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Register a new userComputational catalyst discovery: Active classification through myopic multiscale sampling
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The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straight-forward when using ensembles of atomic-scale calculations (e.g., density functional theory). We attempt to address this issue by creating a multiscale model that estimates bulk catalyst activity using adsorption energy predictions from both density functional theory and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words: why invest so much time finding a best catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: What is the set of catalysts that is worth testing experimentally? Here we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of density functional theory calculations. It is an active classification strategy that seeks to classify catalysts as worth investigating or not worth investigating experimentally. Our results show a ~7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.
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Porous flow-through electrodes are used as the core reactive component across electrochemical technologies. Controlling the fluid flow, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. The variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.
In this paper, we propose a data-driven method to discover multiscale chemical reactions governed by the law of mass action. First, we use a single matrix to represent the stoichiometric coefficients for both the reactants and products in a system without catalysis reactions. The negative entries in the matrix denote the stoichiometric coefficients for the reactants and the positive ones for the products. Second, we find that the conventional optimization methods usually get stuck in the local minima and could not find the true solution in learning the multiscale chemical reactions. To overcome this difficulty, we propose a partial-parameters-freezing (PPF) technique to progressively determine the network parameters by using the fact that the stoichiometric coefficients are integers. With such a technique, the dimension of the searching space is gradually reduced in the training process and the global mimina can be eventually obtained. Several numerical experiments including the classical Michaelis-Menten kinetics and the hydrogen oxidation reactions verify the good performance of our algorithm in learning the multiscale chemical reactions. The code is available at url{https://github.com/JuntaoHuang/multiscale-chemical-reaction}.
A new steady-state kinetic model of ammonia decomposition is presented and analyzed regarding the electronic properties of metal catalysts. The model is based on the classical Temkin-Ertl mechanism and modified in accordance with Wolkenstein electronic theory by implementing participation of free electrons of the catalyst to change the chemical nature of adsorbed species. Wolkenstein original theory only applied to semiconductors but by including the d-band model, the electronic theory can be extended to metals. For both simplified and full reaction mechanisms, including electronic steps, we present a steady-state rate equation where the dependence on the Fermi level of the metal creates a volcano-shaped dependence. According to the kinetic model, an increasing Fermi level of the catalyst, that approaching the antibonding state with adsorbed nitrogen molecules, will increase the fraction of neutral nitrogen molecules and enhance their the desorption. Concurrently, strong chemisorption of ammonia molecules proceeds easily through participation of additional free catalyst electrons in the adsorbate bond. As a result, the reaction rate is enhanced and reaches its maximum value. A further increasing Fermi level of the catalyst that approaches the antibonding state with ammonia molecules will result in a smaller fraction of negatively charged ammonia molecules and less dehydrogenation. Concurrently, the desorption of neutral nitrogen molecules occurs without impairment. As a result, the reaction rate decreases. The detailed kinetic model is compared to recent experimental measurements of ammonia decomposition on iron, cobalt and CoFe bimetallic catalysts.
Drawing independent samples from a probability distribution is an important computational problem with applications in Monte Carlo algorithms, machine learning, and statistical physics. The problem can in principle be solved on a quantum computer by preparing a quantum state that encodes the entire probability distribution followed by a projective measurement. We investigate the complexity of adiabatically preparing such quantum states for the Gibbs distributions of various classical models including the Ising chain, hard-sphere models on different graphs, and a model encoding the unstructured search problem. By constructing a parent Hamiltonian, whose ground state is the desired quantum state, we relate the asymptotic scaling of the state preparation time to the nature of transitions between distinct quantum phases. These insights enable us to identify adiabatic paths that achieve a quantum speedup over classical Markov chain algorithms. In addition, we show that parent Hamiltonians for the problem of sampling from independent sets on certain graphs can be naturally realized with neutral atoms interacting via highly excited Rydberg states.
This paper extends the quantum search class of algorithms to the multiple solution case. It is shown that, like the basic search algorithm, these too can be represented as a rotation in an appropriately defined two dimensional vector space. This yields new applications - an algorithm is presented that can create an arbitrarily specified quantum superposition on a space of size N in O(sqrt(N)) steps. By making a measurement on this superposition, it is possible to obtain a sample according to an arbitrarily specified classical probability distribution in O(sqrt(N)) steps. A classical algorithm would need O(N) steps.
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