Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

We developed a general deep learning framework, FluidGAN, that is capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the physics of fluid without any prior knowledge of underlying fluid and energy transport physics. FluidGAN also learns the coupling between velocity, pressure and temperature fields. Our framework could be used to learn deterministic multiphysics phenomena where the underlying physical model is complex or unknown

Nanopores for detecting and sensing biological molecules

In spite of significant advances in the detection, separation and counting of single biological molecules (DNA, proteins, aminoacids, etc.) with solid-state nanopores, atomically-resolved scanning and detection of these molecules remains a significant challenge. In most nanopore-based DNA sequencing and single molecule detection techniques, ionic current blockade and blockade duration are the primary signatures associated with reading and scanning. Although these techniques are good enough for single molecule detection, they are not sophisticated enough to analyze and detect single DNA bases, fine structures, homologues and mutagenesis. Aside from the detection difficulties, low signal to noise ratio (SNR), fast speed of translocation, and lack of a cross-check signal are the biggest challenges of current nanopore technology. In this study, we explored different nanopore architectures and materials to find solutions to these current challenges. Using extensive atomistic simulations, we showed that a single layer molybdenum Disulfide (MoS2) nanopore is attractive pore for single base DNA detection with high SNR and multi-level conductance. We introduced and simulated MscL (Mechano-Sensitive Channel of Large Conductance) as an alternative to traditional biological nanopores (Alpha-Hemolysin, MspA) since it provides a flexible nanopore with adaptability to DNA base types. Induced tension in MscL is shown to be different and distinguishable for each DNA base type. The speed of DNA translocation is also decreased by one order of magnitude in MscL, providing a better detection resolution compared to its counterpart, e.g. MspA. Next, we explored DNA origami-graphene hybrid nanopore for DNA detection. We found that the dwell time of each base type in the hybrid pore is different and distinguishable compared to pristine graphene nanopore. The specific interaction (hydrogen bonds) between the complimentary bases at the edge of the pore and the translocating DNA bases give rise to distinguishable dwell time for each DNA. In addition to DNA sequencing studies, we also investigated the recognition of natively folded proteins using graphene nanopore. We specifically focused on the detection of Immunoglobin G subclasses since the separation and the detection of different subclasses of IgG is the signature of many diseases. These four subclasses differ only in their hinge regions and are 95% homologues. We showed that the one atom thick graphene is highly capable of distinguishing between the subclasses by using ionic current and water flux signals

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination. However, the optimization process often involves very large number of experiments or simulations which are expensive and time-consuming. In this work, we propose a graphene nanopore optimization framework via the combination of deep reinforcement learning (DRL) and convolutional neural network (CNN) for efficient water desalination. The DRL agent controls the growth of nanopore by determining the atom to be removed at each timestep, while the CNN predicts the performance of nanoporus graphene for water desalination: the water flux and ion rejection at a certain external pressure. With the synchronous feedback from CNN-accelerated desalination performance prediction, our DRL agent can optimize the nanoporous graphene efficiently in an online manner. Molecular dynamics (MD) simulations on promising DRL-designed graphene nanopores show that they have higher water flux while maintaining rival ion rejection rate compared to the normal circular nanopores. Semi-oval shape with rough edges geometry of DRL-designed pores is found to be the key factor for their high water desalination performance. Ultimately, this study shows that DRL can be a powerful tool for material design.Comment: Yuyang Wang and Zhonglin Cao contributed equally to this wor

Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models

Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representations, while integrating observable attributes remains challenging. This research introduces CatBERTa, an energy prediction Transformer model using textual inputs. Built on a pretrained Transformer encoder, CatBERTa processes human-interpretable text, incorporating target features. Attention score analysis reveals CatBERTa's focus on tokens related to adsorbates, bulk composition, and their interacting atoms. Moreover, interacting atoms emerge as effective descriptors for adsorption configurations, while factors such as bond length and atomic properties of these atoms offer limited predictive contributions. By predicting adsorption energy from the textual representation of initial structures, CatBERTa achieves a mean absolute error (MAE) of 0.75 eV-comparable to vanilla Graph Neural Networks (GNNs). Furthermore, the subtraction of the CatBERTa-predicted energies effectively cancels out their systematic errors by as much as 19.3% for chemically similar systems, surpassing the error reduction observed in GNNs. This outcome highlights its potential to enhance the accuracy of energy difference predictions. This research establishes a fundamental framework for text-based catalyst property prediction, without relying on graph representations, while also unveiling intricate feature-property relationships.Comment: 32 pages, 5 figure

Scalable Transformer for PDE Surrogate Modeling

Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PDEs). Despite the introduction of linear-complexity variant, applying attention to a large number of grid points can result in instability and is still expensive to compute. In this work, we propose Factorized Transformer(FactFormer), which is based on an axial factorized kernel integral. Concretely, we introduce a learnable projection operator that decomposes the input function into multiple sub-functions with one-dimensional domain. These sub-functions are then evaluated and used to compute the instance-based kernel with an axial factorized scheme. We showcase that the proposed model is able to simulate 2D Kolmogorov flow on a 256 by 256 grid and 3D smoke buoyancy on a 64 by 64 by 64 grid with good accuracy and efficiency. In addition, we find out that with the factorization scheme, the attention matrices enjoy a more compact spectrum than full softmax-free attention matrices

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view

MAN: Multi-Action Networks Learning

Learning control policies with large action spaces is a challenging problem in the field of reinforcement learning due to present inefficiencies in exploration. In this work, we introduce a Deep Reinforcement Learning (DRL) algorithm call Multi-Action Networks (MAN) Learning that addresses the challenge of large discrete action spaces. We propose separating the action space into two components, creating a Value Neural Network for each sub-action. Then, MAN uses temporal-difference learning to train the networks synchronously, which is simpler than training a single network with a large action output directly. To evaluate the proposed method, we test MAN on a block stacking task, and then extend MAN to handle 12 games from the Atari Arcade Learning environment with 18 action spaces. Our results indicate that MAN learns faster than both Deep Q-Learning and Double Deep Q-Learning, implying our method is a better performing synchronous temporal difference algorithm than those currently available for large action spaces