1

Bug fixing:

• Compulsory label requirement if the corresponding prefactor is set to non-zero. The current behavior is when then label is missing, the corresponding term does not appear in the loss function
• Optional requirement of loss in dipole and polarizability training

Improvement:

• Recommend consistent TensorFlow versions for python and C++ interfaces.