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Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Ab initio study of ${(\nu_\ell,\ell^-)}$ and ${(\overline{\nu}_\ell,\ell^+)}$ inclusive scattering in $^{12}\hbox{C}$: confronting the MiniBooNE and $\hbox{T2K CCQE}$ data

We carry out an ab initio calculation of the neutrino flux-folded inclusive cross sections, measured on $^{12}$C by the MiniBooNE and T2K collaborations in the charged-current quasielastic regime. The calculation is based on realistic two- and three-nucleon interactions, and on a realistic nuclear electroweak current with one-and two-nucleon terms that are constructed consistently with these interactions and reproduce low-energy electroweak transitions. Numerically exact quantum Monte Carlo methods are utilized to compute the nuclear weak response functions, by fully retaining many-body correlations in the initial and final states and interference effects between one- and two-body current contributions. We employ a nucleon axial form factor of the dipole form with $\Lambda_A = 1.0$ or $1.15$ GeV, the latter more in line with a very recent lattice QCD determination. The calculated cross sections are found to be in good agreement with the neutrino data of MiniBooNE and T2K, and antineutrino MiniBooNE data, yielding a consistent picture of nuclei and their electroweak properties across a wide regime of energy and momenta.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Experimental and computational studies of Criegee intermediate reactions with NH 3 and CH 3 NH 2

Ammonia and amines are emitted into the troposphere by various natural and anthropogenic sources, where they have a significant role in aerosol formation. Here, we explore the significance of their removal by reaction with Criegee intermediates, which are produced in the troposphere by ozonolysis of alkenes. Rate coefficients for the reactions of two representative Criegee intermediates, formaldehyde oxide (CH 2 OO) and acetone oxide ((CH 3 ) 2 COO) with NH 3 and CH 3 NH 2 were measured using cavity ring-down spectroscopy. Temperature-dependent rate coefficients, k(CH 2 OO + NH 3 ) = (3.1 ± 0.5) × 10 -20 T 2 exp(1011 ± 48/T) cm 3 s -1 and k(CH 2 OO + CH 3 NH 2 ) = (5 ± 2) × 10 -19 T 2 exp(1384 ± 96/T) cm 3 s -1 were obtained in the 240 to 320 K range. Both the reactions of CH 2 OO were found to be independent of pressure in the 10 to 100 Torr (N 2 ) range, and average rate coefficients k(CH 2 OO + NH 3 ) = (8.4 ± 1.2) × 10 -14 cm 3 s -1 and k(CH 2 OO + CH 3 NH 2 ) = (5.6 ± 0.4) × 10 -12 cm 3 s -1 were deduced at 293 K. An upper limit of ≤2.7 × 10 -15 cm 3 s -1 was estimated for the rate coefficient of the (CH 3 ) 2 COO + NH3 reaction. Complementary measurements were performed with mass spectrometry using synchrotron radiation photoionization giving k(CH 2 OO + CH 3 NH 2 ) = (4.3 ± 0.5) × 10-12 cm3 s-1 at 298 K and 4 Torr (He). Photoionization mass spectra indicated production of NH 2 CH 2 OOH and CH 3 N(H)CH 2 OOH functionalized organic hydroperoxide adducts from CH 2 OO + NH 3 and CH 2 OO + CH 3 NH 2 reactions, respectively. Ab initio calculations performed at the CCSD(T)(F12*)/cc-pVQZ-F12//CCSD(T)(F12*)/cc-pVDZ-F12 level of theory predicted pre-reactive complex formation, consistent with previous studies. Master equation simulations of the experimental data using the ab initio computed structures identified submerged barrier heights of -2.1 ± 0.1 kJ mol -1 and -22.4 ± 0.2 kJ mol -1 for the CH 2 OO + NH 3 and CH 2 OO + CH 3 NH 2 reactions, respectively. The reactions of NH3 and CH3NH2 with CH 2 OO are not expected to compete with its removal by reaction with (H 2 O) 2 in the troposphere. Similarly, losses of NH 3 and CH 3 NH 2 by reaction with Criegee intermediates will be insignificant compared with reactions with OH radicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Development of reactive force fields using ab initio molecular dynamics simulation minimally biased to experimental data

Incorporation of quantum mechanical electronic structure data is necessary to properly capture the physics of many chemical processes. Proton hopping in water, which involves rearrangement of chemical and hydrogen bonds, is one such example of an inherently quantum mechanical process. Standard ab initio molecular dynamics (AIMD) methods, however, do not yet accurately predict the structure of water and are therefore less than optimal for developing force fields. We have instead utilized here a recently developed method which minimally biases AIMD simulations to match limited experimental data to develop novel multiscale reactive molecular dynamics (MS-RMD) force fields by using relative entropy minimization. In this paper, we present two new MS-RMD models using such a parameterization: one which employs water with harmonic internal vibrations and another which uses anharmonic water. We show that the newly developed MS-RMD models very closely reproduce the solvation structure of the hydrated excess proton in the target AIMD data. We also find that the use of anharmonic water increases proton hopping, thereby increasing the proton diffusion constant.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

battery↗
DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thermal scattering law for structure-dependent-Doppler broadening in FLASSH

Traditional Doppler broadening methods assume that target nuclei velocities follow a Maxwell-Boltzmann distribution which inherently assumes that the atoms are a free gas or that their velocities are independent of each other. This approximation is reasonable at high target temperatures and high neutron energies. However, nuclei are not independent: the lattice temperature and chemical binding will define the atomic motion. The thermal scattering law (i.e., TSL or S(α,β)) is a material property which describes the chemical binding and temperature response in terms of available momentum and energy states. In the thermal energy range, this TSL will define the thermal scattering cross sections. In the resonance region, the TSL offers a first-principles description of the probability distribution function for the velocity of the target nuclei. Using the TSL in Doppler broadening captures the structure of the material consistently from the thermal range into the resonance cross sections. In this work, both traditional free-gas and precise TSL Doppler broadening methods for resonance cross section evaluation have been implemented within the Full Law Analysis Scattering System Hub (FLASSH). This framework provides a generalized formulation for evaluating low-lying resonance data and streamlines nuclear data processing methods. TSL data generated using ab initio lattice dynamics (AILD) can be used to broaden cross section libraries in ENDF format for high-fidelity input into reactor physics calculations. These capabilities are demonstrated for the lowest absorption resonances of {sup 238}U in UO{sub 2}, UC and UN. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Elucidating the Chemical Dynamics of the Elementary Reactions of the 1-Propynyl Radical (CH 3 CC; X 2 A 1 ) with Methylacetylene (H 3 CCCH; X 1 A 1 ) and Allene (H 2 CCCH 2 ; X 1 A 1 )

The reactions of the 1-propynyl radical (CH 3 CC; X 2 A 1 ) with two C 3 H 4 isomers, methylacetylene (H 3 CCCH; X 1 A 1 ) and allene (H 2 CCCH 2 ; X 1 A 1 ), along with their (partially) deuterated counterparts were explored at collision energies of 37 kJ mol –1 , exploiting crossed molecular beams to unravel the chemical reaction dynamics to synthesize distinct C 6 H 6 isomers under single collision conditions. The forward convolution fitting of the laboratory data along with ab initio and statistical calculations revealed that both reactions have no entrance barrier, proceed via indirect (complex-forming) reaction dynamics involving C 6 H 7 intermediates with life times longer than their rotation period(s), and are initiated by the addition of the 1-propynyl radical with its radical center to the π-electron density of the unsaturated hydrocarbon at the terminal carbon atoms of methylacetylene (C1) and allene (C1/C3). In the methylacetylene system, the initial collision complexes undergo unimolecular decomposition via tight exit transition states by atomic hydrogen loss, forming dimethyldiacetylene (CH 3 CCCCCH 3 ) and 1-propynylallene (H 3 CCCHCCCH 2 ) in overall exoergic reactions (123 and 98 kJ mol –1 ) with a branching ratio of 9.4 ± 0.1; the methyl group of the 1-propynyl reactant acts solely as a spectator. On the other hand, in the allene system, our experimental data exhibit the formation of the fulvene (c-C 5 H 4 CH 2 ) isomer via a six-step reaction sequence with two higher energy isomers—hexa-1,2-dien-4-yne (H 2 CCCHCCCH 3 ) and hexa-1,4-diyne (HCCCH 2 CCCH 3 )—also predicted to be formed based on our statistical calculations. Here, the pathway to fulvene advocates that, in the allene–1-propynyl system, the methyl group of the 1-propynyl reactant is actively engaged in the reaction mechanism to form fulvene. Because both reactions are barrierless and exoergic and all transition states are located below the energy of the separated reactants, the hydrogen-deficient C 6 H 6 isomers identified in our investigation are predicted to be synthesized in low-temperature environments, such as in hydrocarbon-rich atmospheres of planets and their moons such as Titan along with cold molecular clouds such as Taurus Molecular Cloud-1.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Predicting mesoscale spectral thermal conductivity using advanced deterministic phonon transport techniques

We present a review, demonstration and simulation of phonon transport for the purposes of predicting materials performance at the mesoscale. We focus primarily on the development and implementation of a unified methodology to enable predictive heat transport. We report on the current state of the art as it pertains to deterministic phonon transport methodologies, discussing various topics concerning phonons. In application, we focus on the Self-Adjoint Angular Flux (SAAF) formulation of the Boltzmann transport equation for phonons, and develop the spatial, angular, and material property discretization required to accurately simulate the predictive physics of heat transport in dielectrics. We discuss thermal interfacial resistance and present our formulation of the diffuse mismatch model for simulating phonon interactions at internal boundaries. We have recently developed a deterministic, spectral phonon transport method for predicting effective thermal conductivity ($\kappa_{\textrm{eff}}$), using Bose-Einstein source terms coupled through an average material temperature. In this method, we introduce a closure term to the phonon transport system which acts as a redistribution function for the total energy of the system, and serves as a guide for the amount of non-equilibrium behavior occurring in the system. This method predicts thermal conductivity and equilibrium temperature distributions in homogeneous and heterogeneous materials using data generated by ab initio density functional theory methods. We employ polarization, density of states and full dispersion spectra to resolve thermal conductivity with numerous angular and spatial discretizations. Our implementation utilizes a Richardson iteration on a modified version of the phonon scattering source. The equations associated with this method are solved via a modification of traditional source iteration. We compare the performance of source iteration applied to an existing uncoupled, traditional SAAF method to our new method and comment on the iterative performance of each. We observe ballistic and diffusive phonon scattering as acoustic thickness of the domain changes, and are able to make comparisons between the accuracy and efficiency of both methods.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Temperature induced phase transition of CaMn{sub 0.5}Zr{sub 1.5}(PO{sub 4}){sub 3} phosphate

In this work we investigated the structural behaviour of a CaMn{sub 0.5}Zr{sub 1.5}(PO{sub 4}){sub 3}. Due to the presence of divalent Mn{sup 2+} cations this compound can possess interesting luminescence properties. It was recently understood that this phosphate undergoes a temperature induced irreversible phase transition in the range of 800–875 °C. It has also been shown that the 3d–3d luminescence of Mn{sup 2+} increases 10 fold for the high temperature polymorph. To determine the Mn environment structural investigations of both phases have been performed by the X-ray powder diffraction and Raman spectroscopy methods. The low temperature modification adopts the trigonal NZP structure type with a slightly lower symmetry (space group R32, a=8.7850(2) Å, c=22.6496(7) Å, V=1514.8(1) Å{sup 3}). The high temperature form in turn has orthorhombic symmetry (space group Pnma, a=6.2350(3) Å, b=6.6281(3) Å, c=14.4731(6) Å, V=598.13(5) Å{sup 3}). Both structures were solved ab-initio from powder data and structural analysis was performed. In-situ and RT Raman spectra are consistent with the XRD derived structural model. Mn{sup 2+} cations occupy different types of positions in these structures and a change in Mn coordination number (6 for LT phase, 7 for HT phase) results in different Mn–O bond lengths. These differences may explain the change in the optical properties between the polymorphs. - Graphical abstract: The compound CaMn{sub 0.5}Zr{sub 1.5}(PO{sub 4}){sub 3} was synthesized in order to create a material with enhanced luminescent properties. The goal of present studies is to define Mn{sup 2+} environment and its changes due to the structural transformations of the phosphate along phase transition at the T range of 800–875 °C. It was found that LT modification adopts the trigonal NZP structure type, sp.gr. R32, the HT form in turn exhibits orthorhombic symmetry sp.gr. Pnma. Mn2+ cations occupy different types of positions in those structures and a change in coordination number of Mn (6 for LT phase, 7 for HT phase) results in a change in Mn–O bond lengths.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Competing Magnetic Interactions and the Role of Unpaired 4 f Electrons in Oxygen-Deficient Perovskites Ba 3 R Fe 2 O 7.5 ( R = Y, Dy)

Oxygen-deficient perovskite compounds with the general formula Ba 3 RFe 2 O 7.5 present a good opportunity to study competing magnetic interactions between Fe 3+ 3d cations with and without the involvement of unpaired 4f electrons on R 3+ cations. From analysis of neutron powder diffraction data, complemented by ab initio density functional theory calculations, we determined the magnetic ground states when R 3+ = Y 3+ (non-magnetic) and Dy 3+ (4f 9 ). They both adopt complex long-range ordered antiferromagnetic structures below T N = 6.6 and 14.5 K, respectively, with the same magnetic space group C a 2/c (BNS #15.91). However, the dominant influence of f-electron magnetism is clear in temperature dependence and differences between the size of the ordered moments on the two crystallographically independent Fe sites, one of which is enhanced by R–O–Fe superexchange in the Dy compound, while the other is frustrated by it. We report the Dy compound also shows evidence for temperature- and field-dependent transitions with hysteresis, indicating a field-induced ferromagnetic component below TN.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Understanding Strain and Failure of a Knot in Polyethylene Using Molecular Dynamics with Machine-Learned Potentials

A neural network potential (NNP) has been developed by fitting to ab initio electronic structure data on hydrocarbons and is used to study failure of linear and knotted polyethylene (PE) chains. A linear PE chain must be highly strained before breaking as the stress is equally distributed across the chain. In contrast, the stress in a PE chain with a 31 or overhand knot, accumulates at the knot’s entrance/exit. We find the strain energy is greatest when the bond length and angle are strained simultaneously, and that the knot weakens the chain by increasing the variance of the C–C–C angle, thereby allowing rupture at lower bond strains. Here, we extend our analysis to both 51 and 52 knots and find that both break at the entrance/exit of a loop. Notably, molecular scale PE knots exhibit many of the same characteristics as knots in a macroscopic rope, with stick–slip phenomena upon tightening and similar points of failure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗