scholarly journals Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

scholarly journals Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

scholarly journals Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps

2021 ◽  
Vol 7 (33) ◽  
pp. eabi4763
Author(s):  
Edward O. Pyzer-Knapp ◽  
Linjiang Chen ◽  
Graeme M. Day ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Porous Solids ◽  
Energy Structure ◽  
Processing Unit ◽  
Central Processing ◽  
Property Data ◽  
Computational Discovery ◽  
The Cost

While energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving more than 500,000 central processing unit hours from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultrahigh-throughput screening pipelines by greatly reducing the opportunity risk associated with the choice of system to calculate.

scholarly journals Crystal structure prediction by combining graph network and Bayesian optimization

2021 ◽  
Author(s):  
Guanjian Cheng ◽  
Xin-Gao Gong ◽  
Wan-Jian Yin
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Density Functional ◽  
Computational Cost ◽  
Formation Enthalpy ◽  
Bayesian Optimization ◽  
Chemical Compositions ◽  
Crystal Structure Prediction ◽  
Cell Shapes

Abstract We developed a density functional theory (DFT)-free approach for crystal structure prediction, in which a graph network (GN) is adopted to establish a correlation model between the crystal structure and formation enthalpies, and Bayesian optimization (BO) is used to accelerate the search for crystal structure with optimal formation enthalpy. The approach of combining GN and BO for crystal structure searching (GN-BOSS) can predict crystal structures at given chemical compositions with and without additional constraints on cell shapes and lattice symmetries. The applicability and efficiency of the GN-BOSS approach is then verified by solving the classical Ph-vV challenge. The approach can accurately predict the crystal structures with a computational cost that is three orders of magnitude less than that required for DFT-based approaches. The GN-BOSS approach may open new avenues for data-driven crystal structural predictions without using expensive DFT calculations.

scholarly journals Predicted energy–structure–function maps for the evaluation of small molecule organic semiconductors

2017 ◽  
Vol 5 (30) ◽  
pp. 7574-7584 ◽  
Author(s):  
Josh E. Campbell ◽  
Jack Yang ◽  
Graeme M. Day
Keyword(s):  
Crystal Structure ◽  
Structure Function ◽  
Organic Semiconductors ◽  
Small Molecule ◽  
Structure Prediction ◽  
Energy Structure ◽  
Crystal Structure Prediction

Crystal structure prediction is used to calculate energy–structure–function maps of the charge mobilities in molecular organic semiconductors.

scholarly journals Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development

2021 ◽  
Vol 26 (6) ◽  
pp. 579-590
Author(s):  
Sam Elder ◽  
Carleen Klumpp-Thomas ◽  
Adam Yasgar ◽  
Jameson Travers ◽  
Shayne Frebert ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Assay Development ◽  
Bayesian Optimization ◽  
Biological Assay ◽  
Proof Of Concept ◽  
Screening Assay ◽  
High Throughput Screening Assay ◽  
Cross Platform ◽  
Assay Conditions

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions. Graphical Abstract

SoftBV – a software tool for screening the materials genome of inorganic fast ion conductors

2019 ◽  
Vol 75 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Haomin Chen ◽  
Lee Loong Wong ◽  
Stefan Adams
Keyword(s):  
Force Field ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Computational Cost ◽  
Lewis Base ◽  
Computational Screening ◽  
Wide Range ◽  
Dft Simulations

The identification of materials for advanced energy-storage systems is still mostly based on experimental trial and error. Increasingly, computational tools are sought to accelerate materials discovery by computational predictions. Here are introduced a set of computationally inexpensive software tools that exploit the bond-valence-based empirical force field previously developed by the authors to enable high-throughput computational screening of experimental or simulated crystal-structure models of battery materials predicting a variety of properties of technological relevance, including a structure plausibility check, surface energies, an inventory of equilibrium and interstitial sites, the topology of ion-migration paths in between those sites, the respective migration barriers and the site-specific attempt frequencies. All of these can be predicted from CIF files of structure models at a minute fraction of the computational cost of density functional theory (DFT) simulations, and with the added advantage that all the relevant pathway segments are analysed instead of arbitrarily predetermined paths. The capabilities and limitations of the approach are evaluated for a wide range of ion-conducting solids. An integrated simple kinetic Monte Carlo simulation provides rough (but less reliable) predictions of the absolute conductivity at a given temperature. The automated adaptation of the force field to the composition and charge distribution in the simulated material allows for a high transferability of the force field within a wide range of Lewis acid–Lewis base-type ionic inorganic compounds as necessary for high-throughput screening. While the transferability and precision will not reach the same levels as in DFT simulations, the fact that the computational cost is several orders of magnitude lower allows the application of the approach not only to pre-screen databases of simple structure prototypes but also to structure models of complex disordered or amorphous phases, and provides a path to expand the analysis to charge transfer across interfaces that would be difficult to cover by ab initio methods.

scholarly journals Accurate and efficient representation of intramolecular energy inab initiogeneration of crystal structures. I. Adaptive local approximate models

2016 ◽  
Vol 72 (6) ◽  
pp. 864-874 ◽  
Author(s):  
Isaac Sugden ◽  
Claire S. Adjiman ◽  
Constantinos C. Pantelides
Keyword(s):  
Structure Prediction ◽  
Computational Cost ◽  
Search Space ◽  
Approximate Model ◽  
High Energy ◽  
Computational Effort ◽  
Crystal Structure Prediction ◽  
Blind Test ◽  
Acta Cryst ◽  
Simplifying Assumptions

The global search stage of crystal structure prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in theCrystalPredictor II[Habgoodet al.(2015).J. Chem. Theory Comput.11, 1957–1969] program is presented, where the most efficient use of computational effort is ensuredviathe use of adaptive local approximate model (LAM) placement. The entire search space of the relevant molecule's conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determinedviaan automated process, aiming to minimize the computational effort expended in high-energy regions whilst maximizing the accuracy in low-energy regions. As the size, complexity and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule (XXVI) from the sixth blind test [Reillyet al.(2016).Acta Cryst.B72, 439–459], which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.

scholarly journals Hexagonal Ti2B2 monolayer: a promising anode material offering high rate capability for Li-ion and Na-ion batteries

2018 ◽  
Vol 20 (34) ◽  
pp. 22168-22178 ◽  
Author(s):  
Tao Bo ◽  
Peng-Fei Liu ◽  
Juping Xu ◽  
Junrong Zhang ◽  
Yuanbo Chen ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Density Functional ◽  
Density Functional Method ◽  
Rate Capability ◽  
Functional Method ◽  
High Rate ◽  
Crystal Structure Prediction ◽  
Metal Borides ◽  
Li Ion ◽  
Prediction Techniques

Combining the first-principles density functional method and crystal structure prediction techniques, we report a series of hexagonal two-dimensional transition metal borides including Sc2B2, Ti2B2, V2B2, Cr2B2, Y2B2, Zr2B2, and Mo2B2.

Evolution inspired engineering of antibiotic biosynthesis enzymes

2017 ◽  
Vol 15 (19) ◽  
pp. 4036-4041 ◽  
Author(s):  
M. Metsä-Ketelä
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Antibiotic Biosynthesis ◽  
Screening Systems ◽  
Proof Of Principle

Chimeragenesis is an effective tool to probe the structure/function relationships of proteins without high-throughput screening systems. Here the proof-of-principle is presented with three pairs of proteins.

Sign in / Sign up

Export Citation Format

Share Document