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Chan, Henry

Publications and source records attributed to Chan, Henry.

49 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

The ever-increasing power of modern super-computers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.

Chan, Henry↗
DOE OSTI2019

Ligand dynamics control structure, elasticity, and high-pressure behavior of nanoparticle superlattices

Precise engineering of nanoparticle superlattices (NPSLs) for energy applications requires a molecular-level understanding of the physical factors governing their morphology, periodicity, mechanics, and response to external stimuli. Such knowledge, particularly the impact of ligand dynamics on physical behavior of NPSLs, is still in its infancy. Here in this paper, we combine coarse-grained molecular dynamics simulations, and small angle X-ray scattering experiments in a diamond anvil cell to demonstrate that coverage density of capping ligands (i.e., number of ligands per unit area of a nanoparticle's surface), strongly influences the structure, elasticity, and high-pressure behavior of NPSLs using face-centered cubic PbS-NPSLs as a representative example. We demonstrate that ligand coverage density dictates (a) the extent of diffusion of ligands over NP surfaces, (b) spatial distribution of the ligands in the interstitial spaces between neighboring NPs, and (c) the fraction of ligands that interdigitate across different nanoparticles. We find that below a critical coverage density (1.8 nm-2 for 7 nm PbS NPs capped with oleic acid), NPSLs collapse to form disordered aggregates via sintering, even under ambient conditions. Above the threshold ligand coverage density, NPSLs surprisingly preserve their crystalline order even under high applied pressures (~40–55 GPa), and show a completely reversible pressure behavior. This opens the possibility of reversibly manipulating lattice spacing of NPSLs, and in turn, finely tuning their collective electronic, optical, thermo-mechanical, and magnetic properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Machine learning coarse grained models for water

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP dih , and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10’s of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model).

42 ENGINEERING↗
DOE OSTI2018

Crude-Oil-Repellent Membranes by Atomic Layer Deposition: Oxide Interface Engineering

Crude oil fouling on membrane surfaces is a persistent, crippling challenge in oil spill remediation and oilfield wastewater treatment. In this research, we present how a nanosized oxide coating can profoundly affect the anti crude-oil property of membrane materials. Select oxide coatings with a thickness of similar to 10 nm are deposited conformally on common polymer membrane surfaces by atomic layer deposition to significantly mitigate fouling during filtration processes. TiO 2 - and SnO 2 -coated membranes exhibited far greater anti-crude-oil performance than ZnO- and Al 2 O 3 -coated ones. Tightly bound hydration layers play a crucial role in protecting the surface from crude oil adhesion, as revealed by molecular dynamics simulations. Furthermore, this work provides a facile strategy to fabricate crude oil-resistant membranes with negligible impact on membrane structure, and also demonstrates that, contrary to common belief, excellent crude oil resistance can be achieved easily without implementation of sophisticated, hierarchical structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back-mapping, of coarse grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force-fields and often rely on running a long time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias-Monte-Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias-Monte-Carlo (C-CBMC) algortihm, that allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back-mapping or reverse transformation for model systems ranging from the ice-liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and Molecular Dynamics based back-mapping techniques. In all the cases, the C-CBMC algorithm is able to find optimal hydrogen bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground state energies predicted by the other methods. Detailed analysis of the atomistic structures show a significantly better global hydrogen positioning when contrasted with the existing simpler back-mapping methods. The error in the RDFs of backmapped relative to reference configuration for the CCBMC, MD, and MC were found to be 6.9, 8.7, and 12.9 respectively for the hexagonal system. For the cubic system the relative error of the RDFs for the CCBMC, MD, and MC were found to be 18.2, 34.6, and 39.0 respectively. Finally, our results demonstrate the efficiency and efficacy of our new back-mapping approach, especially for crystalline systems where simple force-field based relaxations have a tendency to get trapped in local minima.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Thermomechanical Response of Self-Assembled Nanoparticle Membranes

Monolayers composed of colloidal nanoparticles, with a thickness of less than 10 nm, have remarkable mechanical moduli and can suspend over micrometer-sized holes to form free-standing membranes. In this paper, we discuss experiment's and coarse-grained molecular dynamics simulations characterizing the thermomechanical properties of these self-assembled nanoparticle membranes. These membranes remain strong and resilient up to temperatures much higher than previous simulation predictions and exhibit an unexpected hysteretic behavior during the first heating cooling cycle. We show this hysteretic behavior can be explained by an asymmetric ligand configuration from the self assembly process and can be controlled by changing the ligand coverage or cross-linking the ligand molecules. Finally, we show the screening effect of water molecules on the ligand interactions can strongly affect the moduli and thermomechanical behavior.

mechanical↗
DOE OSTI2017

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff–Brenner type bond order potential (BOP) for a Co–C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure–property relationships, and temperature/pressure response of Co–C systems, as well as design organic–inorganic hybrid structures with a desired set of properties.

Narayanan, Badri↗
DOE OSTI2016

Self-assembly of nanoparticles into biomimetic capsid-like nanoshells

Nanoscale compartments are one of the foundational elements of living systems. Capsids, carboxysomes, exosomes, vacuoles and other nanoshells easily self-assemble from biomolecules such as lipids or proteins, but not from inorganic nanomaterials because of difficulties with the replication of spherical tiling. Here we show that stabilizer-free polydispersed inorganic nanoparticles (NPs) can spontaneously organize into porous nanoshells. The association of water-soluble CdS NPs into self-limited spherical capsules is the result of scale-modified electrostatic, dispersion and other colloidal forces. They cannot be accurately described by the Derjaguin–Landau–Vervey–Overbeek theory, whereas molecular-dynamics simulations with combined atomistic and coarse-grained description of NPs reveal the emergence of nanoshells and some of their stabilization mechanisms. Morphology of the simulated assemblies formed under different conditions matched nearly perfectly the transmission electron microscopy tomography data. This study bridges the gap between biological and inorganic self-assembling nanosystems and conceptualizes a new pathway to spontaneous compartmentalization for a wide range of inorganic NPs including those existing on prebiotic Earth.

Yang, Ming↗