Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session B26: Predicting Rare Event Kinetics in Complex Systems with Theory, Simulations and Machine Learning IFocus Live

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Sponsoring Units: DCP Chair: Omar Valsson, Max Planck Inst. for Poly Research; Pratyush Tiwary, University of Maryland, College Park 
Monday, March 15, 2021 11:30AM  12:06PM Live 
B26.00001: News Approaches for Boltzmann Generators: Sampling Equilibrium States of ManyBody Systems Invited Speaker: Frank Noe Boltzmann Generators are a novel rareevent sampling framework that combines invertible neural networks (normalizing flows) with statisticalmechanics based reweighting methods [1]. The basic idea is to train the invertible neural network to generate samples from a probability distribution similar to the target equilibrium distribution (e.g. canonical), and then reweight to the exact target density so as to generate asymptotically unbiased samples. Ways to incorporate symmetries of the energy function [2] and stochastic sampling steps in the neural network [3] have also been proposed. In this talk I will describe recent methodological developments and applications to the sampling of classical manybody systems. 
Monday, March 15, 2021 12:06PM  12:42PM Live 
B26.00002: Molecular Latent Space Simulators Invited Speaker: Andrew Ferguson We have developed molecular latent space simulators (LSS) to learn highly efficient and accurate surrogate models of molecular dynamics (MD) by stacking three specialized deep learning networks to (i) encode a molecular system into a slow latent space, (ii) propagate dynamics in this latent space, and (iii) generatively decode a synthetic molecular trajectory. The trained LSS generates novel ultralong molecular trajectories at six orders of magnitude lower cost than MD enabling resolution of rare thermodynamic states and kinetic transitions with arbitrarily low statistical uncertainties. In an application to Trpcage, we generate millisecond trajectories in just minutes of wall clock time and demonstrate excellent agreement with the MD structure, thermodynamics, and kinetics. 
Monday, March 15, 2021 12:42PM  12:54PM Live 
B26.00003: Learning with rare data: Using active importance sampling to optimize objectives dominated by rare events Grant Rotskoff, Eric VandenEijnden Deep neural networks, when optimized with sufficient data, provide accurate representations of highdimensional functions; in contrast, function approximation techniques that have predominated in scientific computing do not scale well with dimensionality. While the promise of unparalleled accuracy may suggest a renaissance for applications that require parameterizing representations of complex systems, in many applications gathering sufficient data to develop such a representation remains a significant challenge. Here we introduce an approach that combines rare events sampling techniques with neural network optimization to optimize objective functions that are dominated by rare events. We show that importance sampling reduces the asymptotic variance of the solution to a learning problem, suggesting benefits for generalization. We study our algorithm for identifying dynamical transition pathways between two states of a system, a problem with applications in statistical and chemical physics. Our numerical experiments demonstrate convergence even with the compounding difficulties of highdimension and rare data. 
Monday, March 15, 2021 12:54PM  1:06PM Live 
B26.00004: State Predictive Information Bottleneck Dedi Wang, Pratyush Tiwary The rapid advances in computational power have made molecular dynamics (MD) a powerful tool for studying biophysical systems. However, there are still at least two open questions in this area: first, how to make use of the deluge of data generated from MD simulation human understandable; second, how to further push the limit of the timescale that can be reached by MD. One key to both difficulties is to uncover a low dimensional manifold (known as reaction coordinate or RC) on which the dynamics of the system can be projected. Here we developed State Predictive Information Bottleneck (SPIB) to learn RC from trajectories. In SPIB, the time delay is interpreted as the minimal time resolution that we care for a dynamic system, and can be used to automatically discretize the highdimensional state space into a few metastable states. Such a discretestate representation then can be employed to guide our RC to focus only on the motion related to the state transitions. In this way, the RC learned by SPIB is strongly related to the committor, and is able to identify the correct transition states. 
Monday, March 15, 2021 1:06PM  1:18PM Live 
B26.00005: Shaping rare events away from equilibrium: bounds on transition rate enhancement and a new take on optimal control of reaction rates. Benjamin KuznetsSpeck, David Limmer Nonequilibrium forces often regulate how quickly a complex random system transitions between longlived states, but there are few theories or guiding principles for how transition rates are enhanced by timedependent external influence. Using tools from stochastic thermodynamics, we develop a general theory of rate enhancement in the transition path ensemble, leading to fundamental bounds on the ratio of transition rates. Under generic physical conditions, we show that the heat dissipated over the course of the transition sets an upper limit on achievable rate enhancement. This basic tradeoff between speed and energy consumption is illustrated in canonical examples of barrier crossing under autonomous, active, and periodically modulated driving protocols. We discuss how our bounds can be employed as a variational principle to design optimal protocols for tuning a system to achieve a target rate, and inferring rare reaction rates in (non)equilibrium settings. 
Monday, March 15, 2021 1:18PM  1:30PM Live 
B26.00006: Rare Event Rates From Probability Distributions Jayashrita Debnath, Michele Parrinello In a rare event scenario, a system undergoes transitions between metastable states separated by large barriers. Calculating transition rate under this circumstances is difficult. However, it has been shown that if a bias can be engineered such that the transition state region is left untouched, transition rates can be nonetheless computed[13]. Here, we present a new method to build a bias that achieves this property in a relatively simple and transparent way. In this method, called Gaussian Mixture Based Enhanced Sampling, the metastable states are discriminated by a set of descriptors[4]. The fluctuations of these descriptors in the different metastable states are fitted to Gaussian Mixtures. From the fit, a bias can be built such that it allows the system to migrate from one metastable state to another. Yet, by construction, the bias is null in the transition state region. We exemplify the effectiveness of the method by calculating rates for chemical reaction, ligand unbinding and protein unfolding. 
Monday, March 15, 2021 1:30PM  2:06PM Live 
B26.00007: Phase Transitions, Molecular Simulations and Machine Learning Invited Speaker: Sapna Sarupria Phase transitions are ubiquitous in nature and play a critical role in a broad range of phenomena from ice 
Monday, March 15, 2021 2:06PM  2:18PM Live 
B26.00008: Learning molecular dynamics with simple language model built upon long shortterm memory neural network SUNTING TSAI, Pratyush Tiwary Recurrent neural networks (RNNs) have led to breakthroughs in natural language processing and speech recognition. Here we show that RNNs, specifically long shortterm memory (LSTM) neural networks can also capture the temporal evolution of chemical/biophysical trajectories. Our language model learns a probabilistic model of 1dimensional stochastic trajectories generated from higherdimensional molecular dynamics. The model captures the Boltzmann statistics of the system and also reproduces kinetics across a large spectrum of timescales. We demonstrate how training the LSTM is equivalent to learning a path entropy, and that the LSTM embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We also demonstrate our model's reliability through different benchmark systems and a single molecule force spectroscopy trajectory for multistate riboswitch. 
Monday, March 15, 2021 2:18PM  2:30PM Live 
B26.00009: Data Driven characterisation of the Free Energy Landscape of Syndiotatic Polysterene Atreyee Banerjee, Yasemin Bozkurt Varolgünes, Joseph F Rudzinski, Tristan Bereau Syndiotatic polysterene (sPS) exhibits complex polymorphic behaviour, resulting in rugged free energy landscapes (FELs) with high energy barriers. Enhanced sampling methods have the potential remedy to overcome the barriers with prior knowledge of collective variables (CVs), typically identified through physical or chemical expertise. Autoencoders are powerful tools for providing a lowdimensional embedding of the essential features, since the technique forces an information compression in the bottleneck region. A specialised autoencoder architecture, the Gaussian mixture variational autoencoder (GMVAE), performs dimensionality reduction and clustering within a single unified framework, and can identify the inherent dimensionality of the system by enforcing physical constraints in the latent space. In order to efficiently describe the local environment of sPS monomers, we adapt an atomic representation used in machine learning. One of the advantages of using these descriptors is that they do not require incorporation of excessive systemspecific intuition and demonstrate good transferability properties. With this datadriven approach, we aim to characterise the pathways between polymorphic transitions. 
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