dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, because of the high electron kinetic energy, the continuum, expanded in partial waves centered on the core site, is approximated by the corresponding atomic one. Given the complexity and high number of final ionic states reached, OCA turns out to be adequate for an overall description of the spectral intensities in current spectra of complex molecules. 387, 390, 391 he computational steps to obtain ssDCH spectral intensities and AES/RAES decay rates in OpenMolcas are summarized in Figure  20. In both cases, the two-particle Dyson matrices corresponding to either the ssDCH intensities or to the Auger amplitudes are a key ingredient. Their computation has been implemented within the RASSI module. 386, 387 dditionally, an interface to the SCAMPI code  298  offers an approximation level to the molecular continuum which lies in between that of OCA and Tiresia.
3. 8. Ultrafast Electron Dynamics. Ultrafast electron dynamics can be studied within the density-matrix-based timedependent restricted active space configuration interaction framework (ρ-TD-RASCI),  392, 393  thanks to the newly developed program module RhoDyn. Via the density-matrix formalism, RhoDyn can describe dynamics in both coherent and incoherent limits and cases in between them. Effectively, it utilizes the time-independent quantities computed in RASSCF, CASPT2, and RASSI modules to construct the Hamiltonian and recast the problem into the time domain by solving the Liouville-von Neumann equation, see Figure  21a. The user can construct the nonequilibrium initial state by specifying an initial density matrix, e. g. , by populating a configuration that is not an eigenstate of the electronic Hamiltonian, or by introducing an external light field in the form of consecutive ultrashort pulses.
When the PESs on which the nuclear wave packet evolves are represented in the approximation of the multidimensional uncoupled displaced harmonic oscillator (DHO), employing a unique set of normal modes and frequencies for all the electronic states (normally computed in the electronic ground state), analytical equations can be derived by means of second-order cumulant expansion of Gaussian fluctuations (CGF), formally exact in the case of adiabatic dynamics. 415 These equations can be parametrized with quantum mechanical (QM) data from a single geometry: transition energies and dipole moments, normal modes and frequencies, and energy gradients. The program iSpectron  446  was designed to parse the QM data from OpenMolcas (among other QM software) and interface it to Spectron, 415 a platform for simulation of coherent nonlinear optical spectroscopy of single molecules and their aggregates in the DHO/CGF framework.
The Open-Molcas-Spectron interface allows to compute the QM quantities with multiconfigurational wave function theory methods such as RASSCF/RASPT2. Employing such methods allows to compute many excited states (those in which the relevant nonadiabatic events occur, as well as the spectroscopically relevant higher-lying ones) and to evaluate the dipole coupling between them. Both homogeneous and inhomogeneous broadening can be taken into account. iSpectron possesses an ample body of tools for displaying and analyzing the spectra. For a detailed overview of the capabilities of iSpectron interested readers are referred to ref 446; the iSpectron code is available free of charge on GitHub. 447 espite its simplicity, the DHO/CGF approach can produce rather accurate spectra with a manageable computational effort.
Analytic Linear Vibronic Couplings for Molecular Magnets. The accurate and efficient modeling of vibronic coupling in metal complexes, i. e. , the mixing of electronic states beyond the Born-Oppenheimer approximation induced by nuclear motion, is crucial for the theoretical investigation of a wide range of physical processes in spectroscopy, quantum information, and magnetic memory applications. The analytic linear vibronic coupling (LVC) method implemented in the SHARC program  172, 270  is already available for nonadiabatic dynamics through its OpenMolcas interface (see section 6. 3 below). 9] [450] [451]  As simulations become more accurate, accounting for many thousands of degrees of freedom in the condensed phase, the numerical approach has practical and theoretical limitations. First, the number of single-point calculations grows linearly with the number of nuclear degrees of freedom and quickly becomes infeasible, both in terms of computational cost and file storage.
The analytic LVC-based differentiation algorithm is implem e n t e d a s p a r t o f t h e P y t h o n p a c k a g e s spin-phonon_suite and angmom_suite, freely available from the PyPI repository, and it is interfaced with the OpenMolcas output. The general usefulness of this implementation extends beyond its application to magnetic relaxation in lanthanide complexes. While the crystal field Hamiltonian is appropriate to describe the electronic states of the ground multiplet of lanthanide complexes, it is not the best choice in other cases. However, this method is equally applicable to other spin Hamiltonian parametrizations or to circumvent model Hamiltonians entirely and compute the bare matrix elements of the vibronic coupling derivatives. Furthermore, with the advent of CAS/RASPT2 gradients and NACs in Open-Molcas (see section 4.
Subsequent evaluations of the molecular gradients for the excited states and NACs use the precomputed values. This technical trick allows for a significant speedup of subsequent MCLR steps and also helps to reduce, in part, the large input/output (I/O) operations in this process. The speedup increases with the computed molecular size and the basis set used. Table  3  shows some relative timings for the comparison of the two compounds. A collection of scripts setting the environment and automatization of OpenMolcas calculations for various tasks, like CASSCF/RASSI/SINGLE_ANISO, CASSCF/ CASPT2/RASSI/SINGLE_ANISO as well as the evaluation of the analytic or numerical vibronic couplings for any molecule is done in a parallel fashion efficiently, even on a single multicore node, with limited scratch space and memory.
Among the most important features is the automated setup of all required inputs, parallel execution of calculations of different spin states, parallel per-root execution of the CASPT2, and parallel evaluation of molecular gradients and NACs. All these tasks are reusing the common ERIs or RICD integrals and other files as much as possible, without duplication or making redundant copies. The scripts are available free of charge on GitLab. 456. 5. Vibrational-Rotational Levels for Diatomics. The VibRot module, included in MOLCAS since its first versions, has allowed the calculation of roto-vibrational levels supported by a potential between a pair of atoms, along with various related properties. VibRot only allows the user to input a point-wise potential. The point-wise potential is then subjected to spline interpolation.
AB INITIO MOLECULAR DYNAMICS The OpenMolcas environment has internal modules for classical and nonadiabatic dynamics, namely, DYNAMIX and SURFACEHOP; it is also interfaced to several external programs, for example to Tinker, for options including quantum mechanics/molecular mechanics (QM/MM) simulations, and to SHARC and Quantics for nonadiabatic dynamics. In this section six new features are described, which include new tools, improvements of existing modules and interfaces, and some details in association with nonadiabatic dynamics using analytical MC-PDFT gradients. First, several methods for generation of initial conditions have been implemented. These range in complexity from random velocities based on a Boltzmann distribution to more complex methods based on a Wigner distribution. These options have been incorporated through an easy-to-use Python script.
Each of these methods require knowledge of the normal mode motion of the system of interest. Therefore, the first step is to perform a Hessian calculation using OpenMolcas. The dynamixtools. py script found within the Tools/ dynamixtools subdirectory can then be used with the vibrational information to generate initial conditions for running molecular dynamics. Below the two different distributions are discussed, and then some benchmark results are presented. 4] [475]  Here the phase space of each normal mode is assumed to be classical in nature, and the positions (Q i ) and momenta (P i ) are sampled through a random phase of the normal mode vibration at t = 0 shown in eqs 21 and 22, respectively: = Q A R cos(2 ) i i i (21) = P A R sin(2 ) i i i i ( 22 ) The amplitude (A i ) of the motion is determined from the vibrational energy available to the normal mode (E i ) and the vibrational frequency of the mode (ω i ), i.
e. , = A E 2 / i i i. In the present implementation, the energy available to each vibrational mode is independently sampled from a classical Boltzmann distribution. Once the normal mode positions and momenta are obtained, they can be transformed into Cartesian coordinates (x i , p i ) using the eigenvectors of the normal mode (L i ) and the mass matrix (M) as in eqs 23 and 24. = + x L Q x i i i 0 (23) = p ML P i i i ( 24 ) Spurious center of mass translation and rotations often arise during the coordinate transformation step. After sampling all vibrational modes, the center of mass motion is removed, and the remaining atomic momenta and displacements are rescaled to match the total vibrational energy of the system (∑E i ). The final velocities and positions obtained from the dynamixtools. py script can then be used directly with the OpenMolcas DYNAMIX module for molecular dynamics simulations.
6. 2. Improved Nonadiabatic Coupling for Trajectory Hopping. 0] [481] [482]  The SURFACEHOP module within OpenMolcas can also be used to perform semiclassical nonadiabatic dynamics, specifically TSH following the "Tully fewest switches approach". 483, 484 Running TSH dynamics directly within OpenMolcas has several benefits: increased speed thanks to reduced read/write, avoided risk of precision loss from reading printed quantities, and better future proofing with fewer potential points of failure. While SURFACEHOP is not new to OpenMolcas, 9 recent work has improved the accuracy and reliability of TSH simulations carried out using this module. The SURFACEHOP implementation of TSH employs the Hammes-Schiffer-Tully (HST) approximation. 485 It relies on time-derivative couplings (TDC) and avoids the bottleneck of calculating NACs using ⟨ϕ i |∂ϕ k /∂t⟩ = ∂R/∂t •⟨ϕ i |∂ϕ k /∂R⟩ with ϕ i and ϕ k electronic adiabatic states, R the nuclear coordinates, and t the time variable.
270, 480 his interface�a Python script that is part of the SHARC package�automatically generates input files for Open-Molcas and parses the output to retrieve data like energies, gradients, or couplings, as indicated in Figure  28a, c. The interface allows SHARC to perform ab initio on-the-fly dynamics simulations at a CASSCF and/or CASPT2 level of theory. Herein two new options now available in the SHARC-OpenMolcas interface and extended functionalities available in SHARC version 3. 0 are reported. To alleviate the cost of expensive on-the-fly calculations, SHARC can now also work with preparameterized model potentials. First, one possibility is to use LVC models,  172, 496  as known from wavepacket dynamics simulations. 185 The second possibility is to employ excited-state neural network potential energy surfaces and other machine learning properties via the SchNarc 497 method�a generalization of the SchNet 498 architecture.
500 (c) Data flow for using SchNarc ML models with SHARC and OpenMolcas: Desired properties (E, Grad, NAC, SOC, DM) are calculated with OpenMolcas and stored via the SHARC-OpenMolcas interface and the SchNarc package in a database. SchNarc uses this database to train machine learning (ML) models that predicts all required properties needed by pysharc. Together with suitable initial conditions, nonadiabatic dynamics trajectories are generated. 497 (d) A trajectory of a roaming hydrogen atom in excited tyrosine, based on simulations on a picosecond time-scale. 501.
AG to calculate energies, gradients, nonadiabatic coupling vectors and possibly (transition) dipole moments via the SHARC-OpenMolcas interface to build up a database, see Figure  28c. ML models of these properties are trained and subsequently employed in nonadiabatic dynamics simulations using the pysharc driver. 172, 499 The latter avoids slow file I/O and, in combination with the fast ML predictions, is able to push dynamics simulations up to the nanosecond time scale. 499 ne example of nonadiabatic dynamics using OpenMolcas and SchNarc is the study of excited tyrosine. 501 Again here, multiconfigurational methods were mandatory to describe anticipated dissociative reactions. Surprisingly, by leveraging neural network models trained on CASPT2 calculations (among others), it was possible to discover unconventional roaming dynamics (Figure  28d ) in excited tyrosine.
A succe sful method to simulate wave packet dynamics is TSH,  484  which represents the wave packet by a swarm of independent trajectories, obtained through sampling the phase space around the ground state (GS) equilibrium, subject to the Newtonian laws of motion along the PES calculated on the fly. The COBRAMM package is an interface to electronic structure codes to facilitate such simulation up to the ps time scale. 472, 518, 519   In the context of a very recent global restructuring of COBRAMM, an automated workflow was implemented to perform transient electronic spectroscopy simulations within the TSH framework exploiting OpenMolcas as the QM software. The OpenMolcas/COBRAMM interface facilitates nonadiabatic dynamics simulations, in gas-phase and in an explicit environment through a QM/MM hybrid scheme, with various RASSCF/RASPT2 flavors and makes use of the recently implemented analytical gradients.
The spectroscopy implementation consists of the extraction of geometries along the dynamics in user-defined intervals, the calculation of the manifold of spectroscopically relevant higher-lying states for every geometry, the generation of the individual time-slices at a given time step and, eventually, their convolution in the final spectrum. The COBRAMM package is available free of charge on GitLab. 520  Routines for trajectory-based transient spectroscopy simulations in other spectral windows, e. g. , photoelectron spectroscopy or X-ray absorption are under current development. 6. 5. Comparison of Two Techniques for Nonadiabatic Dynamics Simulations. The ultrafast photoinduced dynamics of uridine (Urd) is used here to demonstrate the capabilities of the iSPECTRON (see section 5. 2) and COBRAMM platforms, i.
SUMMARY The developments of the OpenMolcas program suite over the last three years have been reported throughout six thematic sections. A compact summary of each topic follows. In the "Electronic Structure Theory" section, a number of new methods and interfaces are described, which enable novel features based on the framework of multiconfigurational SCF. In particular, improvements to the Stocastic-CI and the DMRG approaches are reported. The interfaces to Dice, RelaxSE, COLUMBUS, and GronOR are described. Moreover, three new options to the CASPT2 functionality are presented-the RMS and XDW versions of multistate CASPT2, a regularization as an alternative to level-shifting, and the frozen natural orbital option as implemented for the RASPT2 model. Finally, extensions of the MC-PDFT model are put forward.
The section is completed with a report of a new module -LEVELfor enhanced and improved simulations of ro-vibrational spectroscopy for dinuclear systems. The "Ab Initio Molecular Dynamics" section includes reports on tools to generate initial conditions for semiclassical molecular dynamics simulations. Additionally, improvements of nonadiabatic dynamics using the SURFACEHOP module and of the SHARC package versions 2. 0 and 3. 0, as interfaced to OpenMolcas, are reported. This section is concluded with the presentation of the COBRAMM interface for simulations of transient UV/vis spectroscopy, a comparison of different simulations techniques for nonadiabatic dynamics�trajectory surface hopping vs the displaced harmonic oscillator model, the newly developed OpenMolcas-GAMESS-US/Tinker interface, with an example of a nonadiabatic molecular dynamics simulation of a molecular motor, and at last some notes on the use of the MC-PDFT model in connection with simulations of nonadiabatic processes.
Input and output example for a LEVEL calculation. Complete computational details (including optimized geometries, extra basis functions, active space orbitals, computed energetics, and example input files), data acquisition, and its discussion for the evaluation of Frenkel's excitonic coupling terms and the corresponding absorption spectra. Input examples and discussion for the calculation of ssDCH and Auger spectra. Input example for the RhoDyn module. Input examples for the original and two-step procedures for the analytic evaluation of vibronic coupling. Example input and output files for the SHARC-OpenMolcas interface. Descriptions of the capabilities of the Open-Molcas-GAMESS-US/Tinker interface and information about the software supporting the interface followed with a number of input examples, details of MTDP simulations in solvent.
C. d. G. , A. S. M. , R. B. and T. P. S. acknowledge  PRACE  for awarding access to JUWELS at  GCS@FZJ, Germany , and financial support provided by the  Spanish Ministerio de Ciencia, Innovación y Universidades  (Projects  RTI2018-095460-BI00 ,  PID2020-113187GB-I00  and the  Excellence María de Maeztu  grant  MDM-2017-0767 ) and by the  Generalitat de Catalunya  (Projects  2017SGR13  and  2017-AQ SGR629 ). M. G. D. and M. L. acknowledge support from the  Foundation Olle Engkvist Byggmästare  (Grant No. 183-0403 ). W. D. acknowledges funding from the  European Union 's  Horizon Europe research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 101062864. A. K. acknowledges support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -SFB 1477 " Light-Matter Interactions at Interfaces ", project number  441234705.
M. Kr. acknowledges support from  NCBIR  Grant No. PL-TW/VIII/1/2021. E. D. L. acknowledges kind support from  Luis Seijo. S. L. thanks the  National Science Foundation  for financial support under grant no. CHE-2136142 , as well as the  Academy of Finland  for financial support under project numbers 350282 and 353749. R. L. acknowledges financial support from the  Knut and Alice Wallenberg Foundation  (Grant No. KAW-2013. 0020 )and the  Swedish Research Council  (VR, Grant No. 2020-03182 ). H. L. acknowledges the support by the  National Science Foundation  [grant number  2107923 ],  Division of Chemistry. P. L. R. and A. A. acknowledge support from the  European Centre of Excellence in Exascale Computing TREX , funded by the  Horizon 2020 program  of the  European Union  under grant no. 952165.
2016-03398 ) and the  Olle Engkvist foundation  (grant  18-2006 ). M. R. and A. B. acknowledge the fundings through the  "Quantum for Life Center " funded by the  Novo Nordisk Foundation  (grant  NNF20OC0059939 ). M. R. and M. M. acknowledge support through  ETH Research Grant   ETH-43 20-2. D. C. S. acknowledges infrastructure support from the  Operational Program Competitiveness   2014-2020 , Axis 1, under POC/448/1/1 Research infrastructure projects for public R&D institutions/ Sections F 2018, through the  Research Center with Integrated Techniques for Atmospheric Aerosol Investigation in Romania (RECENT AIR ) project, under grant agreement MySMIS no. 127324. R. S. acknowledges the support by the  US Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division ,  Gas Phase Chemical Physics program  through  Argonne National Laboratory  under Contract  DE-AC02-06CH11357.
L. U. acknowledges the financial support of the research projects  R-143-000-A65-133 ,  A-8000709-00-00 , and  A-8000017-00-00  of the  National University of Singapore. V. V. thanks  eSSENCE@LU  grant 7:3. D. W. acknowledges the  Air Force Office of Scientific Research  by grant no. FA9550-11-0078. D. Y. acknowledges the support by  NSF  grant  CHE-1954723. J. P. Z. and  L. Go. acknowledge the support from the  Deutsche Forschungsgemeinschaft  ( SPP 2102 , project  GO 1059/8-2 ). The authors at the  University of Vienna thank the Vienna Scientific Cluster  for computer resources. The work carried out at the  University of Minnesota  and the  University of Chicago  was supported in part by the  Air Force Office of Scientific Research  by grant  FA9550-20-1-0360 , the  National Science Foundation  under grant  CHE-2054723 , the  U.
S. Department of Energy, Office of Science, Office of Basic Energy Sciences  under award  DE-SC0015997 , and the  Catalyst Design for Decarbonization Center , an  Energy Frontier Research Center , which is funded by the  US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES)  under award  DE-SC0023383. The authors thank the  Research Computing Center (RCC) at the University of Chicago  and the  Minnesota Supercomputing Institute (MSI)  at the  University of Minnesota  for access to computational resources. The NOCI work used resources through the  INCITE program  of the  Oak Ridge Leadership Computing Facility (OLCF)  at the  Oak Ridge National Laboratory , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC05-00OR22725.
The origin and dispersal of the Austronesian language family, one of the largest and most widespread in the world, have long attracted the attention of linguists, archaeologists, and geneticists. Even though there is a growing consensus that Taiwan is the source of the spread of Austronesian languages, little is known about the migration patterns of the early Austronesians who settled in and left Taiwan, i. e. , the "Into-Taiwan" and "Out-of-Taiwan" events. In particular, the genetic diversity/structure within Taiwan and how this relates to the Into/Out-of-Taiwan events is largely unexplored, because most genomic studies have largely utilized data from just two of the 16 recognized highland Austronesian groups in Taiwan. In this study, we generated the largest genome-wide dataset for Taiwanese Austronesians to date, including six highland groups and one lowland group from across the island, and two Taiwanese Han groups.
Taiwanese groups in the light of genetic variation in South Asia, East Asia and Oceania We generated genome-wide single nucleotide polymorphism (SNP) array data on the Affymetrix Human Origins array for 55 individuals from 7 Austronesian (5 Highland, 1 Lowland, and 1 Orchid Island) and 2 Han groups from Taiwan (Fig. 1A ). We merged the new data with comparative modern and ancient genomes from Asia and Oceania (Fig. S1 ). We first investigated overall patterns in the dataset using Principal Component Analysis (PCA) and ADMIXTURE  (Alexander, et al. 2009 ) /DyStruct  (Joseph and Pe'er 2019)  analyses. In the PCA, East Asians (southern East Asians, sEA; northern East Asians, nEA; Mainland Southeast Asians, MSEA; and Island Southeast Asians, ISEA) are separated from South Asians and Oceanians.