Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Single-layer metal dichalcogenides are two-dimensional semiconductors that present strong potential for electronic and sensing applications complementary to that of graphene.

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Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

We present a method for calculating the stability of reaction intermediates of electrochemical processes on the basis of electronic structure calculations. We used that method in combination with detailed density functional calculations to develop a detailed description of the free-energy landscape of the electrochemical oxygen reduction reaction over Pt(111) as a function of applied bias. This allowed us to identify the origin of the overpotential found for this reaction. Adsorbed oxygen and hydroxyl are found to be very stable intermediates at potentials close to equilibrium, and the calculated rate constant for the activated proton/electron transfer to adsorbed oxygen or hydroxyl can account quantitatively for the observed kinetics. On the basis of a database of calculated oxygen and hydroxyl adsorption energies, the trends in the oxygen reduction rate for a large number of different transition and noble metals can be accounted for. Alternative reaction mechanisms involving proton/electron transfer to ...

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Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode

TiO2 photocatalysis and related surface phenomena

Platinum-based heterogeneous catalysts are critical to many important commercial chemical processes, but their efficiency is extremely low on a per metal atom basis, because only the surface active-site atoms are used. Catalysts with single-atom dispersions are thus highly desirable to maximize atom efficiency, but making them is challenging. Here we report the synthesis of a single-atom catalyst that consists of only isolated single Pt atoms anchored to the surfaces of iron oxide nanocrystallites. This single-atom catalyst has extremely high atom efficiency and shows excellent stability and high activity for both CO oxidation and preferential oxidation of CO in H-2. Density functional theory calculations show that the high catalytic activity correlates with the partially vacant 5d orbitals of the positively charged, high-valent Pt atoms, which help to reduce both the CO adsorption energy and the activation barriers for CO oxidation.

Single-atom catalysis of CO oxidation using Pt1/FeOx

http://absimage.aps.org/image/MAR06/MWS_MAR06-2005-001446.pdf

One-dimensional PtO$_{2}$ oxide at Pt steps: formation and reaction with CO

Dissociation of N2, NO and CO on Transition Metal Surfaces

Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these methods often rely on a graph representation, ignoring 3D information necessary for determining the stability of the molecules. We propose a reinforcement learning approach for generating molecules in cartesian coordinates allowing for quantum chemical prediction of the stability. To improve sample-efficiency we learn basic chemical rules from imitation learning on the GDB-11 database to create an initial model applicable for all stoichiometries. We then deploy multiple copies of the model conditioned on a specific stoichiometry in a reinforcement learning setting. The models correctly identify low energy molecules in the database and produce novel isomers not found in the training set. Finally, we apply the model to larger molecules to show how reinforcement learning further refines the imitation learning model in domains far from the training data.

Generating stable molecules using imitation and reinforcement learning

Nanomaterials based on MoS2 and related transition metal dichalcogenides (TMDCs) are remarkably versatile; MoS2 nanoparticles are proven catalysts for processes such as hydrodesulphurization and the hydrogen evolution reaction, and transition metal dichalcogenides in general have recently emerged as novel 2D components for nanoscale electronics and optoelectronics. The properties of such materials are intimately related to their structure and dimensionality. For example, only the edges exposed by MoS2 nanoparticles (NPs) are catalytically active, and extended MoS2 systems show different character (direct or indirect gap semiconducting, or metallic) depending on their thickness and crystallographic phase. In this work, we show how particle size and interaction with a metal surface affect the stability and properties of different MoS2 NPs and the resulting phase diagrams. By means of calculations based on the Density Functional Theory (DFT), we address how support interactions affect MoS2 nanoparticles of varying size, composition, and structure. We demonstrate that interaction with Au modifies the relative stability of the different nanoparticle types so that edge terminations and crystallographic phases that are metastable for free-standing nanoparticles and monolayers are expressed in the supported system. These support-effects are strongly size-dependent due to the mismatch between Au and MoS2 lattices, which explains experimentally observed transitions in the structural phases for supported MoS2 NPs. Accounting for vdW interactions and the contraction of the Au(111) surface underneath the MoS2 is further found to be necessary for quantitatively reproducing experimental results. We finally find that support-induced effects on the stability of nanoparticle structures are general to TMDC nanoparticles on metal surfaces, which we demonstrate also for MoS2 on Au(111), WS2 on Au(111), and WS2 on Ag(111). This work demonstrates how the properties of nanostructured transition metal dichalcogenides and similar layered systems can be modified by the choice of supporting metal.

Bjørk Hammer

Papers

One-dimensional PtO$_{2}$ oxide at Pt steps: formation and reaction with CO

Dissociation of N2, NO and CO on Transition Metal Surfaces

Generating stable molecules using imitation and reinforcement learning

Size-dependent phase stability in transition metal dichalcogenide nanoparticles controlled by metal substrates.

Density-Functional Calculations of N-2 Adsorption and Dissociation on a Ru(0001) Surface (Vol 169, Pg 85, 1997)