Simulations
Singlepoint calculations
Singlepoint calculations can be performed for either given geometries (one or many) or generic input vector(s) X (often, molecular descriptors representing a molecule) and can be performed either with a pretrained model supported by MLatom or with a usertrained model.
Input arguments
The user has to choose at least one model either with MLmodelIn
or by giving the name of a pretrained model.
At least one of the arguments, XYZfile
or XfileIn
, should be chosen.
useMLmodel MLmodelIn=[file with ML model]
orAIQM1
,ANI1ccx
, …One and only one of these two options can be chosen.
useMLmodel MLmodelIn
: see usertrained models for details. No default parameters toMLmodelIn
are provided. Requests to read a file with ML model.AIQM1
,ANI1ccx
, …: see pretrained models for a full list. This argument is optional and no default parameters are provided. Requests one of the pretrained models supported by MLatom.
XYZfile=[file with XYZ coordinates]
orXfileIn=[file with input vectors X]
One and only one of these two options can be chosen. No default file names.
XYZfile
: requests to make predictions for one or many molecules provided in file with their XYZ coordinates. The units of coordinates depend on the model. For pretrained models Å should be used.XfileIn
: requests to make predictions for provided list of input vectors (one input vector per line in text file), which are typically molecular descriptors.
Output file arguments
At least one of the below arguments is required.
YestFile=[file with estimated Y values]
This argument is optional and no default parameters are provided.
Saves predictions Y to the requested file. If a file with the same name already exists, program will terminate and not overwrite it. If predictions are made with pretrained models, they are energies in Hartree. Other predictions depend on a model.
YgradXYZestFile=[file with estimated XYZ gradients]
This argument is optional and no default parameters are provided.
Should be used only with XYZfile option. Saves predicted XYZ gradients (first derivatives) to the requested file. If a file with the same name already exists, program will terminate and not overwrite it. If predictions are made with pretrained models, they are XYZ gradients in Hartree/Å.
YgradEstFile=[file with estimated gradients]
This argument is optional and no default parameters are provided.
Should be used only with XfileIn option. Saves predicted gradients (first derivatives) wrt to elements of input vector X to the requested file. If a file with the same name already exists, program will terminate and not overwrite it.
Note
Calculations with AIQM1based models also generate additional output files, i.e., if one needs other properties than available via the above options, one should use a single XYZ geometry and look at the MNDO output file mndo.out
.
Example
Singlepoint calculations of energies (and gradients if needed) of closedshell molecules in electronic ground state is the simplest job, which can be run with 34 line MLatom input file, e.g., sp.inp
:
AIQM1 # or useMLmodel MLmodelIn=CH3Cl.unf if you, e.g., want to use CH3Cl.unf model
xyzfile=sp.xyz
yestfile=enest.dat
ygradxyzestfile=xyzgradest.dat
This input requires a sp.xyz
file with XYZ geometries of molecules (you can provide many molecules as usual for MLatom), e.g., for hydrogen and methane sp.xyz
file can look like (geometries in Å):
2
H 0.000000 0.000000 0.363008
H 0.000000 0.000000 0.363008
5
C 0.000000 0.000000 0.000000
H 0.627580 0.627580 0.627580
H 0.627580 0.627580 0.627580
H 0.627580 0.627580 0.627580
H 0.627580 0.627580 0.627580
After you prepared your input files sp.inp
and sp.xyz
, you can run MLatom as usual:
mlatom sp.inp > sp.out
After the calculations finish, MLatom output sp.out
will contain information depending on the model, e.g., for AIQM1 it will contain the standard deviation of NN prediction and components of AIQM1 energies:
Standard deviation of NN contribution : 0.00892407 Hartree 5.59994 kcal/mol
NN contribution : 0.00210898 Hartree
Sum of atomic self energies : 0.08587317 Hartree
ODM2* contribution : 1.09094119 Hartree
D4 contribution : 0.00000889 Hartree
Total energy : 1.17893224 Hartree
Standard deviation of NN contribution : 0.00025608 Hartree 0.16069 kcal/mol
NN contribution : 0.00958812 Hartree
Sum of atomic self energies : 33.60470494 Hartree
ODM2* contribution : 6.86968756 Hartree
D4 contribution : 0.00010193 Hartree
Total energy : 40.46490632 Hartree
In any case, MLatom will save predicted values in file enest.dat
which for above calculations will contain AIQM1 energies in Hartree:
1.178932238420
40.464906315250
and XYZ gradients in Hartree/Å will be saved in file xyzgradest.dat
looking like this:
2
0.000000000000 0.000000000000 0.000032023551
0.000000000000 0.000000000000 0.000032023551
5
0.000000000000 0.000000000000 0.000000000000
0.000490470799 0.000490470714 0.000490470881
0.000490470799 0.000490470714 0.000490470881
0.000490470799 0.000490470714 0.000490470881
0.000490470799 0.000490470714 0.000490470881
Note
Your output may have very minor numerical differences.
Geometry optimization
Geometry optimizations can be performed for given geometries (one or many) with either a pretrained model supported by MLatom or a usertrained model. Optimizations are performed using thirdparty software (Gaussian or ASE), please see the installation of thirdparty software packages Gaussian and ASE; the experimental option is to use SciPy geometry optimization, but it is not well tested. On the MLatom@XACS cloud, ASE is used by default.
Input and output arguments
The user has to choose a task (minimum energy or TS geometry optimization), provide initial geometry, and choose one of the models or methods.
type of task (either
geomopt
orTS
)
initial XYZ coordinates with
xyzfile
argumentprogram for optimization with
optprog
argumentfile with optimized XYZ geometries with
optxyz
argument
Task
Choose the tpye of task, one and only one of these arguments is required.
geomopt
: requests minimumenergy geometry optimization.
TS
: requests optimization of a transition state structure. Only works via interface to Gaussian.
Method or model specification
Universal ML and QM/ML methods
If one of the known universal ML models or hybrid QM/ML methods are requested, their names should be provided as
AIQM1
orANI1ccx
(see manual), e.g., input file will look like:geomopt AIQM1 xyzfile=ts_opt.xyz
QM methods
If the QM method is used, please refer to the manual, in short, QM method and program should be provided with
method
andqmprog
arguments like this:geomopt method=B3LYP/631G* qmprog=gaussian xyzfile=ts_opt.xyz
ML models
MLmodelIn=[file with ML model] MLmodelType=[one of the supported types]
: requests to read a file with ML model of the supported type (see manual). Note that model should predict energies in Hartree for a successful geometry optimizations. Example of input:geomopt MLmodelIn=ani.pt MLmodelType=ANI xyzfile=ts_opt.xyz
Initial XYZ coordinates
XYZfile=[file with XYZ coordinates]
: it is required; no default parameters are provided. The units of coordinates should be Å.
Provide the file with initial XYZ coordinates of one or many molecules.
Program for optimization
optprog=[either Gaussian or ASE or SciPy]
: chooses a thirdparty program for optimization. By default, MLatom will try to use Gaussian. If Gaussian is not
found, it will try to use ASE. If ASE is not found, it will try to use SciPy. Default algorithms in Gaussian, ASE,
and SciPy are Berny optimization, LBFGS, and BFTS, respectivel. Optimizations settings can be changed by using
additional options for the thirdparty program.
If ASE is used, it may terminate after maximum number of iterations without informing a user that geometry is not optimized.
Output files
optxyz=[file with optimized XYZ coordinates]
: saves optimized geometries in the requested file. The default file name is optgeoms.xyz
.
In addition, geometry optimizations will dump many useful files:
optimized geometries in
optgeoms.xyz
or other file saved under name requested withoptxyz=
.the optimization trajectories in XYZ format
opttraj1.xyz
and JSON formatopttraj1.json
and so on for each molecule.in case of optimizations with the Gaussian optimizer, you will also get the corresponding input and output files
gaussian1.com
andgaussian1.log
etc for each molecule.
If you want to control how much information is saved (e.g., for big molecules and many molecules):
printmin
will not print information about every iteration.printall
will print detailed information at each iteration.dumpopttrajs=False
will not dump any optimization trajectories.
Additional options for thirdparty programs
Arguments 
Default parameters 


0.02 

200 

LBFGS 
Example
Geometry optimization of closedshell molecules in the electronic ground state is as simple as running single point
calculations and a 4line MLatom input file, e.g., opt.inp
, looks like this:
AIQM1 # or useMLmodel MLmodelIn=CH3Cl.unf if you, e.g., want to use CH3Cl.unf model
xyzfile=init.xyz
optxyz=opt.xyz
geomopt
This input requires init.xyz
file with initial XYZ geometries of molecules to be optimized (you can provide many
molecules as usual for MLatom), e.g., for hydrogen and methane init.xyz
file can look like (geometries in Å):
2
Hydrogen molecule
H 0.0000000000 0.0000000000 0.0000000000
H 0.7414000000 0.0000000000 0.0000000000
5
Methane molecule
C 0.0000000000 0.0000000000 0.0000000000
H 1.0870000000 0.0000000000 0.0000000000
H 0.3623333220 1.0248334322 0.0000000000
H 0.3623333220 0.5124167161 0.8875317869
H 0.3623333220 0.5124167161 0.8875317869
After you prepared your input files opt.inp
and init.xyz
, you can run MLatom as usual:
mlatom opt.inp > opt.out
MLatom output file opt.out
should contain lines similar to those below (if interface to the Gaussian
program was used for optimization):
******************************************************************************
optprog: Gaussian 16
Standard deviation of NN contribution : 0.00892062 Hartree 5.59777 kcal/mol
NN contribution : 0.00210740 Hartree
Sum of atomic self energies : 0.08587317 Hartree
ODM2* contribution : 1.09094281 Hartree
D4 contribution : 0.00000889 Hartree
Total energy : 1.17893227 Hartree
Standard deviation of NN contribution : 0.00025608 Hartree 0.16069 kcal/mol
NN contribution : 0.00958812 Hartree
Sum of atomic self energies : 33.60470494 Hartree
ODM2* contribution : 6.86968742 Hartree
D4 contribution : 0.00010193 Hartree
Total energy : 40.46490617 Hartree
==============================================================================
Wallclock time: 21.60 s (0.36 min, 0.01 hours)
MLatom terminated on 11.11.2022 at 13:21:37
==============================================================================
After the calculations finish, the optimized geometries are saved in a single file opt.xyz
, which for our example
looks like (geometries in Å, there can be slight numerical differences depending on a machine, etc.):
2
H 0.00770082 0.00000000 0.00000000
H 0.73369918 0.00000000 0.00000000
5
C 0.00000000 0.00000000 0.00000000
H 1.08666998 0.00000000 0.00000000
H 0.36222332 1.02452229 0.00000000
H 0.36222332 0.51226114 0.88726233
H 0.36222332 0.51226114 0.88726233
Frequencies and thermochemistry
Calculation of frequencies and thermochemical properties can be performed for given optimized geometries (one or many) with either a pretrained model supported by MLatom or a usertrained model. The geometries should be first optimized with the same model before these calculations can be performed. Calculations require the use of thirdparty software (Gaussian, ASE or PySCF), please see the installation instructions. On the MLatom@XACS cloud, both ASE and PySCF can be used.
Input arguments
The user has to choose at least one model either with MLmodelIn
or by giving the name of a pretrained model.
freq
This argument is required.
Requests frequencies calculations.
useMLmodel
MLmodelIn=[file with ML model]
This argument is optional and no default parameters are provided.
Requests to read a file with ML model. It is an optional argument and no default models are provided. Note that model should predict energies in Hartree for a successful freq calculations.
ANI1ccx
,AIQM1
, etc.see pretrained model for a full list. This argument is optional and no default parameters are provided.
requests one of the pretrained models supported by MLatom, e.g., AIQM1, ANI1ccx, etc. If AIQM1 or ANI1ccx is requested, MLatom will also calculate enthalpies of formation at 298 K, see tutorial. The standard deviation of neural networks in AIQM1 and ANI1ccx models will be reported and if it is larger than 0.41 and 1.68 kcal/mol, respectively, predicted enthalpies of formation have potentially too high uncertainty and a warning will be reported in the output file.
XYZfile=[file with optimized XYZ coordinates]
This argument is required; no default parameters are provided.
File with optimized XYZ coordinates of one or many molecules. The units of coordinates should be Å.
freqprog=[Gaussian, PySCF or ASE]
By default, MLatom will try to use Gaussian. If Gaussian is not found, it will try to use PySCF, then ASE.
Chooses a thirdparty program for frequencies calculations.
Note
Frequency calculations with Gaussian program also generate Gaussian output files mol_1.log
, mol_2.log
, etc, which contain additional information.
Additional options for thirdparty programs
ase.linear=N,…,N
Available parameters are 0 (default) and 1.
0 for nonlinear molecule, 1 for linear molecule. The order is the same as in XYZ file.
ase.symmetrynumber=N,…,N
Available parameter is 1 (default).
rotational symmetry number for each molecule (see Table 10.1 and Appendix B of C. Cramer “Essentials of Computational Chemistry”, 2nd Ed.). This number only affect the results of entropy and free energy, but this influence is usually very small. The order is the same as in XYZ file.
Point Group 
Symmetry number 

\(C_1\) 
\(1\) 
\(C_i\) 
\(1\) 
\(C_s\) 
\(1\) 
\(C_{{\infty}v}\) 
\(1\) 
\(D_{{\infty}h}\) 
\(2\) 
\(S_n,n=2,4,6,...\) 
\(n/2\) 
\(C_n,n=2,3,4,...\) 
\(n\) 
\(C_{nh},n=2,3,4,...\) 
\(n\) 
\(C_{nv},n=2,3,4,...\) 
\(n\) 
\(D_n,n=2,3,4,...\) 
\(2n\) 
\(D_{nh},n=2,3,4,...\) 
\(2n\) 
\(D_{nd},n=2,3,4,...\) 
\(2n\) 
\(T\) 
\(12\) 
\(T_d\) 
\(12\) 
\(O_h\) 
\(24\) 
\(I_h\) 
\(60\) 
This number only affect the results of entropy and free energy, but this influence is usually very small. The order is the same as in XYZ file.
Example
Thermochemical properties of closedshell molecules in the electronic ground state can be calculated at ANI1ccx or AIQM1 level by adding an argument freq
to the MLatom input file, e.g., freq.inp
, and they should be run on geometries optimized with the corresponding model. An example of MLatom input file using the ANI1ccxoptimized geometries:
ANI1ccx
xyzfile=optgeoms.xyz
freq
freqprog=ASE # or freqprog=gaussian if you choose Gaussian
ase.linear=1,0
ase.symmetrynumber=2,12
When ASE is used for the calculation of thermochemical properties, you should specify ase.linear
and ase.symmetrynumber
this two keywrods. ase.linear
is 0 for nonlinear molecule, 1 for linear molecule, and ase.symmetrynumber
is the
rotational symmetry number for each molecule (see Table 10.1 and Appendix B of C. Cramer “Essentials of Computational
Chemistry”, 2nd Ed.). For example, for hydrogen and methane this two molecules, you should set ase.linear=1,0
and
ase.symmetrynumber=2,12
.
File with preoptimized geometries optgeoms.xyz
for our example are (geometries in Å):
2
hydrogen
H 0.15255733 0.00000000 0.00000000
H 0.58884267 0.00000000 0.00000000
5
methane
C 0.00000000 0.00000000 0.00000000
H 1.08733372 0.00000000 0.00000000
H 0.36244456 1.02514806 0.00000000
H 0.36244456 0.51257403 0.88780426
H 0.36244456 0.51257403 0.88780426
After you prepared your input files freq.inp
and optgeoms.xyz
, you can run MLatom as usual:
mlatom freq.inp > freq.out
After the calculations finish, MLatom output freq.out
will contain the summary with atomization enthalpy at 0 K, ZPVEexclusive atomization energy at 0 K, and heat of formation at 298.15 K for each molecule. If you use ASE, MLatom output will contain the same lines as above, but also include additional data such as entropy and the Gibbs free energy:
......
Zeropoint vibrational energy : 4.07528 kcal/mol
Atomization enthalpy at 0 K : 126.42974 kcal/mol
ZPE exclusive atomization energy at 0 K : 130.50502 kcal/mol
Heat of formation at 298.15 K : 23.11424 kcal/mol
* Warning * Heat of formation have high uncertainty!
......
Zeropoint vibrational energy : 27.87144 kcal/mol
Atomization enthalpy at 0 K : 391.92513 kcal/mol
ZPE exclusive atomization energy at 0 K : 419.79657 kcal/mol
Heat of formation at 298.15 K : 17.63420 kcal/mol
If you use Gaussian, the Gaussian output files of frequency calculations are saved in mol_1.log
, mol_2.log
, … files for each molecule; these files contain ZPVE energy and lots of thermochemical data such as entropy and the Gibbs free energy.
IRC
Intrinsic reaction coordinate (IRC) can be used to check the nature of the optimized TS. It can be performed for given geometries (one or many) with either a pretrained model supported by MLatom or a usertrained model. IRC is performed via interface to the Gaussian program.
Input and output arguments
The user has to choose a task (minimum energy geometry optimization, TS optimization, or IRC) and choose at least one
model either with MLmodelIn
or by giving the name of a pretrained model.
IRC
: requests intrinsic reaction coordinate calculations to follow the reaction path from TS structure. Only works via interface to Gaussian.
optimized XYZ coordinates of TS with
xyzfile
argument
Method or model specification
Universal ML and QM/ML methods
If one of the known universal ML models or hybrid QM/ML methods are requested, their names should be provided as
AIQM1
orANI1ccx
(see manual), e.g., input file will look like:IRC AIQM1 xyzfile=ts_opt.xyz
QM methods
If the QM method is used, please refer to the manual, in short, QM method and program should be provided with
method
andqmprog
arguments like this:IRC method=B3LYP/631G* qmprog=gaussian xyzfile=ts_opt.xyz
ML models
MLmodelIn=[file with ML model] MLmodelType=[one of the supported types]
: requests to read a file with ML model of the supported type (see manual). Note that model should predict energies in Hartree for a successful geometry optimizations. Example of input:IRC MLmodelIn=ani.pt MLmodelType=ANI xyzfile=ts_opt.xyz
Initial XYZ coordinates
XYZfile=[file with XYZ coordinates]
: it is required; no default parameters are provided. The units of coordinates should be Å.
Example
Geometry optimization of closedshell molecules in the electronic ground state is as simple as running single point
calculations and a 4line MLatom input file, e.g., opt.inp
, looks like this:
IRC
AIQM1 # or useMLmodel MLmodelIn=CH3Cl.unf if you, e.g., want to use CH3Cl.unf model
xyzfile=ts_opt.xyz
This input requires ts_opt.xyz
file with initial XYZ geometries of molecules to be optimized (you can provide many
molecules as usual for MLatom), e.g., for the Diels–Alder reaction ts_opt.xyz
file can look like (geometries in Å):
16
C 0.48462430 0.55755495 1.43729151
C 0.48462430 0.55755495 1.43729151
C 0.27595797 1.44977527 0.70359025
C 0.27595797 1.44977527 0.70359025
C 0.27595797 1.45086377 0.69299925
C 0.27595797 1.45086377 0.69299925
H 0.37292526 0.50748993 2.51767690
H 1.44526264 0.21636383 1.06867438
H 0.37292526 0.50748993 2.51767690
H 1.44526264 0.21636383 1.06867438
H 1.05536225 2.01444047 1.21328943
H 1.05536225 2.01444047 1.21328943
H 0.51071931 1.96707995 1.23581344
H 1.20625330 1.32768072 1.23591744
H 0.51071931 1.96707995 1.23581344
H 1.20625330 1.32768072 1.23591744
After you prepared your input files irc.inp
and ts_opt.xyz
, you can run MLatom as usual:
mlatom irc.inp > irc.out
After the calculations finish, the IRC results will be saved in the Gaussian output file gaussian.log
.
Molecular dynamics
MLatom can now perform molecular dynamics of molecular systems with various methods and models due to its interfaces to many famous quantum chemistry and machine learning packages.
Input and output arguments
Arguments 
Available and default parameters 
Description 


0.1 by default 
time step; unit: fs 

1000 by default 
length of trajectory; unit: fs 

required 
userprovided initial geometry (should be in Å) 

required when 
userprovided initial velocity (should be in Å/fs) 

userdefined by default, other options: 
algorithm of generating initial conditions 

300 by default 
initial temperature; unit: K; necessary when 

output file of initial geometry 


output file of initial velocity 



MD thermostat 

300 by default 
environment temperature 

0.2 by default, required when 
collision frequency; unit: fs^{1} 

3 by default, required when 
NoseHoover chain length 

3 by default, required when 
multiple time step 

7 by default, required when 
number of YoshidaSuzuki steps; only 1,3,5,7 are available 

0.0625 by default, required when 
NoseHoover chain frequency; unit: fs^{1} 

traj.h5 by default 
trajectory saved in H5MD file format 

traj by default 
trajectory saved in plain text format 
Example
Below is the example of how to run MD with MLatom:
MD # Molecular dynamics
method=AIQM1 # Use AIQM1
initConditions=userdefined # Use userdefined initial conditions
initXYZ=init.xyz # File with initial geometry
initVXYZ=init.vxyz # File with initial velocities
dt=0.1 # Time step
trun=100000 # Length of trajectory
thermostat=nosehoover # Use NoseHoover thermostat
temperature=300 # Set temperature
trajH5MDout=traj.h5 # Save trajectory in traj.h5
qmprog=mndo
IR and power spectra from MD
MD trajectory can be used to generate IR spectrum and power spectrum.
Arguments 
Available and default parameters 
Description 


required if 
file with trajectory in 

required if 
plain text file containing velocities 

required if 
plain text file containing dipole moments 

0.0 by default 
unit: fs; use trajectory from 

maximum time by default 
unit: fs; use trajectory from 

1024 by default 
autocorrelation depth; unit: fs 

1024 by default 
zero padding; unit: fs 

title of the plot 


its value can be ir or ps 
which spectrum to output 
The plot will be saved in ir.png
or ps.png
, and the spectrum will be saved in ir.npy
or ps.npy
.
Example
Below is an example of how to generate IR spectrum from MD trajectory:
MD2vibr # Generate vibrational spectrum from MD trajectory
trajH5MDin=traj.h5 # Read MD trajectory from traj.h5
dt=0.5 # Time step
start_time=3000 # Start time
end_time=100000 # End time
autocorrelationDepth=1024 # Autocorrelation depth
zeropadding=1024 # Zero padding
output=ir # Generate IR spectrum
Below is an example of how to generate power spectrum from MD trajectory:
MD2vibr
trajH5MDin=traj.h5
dt=0.5
start_time=0
end_time=10000
autocorrelationDepth=1024
zeropadding=1024
output=ps
Simulations with universal MLbased models
MLatom supports calculations with the following pretrained MLbased models (MLatom arguments are spelled exaxtly the same way as the method names given below):
 AIQM1, AIQM1@DFT, AIQM1@DFT* (tutorial with examples and installation instructions)
Strengths: AIQM1 is approaching CCSD(T)/CBS accuracy but with a speed of semiempirical methods (thousand times faster than DFT) for energy calculations and geometry optimizations of closedshell molecules in their ground state. It is also transferable for calculations of charged and radical species as well as for excitedstate calculations with good accuracy. MLatom will also report the standard deviation of neural networks correction and if it is larger than 0.41 kcal/mol the AIQM1 calculations have potentially too high uncertainty and a warning will be reported in the output file if heats of formation are predicted.
Limitations: only CHNO elements are supported. On XACS cloud, no analytical gradients are available, i.e., geometry optimizations and frequencies calculations are rather slow; install local MLatom if higher efficiency is needed.
 ANI1ccx, ANI1x, ANI2x, ANI1xD4 and ANI2xD4 (requires to install TorchANI)
Strengths: Faster than AIQM1. ANI1ccx is also approaching CCSD(T)/CBS accuracy for energy calculations and geometry optimizations of closedshell molecules in their ground state but is generally less accurate and reliable than AIQM1. MLatom will also report the standard deviation of neural networks and if it is larger than 1.68 kcal/mol the ANI1ccx calculations have potentially too high uncertainty and a warning will be reported in the output file if heats of formation are predicted.
Limitations: only CHNO elements are supported by ANI1ccx and ANI1x, CHNOFClS are supported by ANI2x. Not transferable for calculations of charged and radical species or to excitedstate calculations. Not good accuracy for noncovalent interactions if no D4 correction is included.
They can be used for such typical simulations as (see the corresponding sections for more details):
Optional arguments
AIQM1 is using interfaces to MNDO or Sparrow to calculate QM contributions. Thus, the following AIQM1specific arguments can be used:
QMprog=[program]
:MNDO
[default]Sparrow
[default if MNDO is not found]chooses a program for calculating QM part of AIQM1. If neither MNDO or Sparrow program is found, MLatom will not be able to run AIQM1 calculations.
mndokeywords=[file with MNDO keywords, e.g., mndokw]
allows to modify the input to MNDO to request nonstandard calculations, e.g., to define charge, multiplicity, excitedstate calculations settings, convergence criteria, etc. These keywords can be provided to MLatom via a MNDO keyword file, e.g.,
mndokw
file, which should contain at least keywordsiop=22 immdp=1
. For more details see the AIQM1 tutorial and MNDO documentation.
Note
Calculations with AIQM1based models also generate additional output files, i.e., if one needs other properties than available via the above arguments, one should perform calculations on a single XYZ geometry and look at the MNDO output file mndo.out
.
Example
Geometry optimization of closedshell molecules in the electronic ground state is as simple as running single point calculations and a 4line MLatom input file, e.g., opt.inp
, looks like this:
AIQM1 # or ANI1ccx, ANI2x, etc.
xyzfile=init.xyz
optxyz=opt.xyz
geomopt
This input requires init.xyz
file with initial XYZ geometries of molecules to be optimized (you can provide many molecules as usual for MLatom), e.g., for hydrogen and methane init.xyz
file can look like (geometries in Å):
2
Hydrogen molecule
H 0.0000000000 0.0000000000 0.0000000000
H 0.7414000000 0.0000000000 0.0000000000
5
Methane molecule
C 0.0000000000 0.0000000000 0.0000000000
H 1.0870000000 0.0000000000 0.0000000000
H 0.3623333220 1.0248334322 0.0000000000
H 0.3623333220 0.5124167161 0.8875317869
H 0.3623333220 0.5124167161 0.8875317869
After you prepared your input files opt.inp
and init.xyz
, you can run MLatom as usual:
mlatom opt.inp > opt.out
MLatom output file opt.out
should contain lines similar to those below (if interface to the Gaussian program was used for optimization):
******************************************************************************
optprog: Gaussian 16
Standard deviation of NN contribution : 0.00892062 Hartree 5.59777 kcal/mol
NN contribution : 0.00210740 Hartree
Sum of atomic self energies : 0.08587317 Hartree
ODM2* contribution : 1.09094281 Hartree
D4 contribution : 0.00000889 Hartree
Total energy : 1.17893227 Hartree
Standard deviation of NN contribution : 0.00025608 Hartree 0.16069 kcal/mol
NN contribution : 0.00958812 Hartree
Sum of atomic self energies : 33.60470494 Hartree
ODM2* contribution : 6.86968742 Hartree
D4 contribution : 0.00010193 Hartree
Total energy : 40.46490617 Hartree
==============================================================================
Wallclock time: 21.60 s (0.36 min, 0.01 hours)
MLatom terminated on 11.11.2022 at 13:21:37
==============================================================================
After the calculations finish, the optimized geometries are saved in a single file opt.xyz
, which for our example looks like (geometries in Å, there can be slight numerical differences depending on a machine, etc.):
2
H 0.00770082 0.00000000 0.00000000
H 0.73369918 0.00000000 0.00000000
5
C 0.00000000 0.00000000 0.00000000
H 1.08666998 0.00000000 0.00000000
H 0.36222332 1.02452229 0.00000000
H 0.36222332 0.51226114 0.88726233
H 0.36222332 0.51226114 0.88726233
Simulations with QM methods
MLatom supports QM calculations with various popular programs.
Available interfaced QM programs
Arguments
method=[QM method]
(required, case insensitive)
Depending on the available interfaced QM program (see below), supported methods include ab initio, DFT, and semiempirical QM methods. The following shortlist shows same Standard QM methods recognized by MLatom:
usual format such as
B3LYP/631G*
GFN2xTB
(interface to xtb)CCSD(T)*/CBS
(interface to ORCA)ODM2
(interface to MNDO)ODM2*
(interface to MNDO and Sparrow)
qmprog=[supported QM program]
(required, case insensitive)
Brief and incomplete examples for each QM programs are given below
QMprogramKeywords=[file with keywords of QM program]
(optional)
Now only xtb and mndo keywords are supported for the respective programs.
multiplicities=[multiplicities of molecules]
(optional)
Default value is
1
. If more than one molecules are provides, please use comma to separate the multiplicities, e.g.multiplicities=3,3
.charges=[charges of molecules]
(optional)
Default value is
0
. If more than one molecules are provied, please use comma to separate the charges, e.g.charges=1,1
.nthreads=[number of threads used]
(optional)
Default value is
1
Examples of different calculation types
Singlepoint calculations
When doing simulations with QM methods, it is necessary to include
method
andqmprog
keywords in your input file (except for xTB which does not requireqmprog
). The molecular structure file and output file can be defined as usual. See tutorial about the singlepoint calculations. Generally, the input filesp.inp
should look like this:method=B3LYP/631G* qmprog=gaussian xyzfile=sp.xyz yestfile=enest.datwhere the input structure file
sp.xyz
contains:5 C 0.00000000 0.00000000 0.00000000 H 0.62783705 0.62783705 0.62783705 H 0.62783705 0.62783705 0.62783705 H 0.62783705 0.62783705 0.62783705 H 0.62783705 0.62783705 0.62783705After running
$mlatom sp.inp > sp.out
, the output filesp.out
will give calculated single point energy like this: (also in enest.dat)****************************************************************************** You are going to use feature(s) listed below. Please cite corresponding work(s) in your paper: Gaussian program: See the Gaussian output file for the proper citation ****************************************************************************** Energy of molecule 1: 40.5182964000000 Hartree ============================================================================== Wallclock time: 1.07 s (0.02 min, 0.00 hours) MLatom terminated on 08.10.2023 at 10:13:54 ==============================================================================If definition of charges and multiplicities is required, the input file can also look like this:
method=B3LYP/631G* qmprog=gaussian xyzfile=sp.xyz yestfile=enest.dat charges=0,0,1 multiplicities=3,3,1where structures of 3 molecules are included in
sp.xyz
. Note that charges and multiplicities should be defined in the same order.
Frequency calculations
See tutorial about the frequency calculation and thermochemistry. Here is the typical input file:
method=B3LYP/631G* qmprog=gaussian xyzfile=sp.xyz charges=0,0,1 multiplicities=3,3,1 freq freqprog=gaussian # optional (default Gaussian)
Geometry optimization
See tutorial about the geometry optimization. Here is the typical input file:
method=B3LYP/631G* qmprog=gaussian xyzfile=sp.xyz optxyz=opt.xyz charges=0,0,1 multiplicities=3,3,1 geomopt optprog=gaussian # optional (default Gaussian)
Examples for each QM programs
(single point calculation only)
Gaussian
Gaussian is a commonly used QM program that supports various types of calculations with different level of theory. Each Gaussian job should specify both method and basis set (usually separated by
/
), which will be used in method keyword in MLatom. For methods available, see https://gaussian.com/capabilities/?tabid=0.A typical input file of MLatom using Gaussian looks like this:
method=B3LYP/631G* qmprog=gaussian xyzfile=sp.xyz yestfile=enest.dat
PySCF
The Pythonbased Simulations of Chemistry Framework (PySCF) is an opensource Python package that possesses various electronic structure modules. It can be used to simulate the properties of molecules, crystals, and custom Hamiltonians using meanfield and postmeanfield methods. For methods available, see https://pyscf.org/user.html
Here is the list of methods and jobs currently supported in pyscf.
Energy: HF, MP2, DFT, CISD, FCI, CCSD/CCSD(T), TDDFT/TDHF
Gradients: HF, MP2, DFT, CISD, CCSD, RCCSD(T), TDDFT/TDHF
Hessian: HF, DFT
A typical input file of MLatom using PySCF looks like this:
method=b3lyp/631g* qmprog=pyscf xyzfile=sp.xyz yestfile=enest.dat
Orca
Orca is a generalpurpose computational program for quantum chemistry with specific emphasis on spectroscopic properties of openshell molecules. It supports various quantum chemistry methods with different level of theory. More information can be found on their website.
We also implement CCSD(T)*/CBS method in Orca interface which uses a composite scheme to extrapolate CCSD(T) to the complete basis set, which is faster than full CCSD(T)/CBS without sacrificing accuracy. Details of components in CCSD(T)*/CBS can be checked here
A typical input file of MLatom using Orca looks like this:
method=b3lyp/631g* qmprog=orca xyzfile=sp.xyz yestfile=enest.datFor CCSD(T)*/CBS method, users just need to directly specify it like this:
CCSD(T)*/CBS xyzfile=sp.xyz yestfile=enest.dat
xTB
The opensource semiempirical extended tight binding (xTB) program supports the calculation with popular semiempirical quantum mechanical methods GFNnxTB. Currently MLatom use GFN2xTB as default.
A typical input file of MLatom using GFN2xTB looks like this:
method=GFN2xTB xyzfile=sp.xyz yestfile=enest.dat QMprogramKeywords=xtb_kw # optionalxtb_kw file looks like: (details see xTB command line option https://xtbdocs.readthedocs.io/en/latest/commandline.html)
c 1 u 3Note
When using xTB, no need to specify
qmprog=xTB
If GFNxTB is to use, please specify
gfn 1
in keyword file
MNDO
MNDO is a semiempirical quantum chemistry program that supports semiempirical calculations using orthogonalization corrections. For methods available, please refer to https://mndo.kofo.mpg.de/input.php
A typical input file of MLatom using MNDO looks like this:
method=ODM2 qmprog=mndo xyzfile=sp.xyz yestfile=enest.dat QMprogramKeywords=mndokw # optional
Sparrow
SCINE Sparrow is an opensource command line tool for various semiempirical methods including MNDOtype models and DFTB models. For methods available, please refer to https://scine.ethz.ch/download/sparrow
A typical input file of MLatom using Sparrow looks like this:
method=ODM2* qmprog=sparrow xyzfile=sp.xyz yestfile=enest.dat
Simulations with usertrained models
MLatom can read a usertrained model from a file to make predictions (singlepoint calculations) with it for new data (given either as input vectors X or as XYZ coordinates) and ultimately to perform the following simulations (for data given in XYZ coordinates):
singlepoint calculations
geometry optimizations
frequencies and thermochemistry
molecular dynamics
The models can be either native MLatom or from thirdparty interfaces to popular ML model types:
kernel ridge regression (KRR) models
KREG (native). See tutorial. Can only be used for singlemolecule PES.
KRRCM (KRR with Coulomb matrix, native).
DeepPotSE and DPMD (through DeePMDkit)
sGDML (through sGDML). Can only be used for singlemolecule PES
Arguments
Calculations with native implementations do not require additional arguments, while the use of thirdparty models require the specification of a model type and/or the name of a thirdparty program, i.e., the user should provide either MLmodelType
and/or MLprog
argument (see also installation instructions). They can be used for such typical simulations as (see the corresponding sections for more details).
MLmodelType=[supported ML model type]
+++  MLmodelType  default MLprog  +++  KREG  MLatomF  +++  sGDML  sGDML  ++ +  GAPSOAP  GAP  +++  PhysNet  PhysNet  +++  DeepPotSE  DeePMDkit  +++  ANI  TorchANI  +++
MLprog=[supported ML program]
Supported interfaces with default and tested ML model types:
+++  MLprog  MLmodelType  +++  MLatomF  KREG [default]    see    MLatom.py KRR help  +++  sGDML  sGDML [default]    GDML  +++  GAP  GAPSOAP  +++  PhysNet  PhysNet  +++  DeePMDkit  DeepPotSE [default]    DPMD  +++  TorchANI  ANI [default]  +++
Note
Calculations with thirdparty programs may also generate additional output files.
Example
Below is an input file example of how to use KREG model to optimize geometry (see tutorial):
geomopt # Request geometry optimization
useMLmodel # using existing ML model
MLmodelIn=energies.unf # in energies.unf file
MLmodelType=KREG # of the KREG type
xyzfile=eq.xyz # The file with initial guess
Quantum dynamics with machine learning
MLatom can perform quantum dissipative dynamics with a range of machinelearning methods via an interface to the MLQD program. Supported methods from the program’s website:
Kernel Ridge Regression (KRR)based recursive (iterative) Quantum Dissipative Dynamics method: Here is the corresponding article → Speeding up quantum dissipative dynamics of open systems with kernel methods. Recently, we have performed a comparative study where KKR method outperforms NN models, here is the article → A comparative study of different machine learning methods for dissipative quantum dynamics
AIQD nonrecursive (noniterative) approach: Here is the corresponding article → Predicting the future of excitation energy transfer in lightharvesting complex with artificial intelligencebased quantum dynamics
The blazingly fast OSTL nonrecursive (noniterative) approach: Here is the corresponding article → OneShot Trajectory Learning of Open Quantum Systems Dynamics
Input and output arguments
QDmodel=[createQDmodel or useQDmodel] (not optional)
default option is useQDmodel
requests MLQD to create or use QD model
QDmodelIn=[userprovided model file]
Not optional if QDmodel=useQDmodel. Passing the name of file with the trained model
QDmodelOut=[userdefined name of created model](optional)
You can pass it if QDmodel=createQDmodel and MLQD will save the trained model with this name. However, its optional, if you don’t pass it, MLQD will choose a random name.
QDmodelType=[KRR or AIQD or OSTL]
default option is OSTL
It tells MLQD what type of QD model to use
systemType=[SB or FMO](not optional)
no default option
It tells MLQD the type of the system
QDtrajOut=file name for the output trajectory
You can pass it if QDmodel=useQDmodel and MLQD will save the predicted dynamics with this name. However, its optional, if you don’t pass it, MLQD will choose a random name.
prepInput=[True or False]
default is False. Case sensitive
Prepare input files X and Y from the data
hyperParam=[True or False]
default is False. Case sensitive
Optimize the hyper parameters of the model
patience=[integer nonnegative number]
Default value is 10
Patience for early stopping in CNN training
epochs=[integer nonnegative number]
Default value is 100
Number of epochs for training and optimization of CNN model [OSTL and AIQD methods]
max_evals=[integer nonnegative number]
Default value is 100
Number of maximum evaluations in hyperopt optimization of CNN model [OSTL and AIQD methods]
XfileIn=[name of X file]
Default is x_data if QDmodel=createQDmodel and prepInput=True
In the case of QDmodel=createQDmodel, its optional. It passes the name for X file. It saves the Xfile with this name if prepInput=True , and it passes the Xfile if prepInput=False . However if QDmodel=useQDmodel and QDmodelType=KRR , then it is not optional. You need to pass the input shottime trajectory.
YfileIn=[name of Y file]
Default is y_data if QDmodel = createQDmodel and prepInput=True
In the case of QDmodel = createQDmodel, it is optional. It passes the name for Y file. It saves the Yfile with this name if prepInput=True , and it passes the Yfile if prepInput=False.
dataPath=[absolute or relative path with data]
In the case of QDmodel=createQDmodel, and prepInput=True, need to pass path to the data, so MLQD can prepare the X and Y files. It should be noted that, data should be in the same format as our in our data set QDDSET1 (to be published) especially when QDmodelType=OSTL or AIQD
n_states=[number of states or sites, integer]
Default is 2 for SB and 7 for FMO
Number of states (SB) or sites (FMO)
initState=[number of initial site]
Default value is 1 (Initial exictation is on site1)
It represents initial site in FMO complex. Only required when we propagate dynamics with OSTL or AIQD method
time=[propagation time]
Default is 20 for SB and 50 for FMO
Propagation time in picoseconds (ps) for FMO complex and in atomic units (a.u.) for spinboson model
time_step=[time step of propagation]
Default is 0.05 for SB and 0.005 for FMO
time step of propagation
energyDiff=[energy difference]
Default value is 1.0
Energy difference between the states in the case of SB, needed only when QDmodelType=OSTL or AIQD
Delta=[tunneling matrix element]
Default value is 1.0
The tunneling matrix element in the case of SB, needed only when QDmodelType = OSTL or AIQD
gamma=[characteristic frequency]
Default value is 10 in the case of SB and 500 in the case of FMO
Characteristic frequency. In cm^1 for FMO and in (a.u.) for SB, and needed only when QDmodelType=OSTL or AIQD
lamb=[systembath coupling strength]
Default value is 1.0 in the case of SB and 520 in the case of FMO
Systembath coupling strength. In cm^1 for FMO and in (a.u.) for SB, and needed only when QDmodelType=OSTL or AIQD
temp=[temperature]
Default value is 1.0 in the case of SB and 510 in the case of FMO
Temperature (K) in the case FMO complex and inverse temperature in the case of SB, and needed only when QDmodelType=OSTL or AIQD
energyNorm=[normalizer]
Default value is 1.0
Normalizer for the energy difference between the states in the case of SB
energyNorm=[normalizer]
Default value is 1.0
Normalizer for the tunneling matrix element in the case of SB
gammaNorm=[normalizer]
Default value is 10 in the case of SB and 500 in the case of FMO
Normalizer for characteristic frequency
lambNorm=[normalizer]
Default value is 1.0 in the case of SB and 520 in the case of FMO
Normalizer for systembath coupling strength
tempNorm=[normalizer]
Default value is 1.0 in the case of SB and 510 in the case of FMO
Normalizer for temperature in the case of FMO and for inverse temperature in the case of SB
numLogf=[number of logistic functions]
Default value is 1
Number of logistic functions normalizing the dimension of time
LogCa=[coefficient]
Default value is 1.0
Coefficient “a” in the logistic function
LogCb=[coefficient]
Default value is 15.0
Coefficient “b” in the logistic function
LogCc=[coefficient]
Default value is 1.0
Coefficient “c” in the logistic function
LogCd=[coefficient]
Default value is 1.0
Coefficient “d” in the logistic function
dataCol=[column number]
Default value is 1
When QDmodelType=KRR , it only works for single output values. If ther are multiple columns in you data files, you need mention which column to grab
dtype=[real or imag]
Default is real
When you pass the column with dataCol and your data is complex, then need to mention which part of the complex data the MLQD to grab, real or imaginary
xlength=[number of time steps in the short seed trajectory]
Default value is 81
Length of the input short trajectory. It is the number of time steps in the data you passed with dataCol
refTraj
MLQD has the option to plot the predicted dynamics against the reference trajectory. It is optional, if reference trajectory is provided, MLQD will go for plotting otherwise not
xlim=[xaxis limit]
Default option is equal to the propagation time
The user can define xaxis limit for plotting
pltNstates=[number of states to be plotted]
Default option is to plot all states
Users can define how many states should be plotted by MLQD
Examples
These are just very brief examples, please see our detailed tutorial.
Training a KRR model
In the case of spin boson model, we have provided 20 trajectories from our QD3SET1 database for demonstration. The MLQD will grab them automatically if you don’t pass data path.
MLQD
QDmodel=createQDmodel
QDmodelType=KRR
prepInput=True
dataCol=1
dtype=real
xlength=81
systemType=SB
QDmodelOut=KRR_SB_model
Propagation of dynamics with the trained KRR model
We are providing a short input trajectory saved as state_1_pop.txt
:
MLQD
time=20
time_step=0.05
QDmodel=useQDmodel
QDmodelType=KRR
XfileIn=state_1_pop.txt
systemType=SB
QDmodelIn=KRR_SB_model
QDtrajOut=KRR_trajectory
The reference trajectory for comparison: 2_epsilon0.0_Delta1.0_lambda0.1_gamma4.0_beta1.0.npy
Training an AIQD model
MLQD
n_states=2
time=20
time_step=0.05
QDmodel=createQDmodel
QDmodelType=AIQD
prepInput=True
numLogf=10
LogCa=1.0
LogCb=15.0
LogCc=1.0
LogCd=1.0
energyNorm=1.0
DeltaNorm=1.0
gammaNorm=10
lambNorm=1.0
tempNorm=1.0
systemType=SB
hyperParam=True
patience=10
epochs=10
max_evals=10
QDmodelOut=AIQD_SB_model
Propagation of dynamics with the trained AIQD model
We just pass the parameters and the trained AIQD model should be able to predict the corresponding dynamics
MLQD
n_states=2
time=20
time_step=0.05
energyDiff=1.0
Delta=1.0
gamma=4.0
lamb=0.1
temp=1.0
QDmodel=useQDmodel
QDmodelType=AIQD
energyNorm=1.0
DeltaNorm=1.0
gammaNorm=10
lambNorm=1.0
tempNorm=1.0
numLogf=10
systemType=SB
QDmodelIn=AIQD_SB_model.hdf5
QDtrajOut=Qd_trajectory
Training an OSTL model
MLQD
n_states=2
QDmodel=createQDmodel
QDmodelType=OSTL
prepInput=True
energyNorm=1.0
DeltaNorm=1.0
gammaNorm=10
lambNorm=1.0
tempNorm=1.0
systemType=SB
hyperParam=True
patience=10
epochs=10
max_evals=10
QDmodelOut=OSTL_SB_model
Propagation of dynamics with the trained OSTL model
We just pass the parameters and the trained OSTL model should be able to predict the corresponding dynamics in one shot
MLQD
n_states=2
time=20
time_step=0.05
energyDiff=1.0
Delta=1.0
gamma=4.0
lamb=0.1
temp=1.0
QDmodel=useQDmodel
QDmodelType=OSTL
energyNorm=1.0
DeltaNorm=1.0
gammaNorm=10
lambNorm=1.0
tempNorm=1.0
systemType=SB
QDmodelIn=OSTL_SB_model.hdf5
QDtrajOut=Qd_trajectory
UV/vis spectra
UV/vis spectra (crosssections) can be calculated with MLNuclear Ensemble Approach (MLNEA). Detailed tutorial 1 and tutorial 2 are available.
For full functionality, NewtonX (tested with version 2.2) and Gaussian should be installed (see installation instructions including settings appropriate environmental variables like $NX
and $GAUSS_EXEDIR
). Neither NewtonX nor Gaussian are available on MLatom@XACS cloud.
optional arguments:

number of excited states to calculate. 
default=3 

userdefined number of QM calculations for training ML. 
default=0, number of QM calculations will be determined iteratively 

requests plotting QCNEA cross section 


define the broadening parameter of QCNEA cross section 


plotting cross section obtained via single point convolution 
advanced arguments (not recommended to modify):

maximum number of QC calculations in the iterative procedure. 
default=10000 

number of ML calculations. 
default=50000 
required files:
 mandatory file
gaussian_optfreq.com
input file for Gaussian opt and freq calculations Alternatively, fileseq.xyz
(XYZ file with equilibrium, optimized, geometry) andnea_geoms.xyz
(file with all geometries in nuclear ensemble) can be provided.gaussian_ef.com
template file for calculating excitation energies and oscillator strengths with Gaussian.
 optional file
crosssection_ref.dat
reference cross section file calculated in format similar to that of NewtonX (1st column: DE/eV; 2nd column: lambda/nm; 3rd column: sigma/A^{2})eq.xyz
file with optimized geometry (has to be used together withnea_geoms.xyz
)nea_geoms.xyz
file with all geometries in nuclear ensemble (has to be used together witheq.xyz
)E1.dat E2.dat ...
andf1.dat f2.dat ...
files that stores the exciting energy and oscillator strength per line which correspond tonea_geoms.xyz
.
output files:
crosssection/crosssection_mlnea.dat:
crosssection spectra calculated with MLNEA methodcrosssection/crosssection_qcnea.dat:
crosssection spectra calculated with QCNEA methodcrosssection/crosssection_spc.dat:
crosssection spectra calculated with singlepointconvolutioncrosssection/plot.png:
the plotting that contains crosssection calculated with different kinds of method.
Twophoton absorption cross sections
This simulation type is performed as described in this publication. It is currently only available on the MLatom@XACS cloud and will be released soon. See the original source code on GitHub.
To run MLTPA calculations locally, the following packages have to be installed:
python >= 3.7
scikitlearn<1.0.0
xgboost>=1.5.0
rdkit>=2022.03.3
numpy>=1.21.1
pandas>=1.0.1
After proper python environment is built. Install packages from conda is recommened, i.e., you need to run:
pip install pandas
pip install numpy==1.22
pip install scikitlearn==0.24.2
pip install xgboost==1.5
pip install rdkit
Input and output arguments
MLTPA
required.
requests calculation of the twophoton absorption (TPA) cross section for a spectra or a given wavelength.
SMILLESfile=[file with SMILES]
this argument is required; no default file name.
file with SMILES of one or many molecules.
Output contains the commaseparated predicted MLTPA cross section values or spectra in units of GM for each wavelength. Output files tpa[molecular index as in SMILESfile].txt
are saved in a folder tpa[absolute time]
in current path.
Additional options for wavelength and solvent
auxfile=[file with the information of wavelength and Et30 in the format of 'wavelength_lowbound,wavelength_upbound,Et30'] (wavelength in nm.)
:
If the auxiliary file does not exist, then the default value of Et30 will be 33.9 (toluene) and the whole spectra between 6001100 nm will be provided. The entries (lines) should be provided in the same order as in SMILLESfile. See the list with the solvents and their Et30 values.
Example
Here we show how to calculate TPA cross section for RHODAMINE 6G and RHODAMINE 123 molecules with MLatom input file mltpa.inp
:
MLTPA
SMILESfile=Smiles.csv
auxfile=_aux.txt
This input requires Smiles.csv
file with SMILES of molecules:
CCNC1=CC2=C(C=C1C)C(=C3C=C(C(=[NH+]CC)C=C3O2)C)C4=CC=CC=C4C(=O)OCC.[Cl]
COC(=O)C1=CC=CC=C1C2=C3C=CC(=N)C=C3OC4=C2C=CC(=C4)N.Cl
and optional _aux.txt
:
600,850,55.4
600,600,33.9
After you prepared your input files mltpa.inp
, Smiles.csv
, and _aux.txt
, you can run MLatom as usual:
mlatom mltpa.inp > mltpa.out
After the calculations finish, the predicted TPA cross section values are saved in a Folder named tpa[absolute time]
. In the folder, there are two files for two molecules: tpa1.txt
and tpa2.txt
. For our examples, it looks like:
wavelength,predicted_sigma (GM)
600.0,285.19455
610.0,297.71707
620.0,284.11694
......
810.0,121.51988
820.0,116.537994
830.0,118.04909
840.0,103.65925
850.0,113.72374
wavelength,predicted_sigma (GM)
600.0,138.2346