In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.
Compositionally complex alloys or concentrated solid solutions are the latest frontier in catalyst design, but mixing different elements in one catalyst may result in surface segregation. Atomistic simulations can predict segregation patterns, but standard approaches based on mean-field models, cluster expansion, or classical interatomic potentials are often limited for the description of multicomponent alloys. We present machine learning potentials that can describe surface segregation with near DFT accuracy. The method is used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, …
To help understand the high activity of silver as an oxidation catalyst, e.g., for the oxidation of ethylene to epoxide and the dehydrogenation of methanol to formaldehyde, the interaction and stability of oxygen species at the Ag(111) surface has been studied for a wide range of coverages. Through calculation of the free energy, as obtained from density-functional theory and taking into account the temperature and pressure via the oxygen chemical potential, we obtain the phase diagram of O/Ag(111). Our results reveal that a thin surface-oxide structure is most stable for the temperature and p…
We present a density-functional theory based Wulff construction of the equilibrium shape of RuO2 particles in an oxygen environment. The obtained intricate variations of the crystal habit with the oxygen chemical potential allow for a detailed discussion of the dependence on the oxidizing pretreatment observed in recent powder catalyst studies. The analysis specifically indicates an incomplete particle shape equilibration in previously employed low temperature calcination. Equilibrated particles could be active CO oxidation catalysts with long-term stability in oxidizing feed and then represen…
We explore the role of Mo in Fe:Mo nanocatalyst thermodynamics for low-temperature chemical vapor deposition growth of single walled carbon nanotubes (SWCNTs). By using the size-pressure approximation and ab initio modeling, we prove that for both Fe-rich (~80% Fe or more) and Mo-rich (~50% Mo or more) Fe:Mo clusters, the presence of carbon in the cluster causes nucleation of Mo2C. This enhances the activity of the particle since it releases Fe, which is initially bound in a stable Fe:Mo phase, so that it can catalyze SWCNT growth. Furthermore, the presence of small concentrations of Mo reduce…
Introduction of interstitial dopants has opened a new pathway to optimize nanoparticle catalytic activity for, e.g., hydrogen evolution/oxidation and other reactions. Here, we discuss the stability of a property-enhancing dopant, B, introduced through controlled synthesis of an electrocatalyst Pd aerogel. We observe significant removal of B after the hydrogen evolution/oxidation reaction. Ab-initio calculations show that the high stability of sub-surface B in Pd is substantially reduced when H is ad/absorbed on the surface, favoring its departure from the host nanostructure. The destabilizatio…
The use of proton exchange membrane (PEM) electrolyzers is the method of choice for the conversion of solar energy when frequently occurring changes of the current load are an issue. However, this technique requires electrolytes with low pH. All oxygen evolving electrodes working durably and actively in acids contain IrOx. Due to their scarcity and high acquisition costs, noble elements like Pt, Ru and Ir need to be replaced by earth abundant elements. We have evaluated a cobalt containing steel for use as an oxygen-forming electrode in H2SO4. We found that the dissolving of ingredients out of…
Fuel cells recombine water from H2 and O2 thereby powering e.g. cars or houses with no direct carbon emission. In anion-exchange membrane fuel cells (AEMFCs), to reach high power densities, operating at high pH is an alternative to using large volumes of noble metals catalysts at the cathode, where the oxygen-reduction reaction occurs. However, the sluggish kinetics of the hydrogen-oxidation reaction (HOR) hinders upscaling despite promising catalysts. Here, we observe an unexpected ingress of B into Pd nano-catalysts synthesised by wet-chemistry, gain control over this B-doping, and report on…
The catalyst development for N2 electroreduction reaction (NRR) with low onset potential and high Faradaic efficiency is highly desired, but remains challenging. Machine learning (ML) recently emerged as a complementary tool to accelerate material discovery; however a ML model for NRR has yet to be developed. Here, we develop and report slab-graph convolutional neural network (SGCNN), an accurate and flexible ML model that is applicable to catalytic surface reactions. With the self-accumulated database of 2,699 surface calculations, SGCNN predict binding energies, ranging over 8 eV, of five ke…
Potential-Induced Dynamic Coordination of Nonmetal Atoms Directly Bound to Metal Centers in Graphene-Embedded Single-Atom Catalysts and Its Implications
Anhui Provincial Collaborative Innovation Center for Advanced Functional Composite Materials, College of Physics and Electronic Engineering, Huaibei Normal University（淮北师范大学物理与电子工程学院安徽省先进功能复合材料协同创新中心）
Calculations of the potential energy surface for tracer Ga and In adatoms above three GaAs (111)A surface reconstructions are presented in order to understand the growth conditions required to form axial heterostructures in GaAs/InGaAs nano-pillars. In all calculations the Ga adatom has a stronger bond energy to the surface than the In adatom. The diffusion barriers for Ga adatoms are 140meV larger than for In adatoms on the Ga vacancy surface, but they are comparable on the As trimer surface. Also the binding energy for an In adatom is closer to that of a Ga adatom on the As trimer surface. W…
We study the dissociative adsorption of methane at the surface of graphene. Free energy profiles, which include activation energies for different steps of the reaction, are computed from constrained ab initio molecular dynamics. At 300 K, the reaction barriers are much lower than experimental bond dissociation energies of gaseous methane, strongly indicating that graphene surface acts as a catalyst of methane decomposition. On the other hand, the barriers are still much higher than on nickel surface. Methane dissociation therefore occurs at a higher rate on nickel than on graphene. This reacti…
The conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane) facilitates its transportation and provides a way to synthesize higher value chemicals. The search for high-performance catalysts to achieve this conversion is the main scope of most corresponding studies in the field of OCM. Here, we present a general data-driven strategy for the search of novel catalytic materials, focusing particularly on materials useful for the OCM reaction. Our strategy is based on consistent experimental measurements and includes ab initio thermodynamics calculations and …
During many of its applications (especially as a catalyst support material), MoO3 acts as a medium for hydrogen storage via hydrogen spillover (H atom donation from proton and electron sources to a support), for which the energetics of H atoms on MoO3 are of importance. Despite the seeming simplicity of hydrogen spillover, previously reported ab initio results for the H adsorption on MoO3 contradict both experimental work and other ab initio results. In the present study, we resolve these discrepancies and provide a comprehensive ab initio understanding of H adsorption for MoO3 in the bulk and…
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces
Department of Computer Science；Engineering, Hong Kong University of Science；Department of Chemistry, Hong Kong University of Science；Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland；Platform Laboratory for Science \& Technology, Asahi Kasei Corporation, Tokyo, Japan；IAS Center for AI for Scientific Discoveries, Hong Kong University of Science
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so precise description of host-guest interactions is essential for high-throughput screening aimed at ranking the most promising candidates. However, highly accurate ab initio calculations cannot be routinely applied to model thousands of structures due to the demanding computational costs. On the other side, methods based on force field (FF) parametrization s…
