Using Machine Learning to Understand the Causes of Quantum Decoherence in Solution-Phase Bond-Breaking Reactions
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “computational materials and chemistry”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Real-space Kohn–Sham density functional theory for complex energy applications
Real-space Kohn-Sham density functional theory (real-space KS-DFT) enables large-scale electronic structure simulations that is particularly well-suited for the modern high-performance computing (HPC) architectures. This feature article reviews its theoretical foundations, highlights the algorithmic advances and recent developments, and showcases applications in complex nano systems. We aim to provide a perspective on the trajectory of real-space KS-DFT as an emerging tool for computational chemistry and materials science in the exascale era.
Zhang, Zeyi↗
57Fe Mössbauer isomer shift of pure iron and iron oxides at high pressure—An experimental and theoretical study
The 57Fe isomer shift (IS) of pure iron has been measured up to 100 GPa using synchrotron Mössbauer spectroscopy in the time domain. Apart from the expected discontinuity due to the α→ε structural and spin transitions, the IS decreases monotonically with increasing pressure. The absolute shifts were reproduced without semi-empirical calibrations by periodic density functional calculations employing extensive localized basis sets with several common density functionals. However, the best numerical agreement is obtained with the B1WC hybrid functional. Extension of the calculations to 350 GPa, a pressure corresponding to the Earth’s inner core, predicted the IS range of 0.00 to -0.85 mm/s, covering the span from Fe(0) to Fe(VI) compounds measured at ambient pressure. The calculations also reproduced the pressure trend from polymorphs of prototypical iron oxide minerals, FeO and Fe2O3. Analysis of the electronic structure shows a strong donation of electrons from oxygen to iron at high pressure. The assignment of formal oxidation to the Fe atom becomes ambiguous under this condition.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.
Materials science↗
Predictive Models for Semiconductor Device Design and Processing
The device feature size continues to be on a downward trend with a simultaneous upward trend in wafer size to 300 mm. Predictive models are needed more than ever before for this reason. At NASA Ames, a Device and Process Modeling effort has been initiated recently with a view to address these issues. Our activities cover sub-micron device physics, process and equipment modeling, computational chemistry and material science. This talk would outline these efforts and emphasize the interaction among various components. The device physics component is largely based on integrating quantum effects into device simulators. We have two parallel efforts, one based on a quantum mechanics approach and the second, a semiclassical hydrodynamics approach with quantum correction terms. Under the first approach, three different quantum simulators are being developed and compared: a nonequlibrium Green's function (NEGF) approach, Wigner function approach, and a density matrix approach. In this talk, results using various codes will be presented. Our process modeling work focuses primarily on epitaxy and etching using first-principles models coupling reactor level and wafer level features. For the latter, we are using a novel approach based on Level Set theory. Sample results from this effort will also be presented.
Meyyappan, Meyya↗
Transforming Energy through Computational Science: Computing for Clean Energy
This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.
advanced computing↗
High-temperature properties of gases
Computational chemistry is being applied at NASA Ames Research Center to a variety of problems in chemistry, physics, and materials sciences. Supercomputers and modern computational chemistry techniques have provided a powerful new tool to help fill NASA's continuing need for information about the properties of gases and materials as well as their interaction. For example, radiative transition probabilities, spectroscopic constants, bond dissociation energies, transport properties, and chemical reaction rates for molecules can be determined computationally just as reliably as by experiment. Recent results on the radiative transition probabilities of the strongly radiating systems of air species, based on state-of-the-art computational chemistry calculations, are presented and compared with experimental data.
Cooper, David M.↗
Ultra-fast interpretable machine-learning potentials
Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.
36 MATERIALS SCIENCE↗
High-Resolution Mapping of Photocatalytic Activity by Diffusion-Based and Tunneling Modes of Photo-Scanning Electrochemical Microscopy
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
pyGWBSE: a high throughput workflow package for GW-BSE calculations
Abstract We develop an open-source python workflow package, py GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. GW approximation accurately predicts bandgaps of materials by overcoming the bandgap underestimation issue of the more widely used density functional theory (DFT). BSE formalism produces absorption spectra directly comparable with experimental observations. py GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. py GWBSE is integrated with Wannier90 , to generate QP bandstructures, interpolated using the maximally-localized wannier functions. py GWBSE also enables the automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis.
36 MATERIALS SCIENCE↗
A rule-free workflow for the automated generation of databases from scientific literature
Abstract In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.
36 MATERIALS SCIENCE↗
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.
36 MATERIALS SCIENCE↗
Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets
Abstract We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.
36 MATERIALS SCIENCE↗
Tardigrade (NA-22 Quarterly Report)
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 24 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.
36 MATERIALS SCIENCE↗
The design space of E(3)-equivariant atom-centred interatomic potentials
Abstract Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.
Computer Science↗
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.
14 SOLAR ENERGY↗
Implementing the donor–acceptor approach in electronically conducting copolymers via electropolymerization
Electropolymerization has become a convenient method for synthesizing and characterizing complex organic copolymers having intrinsic electronic conductivity, including the donor (D)–acceptor (A) class of electronically conducting polymers (ECPs). This review begins with an introduction to the electrosynthesis of common second-generation ECPs. The information obtainable from electroanalytical studies, charge carriers such as polarons (positive and negative) and bipolarons (positive and negative) and doping will be discussed. The evolutionary chain of ECPs is then presented. ECPs comprising electron-rich D and electron-deficient A moieties have been shown to possess intrinsic electronic conductivity and unique optical and electronic properties. They are third generation ECPs and electropolymerization of mixtures of D and A leads to stoichiometrically controlled block copolymers. These D–A type ECPs are discussed on the basis of selected representative materials. Since the discovery of electropolymerization as a powerful tool to synthesize copolymers of conjugated monomers with a pre-determined ratio of D and A repeat units present in the polymer, the field of D–A type ECPs has grown considerably and the literature available since 2004 to 2021 is summarized and tabulated. Electronic and optical properties of the materials determined by computational chemistry are presented. The data obtained from electrochemical and optical methods are compared with those obtained from computational methods and reasons for discrepancies are given. The literature on the concept of electropolymerization extended to synthesizing triblock and many-block copolymers is reviewed. Finally, applications of D–A polymers in optoelectronic devices (organic solar cells and field-effect transistors) and in bio-imaging are explained quoting appropriate examples.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
GradDFT. A software library for machine learning enhanced density functional theory
Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy.
Chemistry↗