Communication: A novel method for generating molecular mixtures at extreme conditions: The case of hydrogen and oxygen
Abstract not provided
Pravica, Michael↗
SEARCH · Search NASA
Results for “molecular generation”
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.
Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders
In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.
97 MATHEMATICS AND COMPUTING↗
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Multi-objective latent space optimization of generative molecular design models
Not Available
97 MATHEMATICS AND COMPUTING↗
Deep generative molecular design reshapes drug discovery
Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.
59 BASIC BIOLOGICAL SCIENCES↗
Generating Molecular Rovibrational Coherence by Two-Photon Femtosecond Photoassociation of Thermally Hot Atoms
The formation of diatomic molecules with rotational and vibrational coherence is demonstrated experimentally in free-to-bound two-photon femtosecond photoassociation of hot atoms. In a thermal gas at a temperature of 1000 K, pairs of magnesium atoms, colliding in their electronic ground state, are excited into coherent superpositions of bound rovibrational levels in an electronically excited state. The rovibrational coherence is probed by a time-delayed third photon, resulting in quantum beats in the UV fluorescence. A comprehensive theoretical model based on ab initio calculations rationalizes the generation of coherence by Franck-Condon filtering of collision energies and partial waves, quantifying it in terms of an increase in quantum purity of the thermal ensemble. Our results open the way to coherent control of a binary reaction.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A novel method for generating molecular mixtures at extreme conditions: The case of fluorine and oxygen
Abstract not provided
Pravica, Michael↗
Artificial Photosynthesis with Next Generation Molecular Catalysts
The objective of this project was to develop a new class of molecular photocatalysts for CO 2 reduction that will enable breakthrough advances in artificial photosynthesis. A major limitation of existing photocatalytic assemblies is that only a small fraction of absorbed photons are converted into fuel due to inefficiencies in electron transport between the different system components. In the case of molecular catalysts for reduction of CO 2 , highly reducing electron equivalents are required for catalysis. As a result, the electron flux between the photosensitizer and CO 2 reduction catalyst is heavily biased towards the photosensitizer. One method to direct the electron flux towards productive photocatalysis is to design molecular catalysts for CO 2 reduction that operate at more favorable potentials than existing catalysts. The conventional approach is to tune the potential of the catalyst through selection of the supporting ligands, however, this only leads to incremental improvements due to the existence of an unfavorable scaling relationship between the rate and potential of catalysis. Our hypothesis is that the catalytic rate and potential can be simultaneously enhanced using bimetallic catalysts in which one metal improves the potential of catalysis and the second metal increases the rate of catalysis. During the course of this project, a few studies reported some success in improving photocatalytic CO 2 reduction with heterobimetallic complexes, demonstrating the potential of this approach to catalyst design.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Systems and methods for generating a molecular dynamic graded lattice structure and their application to additive manufacturing
Systems and methods for generating molecular dynamic graded lattice structures that can be used as infill for additively manufactured articles. Molecular dynamically generated lattice infill is based on force balancing a node distribution instead of a circle packing. Field data can be utilized to adjust the spacing of the node distribution according to a force balance equilibrium model that accounts for the field expected to be experienced by the article being additively manufactured. The resultant non-uniform honeycomb structures from force-balancing robustly and efficiently address the connection issues with traditional non-uniform lattice structures.
Kim, Seokpum↗
A comparison of probabilistic generative frameworks for molecular simulations
Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.
Artificial intelligence↗
Genarris 2.0: A Random Structure Generator for Molecular Crystals
Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.
Crystal structure prediction↗
Monitoring attosecond dynamics of coherent electron-nuclear wave packets by molecular high-order-harmonic generation
A pump-probe scheme for preparing and monitoring electron-nuclear motion in a dissociative coherent electron-nuclear wave packet is explored from numerical solutions of a non-Born-Oppenheimer time-dependent Schroedinger equation. A mid-ir intense few-cycle probe pulse is used to generate molecular high-order-harmonic generation (MHOHG) from a coherent superposition of two or more dissociative coherent electronic-nuclear wave packets, prepared by a femtosecond uv pump pulse. Varying the time delay between the intense ir probe pulse and the uv pump pulse by a few hundreds of attoseconds, the MHOHG signal intensity is shown to vary by orders of magnitude, thus showing the high sensitivity to electron-nuclear dynamics in coherent electron-nuclear wave packets. We relate this high sensitivity of MHOHG spectra to opposing electron velocities (fluxes) in the electron wave packets of the recombining (recolliding) ionized electron and of the bound electron in the initial coherent superposition of two electronic states.
74 ATOMIC AND MOLECULAR PHYSICS↗
CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field
Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 μs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. Additionally, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. Finally, the capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Impact of corrosion layer composition and thermal pretreatment on the radiolytic generation of molecular hydrogen from aluminum alloys
To fully evaluate the feasibility of extending the dry storage of aluminum-clad spent nuclear fuel (ASNF) in helium-backfilled cannisters, the amount of radiation-induced molecular hydrogen (H 2 ) generated and the impact of absorbed radiation dose on the ASNF corrosion layer composition must be accurately assessed. Here, in this study, we report a slowing in the rate of radiolytic H 2 generation for pre-corroded AA1100 and AA6061 aluminum alloy coupons irradiated to up to 53 MGy of absorbed cobalt-60 gamma dose. By exploring a variety of thermal pretreatment conditions for AA6061 coupons, we find that the “steady-state” yield of H 2 depends more on the aluminum alloy used than on the treatments applied prior to dry storage. Scanning electron microscopy and positron annihilation lifetime spectroscopy techniques also provided evidence for gamma radiation-induced defects in the corrosion layers of the investigated aluminum alloy coupons for high absorbed doses (~50 MGy), the consequences of which on cladding integrity and H 2 generation should be explored in future works.
38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
Unraveling the Spectral Signatures of Solvent Ordering in K-edge XANES of Aqueous Na +
The aqueous solvation structure of the Na + ion is studied both by molecular dynamics (MD) simulations and K-edge X-ray absorption near edge spectroscopy (XANES). In this paper, we present a systematic study contrasting the differences in the predicted XANES spectra, using molecular configurations generated from classical and quantum MD. Using a set of suitable order parameters, we elucidate how the spectroscopic features are influenced by the local solvation structure around the Na + ion. Lastly, our findings suggest that XANES is sensitive to fluctuations between the first and second solvation shells and can distinguish between classical and quantum interaction representations used to generate molecular ensembles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Fine- and hyperfine-structure effects in molecular photoionization. II. Resonance-enhanced multiphoton ionization and hyperfine-selective generation of molecular cations
Resonance-enhanced multiphoton ionization (REMPI) is a widely used technique for studying molecular photoionization and producing molecular cations for spectroscopy and dynamics studies. Here, we present a model for describing hyperfine-structure effects in the REMPI process and for predicting hyperfine populations in molecular ions produced by this method. This model is a generalization of our model for fine- and hyperfine-structure effects in one-photon ionization of molecules presented in Paper I [M. Germann and S. Willitsch, J. Chem. Phys. 145, 044314 (2016)]. This generalization is achieved by covering two main aspects: (1) treatment of the neutral bound-bound transition including the hyperfine structure that makes up the first step of the REMPI process and (2) modification of our ionization model to account for anisotropic populations resulting from this first excitation step. Our findings may be used for analyzing results from experiments with molecular ions produced by REMPI and may serve as a theoretical background for hyperfine-selective ionization experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Monte Carlo Simulation of Borosilicate Glass Dissolution using Molecular Dynamics-Generated Glass Structures
Atomic-scale insights into the dissolution mechanisms of borosilicate glasses are critical to the development of the waste form corrosion models that inform performance assessments of nuclear waste disposal facilities. Monte Carlo (MC) simulations have been instrumental in advancing the state of knowledge of borosilicate glass dissolution; however, all such simulations to date have relied on lattices to represent the amorphous glass structure, thereby potentially introducing artifacts. Therefore, a new approach, referred to as the amorphous MC approach, has been developed whereby glass structures generated from molecular dynamics simulations are used as starting points for MC simulations of glass dissolution. MC simulations of sodium borosilicate glasses that covered a wide compositional range were performed and revealed correlations between dissolution rate and structural features of the glasses. Notably, the lattice MC approach predicted the fraction of tetrahedral boron to have a lesser influence on the dissolution rate than computed with the amorphous MC approach. The lattice MC simulations also overestimated the dissolution rate for a given glass composition. These findings highlight the importance of using truly amorphous structures in MC simulations of glass corrosion.
Kerisit, Sebastien N.↗