On sierra 10.12 need to modify the SH.hpp file in order to compile

[thegodone]

I receive to compile on sierra 10.12 by changing the SH.hpp file: replace those two lines:

include <tr1/array>

using namespace std::tr1; by this:

include

//using namespace std::tr1;

in this bloc of conditions:

ifdef clang

if __clang_major__ >= 7

include

using namespace std;

else

include

include

//using namespace std::tr1;

endif

else

include

include

using namespace std;

endif

[jparkhill]

Thanks for the comments Guillaume. Apple's version of Clang is a source of persistent frustration. I will add some more macros to deal with Sierra.

If you would like a pre-trained network for CHNO Place the three files in https://drive.google.com/drive/folders/14QioAgc-1IbKxhFMtQjXH4uFme2mekCP?usp=sharing into /TensorMol/networks.

You can then evaluate periodic or aperiodic forces following the examples in /samples/test_neb.py Ie: python samples/test_neb.py

Best- John

[thegodone]

Dear John,

I follow instructions in the supporting file of your article. It's working fine.

I would like to use your model to explain GCMS fragmentation based on bond NRJ (my molecules are mostly CHNO).

I will like to use RDKit 3D generated molecules (or corina).

Did you check your model error on those type of "none 3D ab initio optimized" molecules ?

Thanks to sharing your model.

Best regard,

Guillaume

2017-11-11 16:45 GMT+01:00 John Parkhill notifications@github.com:

Thanks for the comments Guillaume. Apple's version of Clang is a source of persistent frustration. I will add some more macros to deal with Sierra.

If you would like a pre-trained network for CHNO Place the three files in https://drive.google.com/drive/folders/14QioAgc- 1IbKxhFMtQjXH4uFme2mekCP?usp=sharing into /TensorMol/networks.

You can then evaluate periodic or aperiodic forces following the examples in /samples/test_neb.py Ie: python samples/test_neb.py

Best- John

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[jparkhill]

Guillaume- That's an interesting idea. The bond neural network was trained on Ab-Initio minimum geometries, because it's all we had available at the time. It won't do very well if the bond lengths are far from minimum. You easily could use the BP network I just provided to optimize molecules with high-throughput if the bond energies that you get from the old network with the geometries you feed in seems poor.

Since the last paper we've put together better datasets, that we could use to make bond energy predictions away from equilibrium. Kun simply hasn't had the time to train the bond network with the new data. Don't hesitate to keep in touch for assistance or collaboration.

Best- John