Search

Molecular property prediction is a crucial step in many modern-day scientific applications including drug discovery and energy storage material design. Despite the availability of numerous machine learning models for this task, we are lacking in models that provide both high accuracies and interpretability of the predictions. We introduce the FragNet architecture, a graph neural network not only capable of achieving prediction accuracies comparable to the current state-of-the-art models, but also able to provide insight on four levels of molecular substructures. This model enables understanding of which atoms, bonds, molecular fragments, and molecular fragment connections are critical in the prediction of a given molecular property. The ability to interpret the importance of connections between fragments is of particular interest for molecules which have substructures that are not connected with regular covalent bonds. The interpretable capabilities of FragNet are key to gaining scientific insights from the model's learned patterns between molecular structure and molecular properties.

The properties of constrained fluids have increasingly gained relevance for applications ranging from materials to biology. In this work, we propose a multiscale model using twin neural networks to investigate the properties of a fluid constrained between solid surfaces with complex shapes. The atomic scale model and the mesoscale model are connected by the coarse-grained potential which is represented by the first neural network. Then we train the second neural network model as a surrogate to predict the velocity profile of the constrained fluid with complex boundary conditions at the mesoscale. The effect of complex boundary conditions on the fluid dynamics properties and the accuracy of the neural network model prediction are systematically investigated. We demonstrate that the neural network-enhanced multiscale framework can connect simulations at atomic scale and mesoscale and reproduce the properties of a constrained fluid at mesoscale. This work provides insight into multiscale model development with the aid of machine learning techniques and the developed model can be used for modern nanotechnology applications such as enhanced oil recovery and porous materials design.

Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using a ASO anolyte database with a thousand potential materials generated by a 780 $\text{cm}^2$ interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training process. This achieves higher efficiency and robustness compared to the non-physics-guided continual learning while retaining previously learned battery material knowledge. The trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers., Comment: 12 pages, 6 figures

The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory., Comment: 8 figures

Here, using unique in-situ liquid secondary ion mass spectroscopy on isotope-labelled solid-electrolyte-interphase (SEI), assisted by cryogenic transmission electron microscopy and constrained ab initio molecular dynamics simulation, for the first time we answer the question regarding Li+ transport mechanism across SEI, and quantitatively determine the Li+-mobility therein. We unequivocally unveil that Li+ transport in SEI follows a mechanism of successive displacement, rather than "direct-hopping". We further reveal, in accordance with spatial-dependence of SEI structure across the thickness, the apparent Li+ self-diffusivity varies from 6.7*10-19 m2/s to 1.0*10-20 m2/s, setting a quantitative gauging of ionic transport behavior of SEI layer against the underlining electrode as well as the rate limiting step of battery operation. This direct study on Li+ kinetics in SEI fills part of the decade-long knowledge gap about the most important component in advanced batteries and provides more precise guidelines to the tailoring of interphasial chemistries for future battery chemistries., Comment: 25 pages, 4 figures

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

Even though atomistic and coarse-grained (CG) models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are available, and most of them are limited to planar interfaces. In this work, we evaluate several existing force fields (FF)s, including two atomistic and three CG FFs, with respect to modeling the interface structure and thermodynamic properties of the water-hexane interface. Both atomistic FFs are able to quantitatively reproduce the interfacial tension and the coexisting densities of the experimentally-observed planar interface. We use the atomistic FFs to model water droplets in hexane and use these simulations to test the CG FFs. We find that the tested CG FFs cannot reproduce the interfacial tensions of planar and/or curved interfaces. Finally, we propose a new approach for learning CG potentials within the CG SDK (Shinoda-DeVane-Klein) FF framework from atomistic simulation data. We demonstrate that the new potential significantly improves the prediction of both the interfacial tension and structure of water-hexane planar and curved interfaces.

In this paper, we present a new coarse-grained (CG) model for poly (alpha-peptoid)s that is compatible with the MARTINI CG FF. In the proposed model, CG poly (alpha-peptoid) is composed by a CG backbone (here we select polysarcosine as the backbone) and side chain beads. The CG model of the backbone (polysarcosine) in a solvent is first developed and then extended to poly (alpha-peptoid)s with different side groups that can be obtained from MARTINI FF. We demonstrate that our CG model has good transferability. For example, the CG potentials for polysarcosine can be transferred to predict hydration free energy of other peptoids. Also, the CG polypeptoid model accurately predicts the radius of gyration over a wide range of chain lengths and the solvation free energy for relatively short peptoid molecules in good solvents. We use the CG model to study sequenced diblock polypeptoid in binary solvent mixtures and compare the results with the experimentally observed coil-globule transition.

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed the new approach to construct orthonormal polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters.