<h3>Title</h3>
<p><span style="color: #0000ff;">Classical Trajectory Methods for Approximate Quantum Dynamics</span></p>
<p><strong><span style="text-decoration: underline;">Abstract</span> :</strong></p>
<p>Chemistry is the study of large systems of molecules.  Accurate simulations of quantum dynamics scale exponentially with the number of degrees of freedom, making them impractical.  Thus, approximations have to be made.  In the previous talk, I discussed my work involving the system-solvent decomposition using real-time path integral.  In this talk, I would like to focus on problems that are not amenable to such decompositions such as calculating the infrared and Raman spectra or bulk transport properties like diffusion coefficient.  Often dynamical quantum interference gets washed away in condensed phases under ambient conditions leading to an effective utilization of semiclassical<sup>1-3</sup> and other classical trajectory-based approximations<sup>4-6</sup>.  The simplest of these approximations incorporates quantum dispersion effects into a phase space distribution called the Wigner distribution<sup>3</sup>, defined as a multidimensional Fourier transform.  Computational simulation of this distribution is complicated by the Monte Carlo “sign problem.”  I have developed various methods to overcome these problems.  First, I will introduce a very simple adiabatic switching method<sup>7, 8</sup> that can approximate the Wigner distribution using classical trajectories.  Then I would describe an exact path integral method<sup>9</sup> that is systematically convergeable and mitigates the sign problem through a physically motivated series expansion.  Other approaches of extracting quantum information from classical trajectories would also be presented<sup>10</sup>.  I will show my current work in comparing various augmented classical trajectory methods in simulating spectra and density of states of bulk water using <em>ab initio</em> deep neural network force fields<sup>11-13</sup>.</p>
<p>Finally, I will discuss my future research plans utilizing many of the frameworks that I have developed to address open research questions in Chemistry, and also talk about avenues of further improving these methods.</p>
<h1>References</h1>
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<li>Bose, A.; Makri, N., Coherent State-Based Path Integral Methodology for Computing the Wigner Phase Space Distribution. <em>J. Phys. Chem. A </em><strong>2019,</strong> <em>123</em> (19), 4284-4294.</li>
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<li>Zhang, L.; Chen, M.;  Wu, X.;  Wang, H.;  E, W.; Car, R., Deep neural network for the dielectric response of insulators. <em>Phys. Rev. B </em><strong>2020,</strong> <em>102</em> (4), 041121.</li>
</ol>