## New publication - Ligand Enhanced Activity of in Situ Formed Nanoparticles for Photocatalytic Hydrogen Evolution

| categories: | tags:

In this paper we show that we can stabilize in situ synthesized nanoparticles by including stabilizing ligands in the synthesis. This gives the in situ particles comparable stability to pre-synthesized particles, while retaining the flexibility of the in situ synthesis. We show that not all stabilizing ligands work for all metals, although PEGSH is a reasonable starting point for the metals we considered in this work. Additionally, we show that the incorporation of PEGSH can make some metals that appear inactive without stabilization due to precipitation, become active when stabilized. Although the addition of stabilizing ligands complicates the optimization of the synthesis conditions, it leads to more reproducible, and in some cases better results than leaving them out.

@article{simon-2021-ligan-enhan,
author =       {Zoe C. Simon and Eric M. Lopato and Maya
Bhat and Paige J. Moncure and Sarah M. Bernhard and John R.
Kitchin and Stefan Bernhard and Jill Millstone},
title =        {Ligand Enhanced Activity of in Situ Formed Nanoparticles for
Photocatalytic Hydrogen Evolution},
journal =      {ChemCatChem},
volume =       {},
number =       {},
pages =        {},
year =         2021,
doi =          {10.1002/cctc.202101551},
url =          {http://dx.doi.org/10.1002/cctc.202101551},
DATE_ADDED =   {Mon Nov 29 17:01:16 2021},
}


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## New publication - Uncertainty quantification in machine learning and nonlinear least squares regression models

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Say you have acquired some data, and fitted a nonlinear model to it. The fit looks good, but how good are predictions from the model? In high dimensional space it is tricky to tell if you are extrapolating, what should you do? First, read this paper! We illustrate a simple tool called the delta method that can help you estimate the prediction uncertainty from your model using automatic differentiation to get the required derivatives. We show some examples, and how you can use this method to refine what data you should use in fitting your models. We even show how to handle some tricky cases with models where the Hessian is not invertable! The examples are from molecular simulation, but the approach is general and should work for other models too.

@article{zhan-2021-uncer-quant,
author =       {Ni Zhan and John R. Kitchin},
title =        {Uncertainty Quantification in Machine Learning and Nonlinear
Least Squares Regression Models},
journal =      {AIChE Journal},
volume =       {},
number =       {},
pages =        {},
year =         2021,
doi =          {10.1002/aic.17516},
url =          {http://dx.doi.org/10.1002/aic.17516},
DATE_ADDED =   {Mon Nov 8 08:51:21 2021},
}


Checkout the video brief here:

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| categories: news | tags:

For fun, I have been live-streaming some of our research talks from the past year. Two of these talks are shown below. This is an experiment of sorts, let me know if you like them!

## 1. Machine learned potentials and automatic differentiation in molecular simulation

Machine learned potentials are revolutionizing molecular simulations. In this talk, I will introduce what machine learned potentials are, how we think about them, and how we create them. Then, I will show an example of how we use them to model segregation in a metal alloy surface at many different bulk compositions. Finally, I will show how automatic differentiation, which is one of the foundations of machine learning, can be used more broadly in scientific programming with derivatives.

This was the second talk I did, but it is somewhat of an introduction to the next talk below.

## 2. Leveraging machine learning to accelerate simulations of dilute alloy catalysts with adsorbates

In this talk I talk about how we use machine learning to build cheap and accurate surrogate models of alloy catalyst surfaces in the dilute limit. I will show how we use this to simulate acrolein adsorption on dilute Ag-Pd alloys.

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## New publication "Machine-learning accelerated geometry optimization in molecular simulation"

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Geometry optimization and transition state searches are two very common tasks in molecular simulation. Typically every one of these is done from scratch, and they can only be made faster by using a better initial guess. Typical optimization algorithms use some variation of gradient descent, and the best ones also use an iterative approach to estimate the Hessian (second derivatives). The problem is we do not know the underlying function that is being optimized, so there is hardly any choice to benefit from the Hessian (which allows bigger, more accurate steps to be taken).

In this paper, we use machine learning to develop a surrogate model that is cheap compared to the DFT calculations, and that has an uncertainty quantification so we can tell when it is accurate. This allows us to take many cheap steps when the surrogate model is accurate, and only do expensive calculations when needed. More importantly though, the surrogate model works across many different geometry optimizations, which allows us to benefit from previous calculations. We show this works on a variety of atomic geometries ranging from metal slabs, slabs with adsorbates, and nanoparticle geometries, as well as with nudged elastic band calculations for transitions state searches.

@article{yang-2021-machin-learn,
author =       {Yilin Yang and Omar A. Jim{\'e}nez-Negr{\'o}n and John R.
Kitchin},
title =        {Machine-Learning Accelerated Geometry Optimization in
Molecular Simulation},
journal =      {The Journal of Chemical Physics},
volume =       154,
number =       23,
pages =        234704,
year =         2021,
doi =          {10.1063/5.0049665},
url =          {https://doi.org/10.1063/5.0049665},
}


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## New publication - Semi-grand Canonical Monte Carlo Simulation of the Acrolein induced Surface Segregation and Aggregation of AgPd with Machine Learning Surrogate Models

| categories: news | tags:

Modeling alloys is tricky, and modeling dilute alloys has been an open challenge for a long time. Segregation is one of the biggest challenges; the surface composition is not the same as the bulk, and is influenced by adsorption. Although we know that the composition varies to lower the surface free energy, the configurational degrees of freedom make it difficult to find the lowest energy arrangement of atoms. In the dilute limit, the quantum chemical calculations and Monte Carlo simulates we do become very expensive due to the unit cell size. In this work, we use machine learning to build surrogate models for the configurational energy of an alloy slab in the dilute limit. Then, we use those models in conjunction with a semi-grand canonical Monte Carlo (MC) simulation to solve several of these problems. First, by fixing the alloy chemical potential at the desired dilute limit, we avoid the need for large unit cells. Second, the surrogate models are much more efficient than DFT, so we can use them in the MC simulations to run tens of thousands of simulation steps to get the required samples for reliable statistical averaging. In dilute alloys, the focus is on the unique catalytic properties of single atoms of an active metal like Pd in an inert metal like Ag. In the single atom limit though, there are few active sites. If you increase the bulk concentration, at some point the single atoms begin to aggregate into dimers and trimers, and adsorbates can make the aggregation happen faster. Aggregation is undesirable because it usually leads to lower selectivity, and more bulk like reactivity. An open question has been how do you design alloy catalysts under reaction conditions, given all this complexity. We apply this approach to the adsorption of acrolein on a dilute AgPd alloy, and show how to use the method to identify the bulk concentration where aggregation begins in a reactive environment.

@article{liu-2021-semi-grand,
author =       {Mingjie Liu and Yilin Yang and John R. Kitchin},
title =        {Semi-Grand Canonical Monte Carlo Simulation of the Acrolein
Induced Surface Segregation and Aggregation of {AgPd} With
Machine Learning Surrogate Models},
journal =      {The Journal of Chemical Physics},
volume =       154,
number =       13,
pages =        134701,
year =         2021,
doi =          {10.1063/5.0046440},
url =          {https://doi.org/10.1063/5.0046440},
}