Density functional theory predictions of the mechanical properties of crystalline materials

Kiely, Evan; Zwane, Reabetswe; Fox, Robert B.; Reilly, Anthony M.; Guerin, Sarah

doi:10.1039/d1ce00453k

Cited by 66 publications

(48 citation statements)

References 141 publications

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“…Kiely et al [8] studied the utilization of DFT for mechanical property prediction of crystalline materials. They have demonstrated obtaining elastic properties of electroactive materials using the VASP (Vienna ab initio Simulation Package) software with using the plane wave basis set where, solution to many body SH equation is approximated in a form of a plane wave [17].…”

Section: Mehanical Property Prediction Using Dftmentioning

confidence: 99%

“…However, for more complicated systems, specifically many body systems which involve multiple electrons, solving SH equation is nearly impossible. Therefore, the goal of DFT is to approximate a solution to the many body SH equation, finally providing the ground state energy of the system [8]. Hohenburg and Kohn (HK) theorem can be considered as the main foundation for DFT [7].…”

Section: Introductionmentioning

confidence: 99%

“…HK theorem states that the ground state energy of many electron system is a functional of local electron density. And also, contributions to the total energy of the system which are, ion-electron potential energy, ion-ion potential energy, electron-electron potential energy, kinetic energy and exchangecorrelation energy can be given based on electron density [7], [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review

2022

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In this paper, utilization of density functional theory (DFT) to obtain mechanical, electrical and thermal properties of crystalline materials are reviewed. DFT has resulted as an efficient tool for predicting ground states of many body systems thus aiding in resolving dispersion spectrums of complex atomic arrangements where solution by traditional Schr dinger (SH) equation is infeasible. Great success has been reported by previous researchers on utilizing DFT for functional property predictions of crystalline solids.

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Section: Mehanical Property Prediction Using Dftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review

2022

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“…Materials Acceleration Platforms (MAP) [ 19 ] are an emerging paradigm to accelerate the discovery of materials and especially functional nanomaterials. The reduced timeline of accelerated nanomaterial development is facilitated by the convergence of High-Throughput Computational (HTC) screening [ 53 , 54 , 55 , 56 ] with improved computations using ab initio codes for simulations [ 57 , 58 ] of electronic structure, materials properties predictions [ 59 , 60 , 61 ], automation [ 62 ], and robotic [ 63 , 64 , 65 ] systems for chemistry laboratories, using scientific AI in materials science [ 66 ] and the diversity of machine learning methods adapted to material science and areas of physics and chemistry.…”

Section: Brief Review Of Nanomaterials Discovery Approachesmentioning

confidence: 99%

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

et al. 2021

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Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

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“…Addressing the first challenge, elastic tensor analysis is a computational tool to quantify API stability during process implementation, utilized to study the interactions between APIs, excipients and co-formers, as well as the interactions between crystalline materials [13,14]. Regarding the second challenge, the drug nanoparticle core-shell was recently found to exist surrounded by a pseudo-and/or semi-solid phase structured by stabilizer and API placements, remaining in equilibrium with the solvent phase [15].…”

Section: Introductionmentioning

confidence: 99%

Integrating Elastic Tensor and PC-SAFT Modeling with Systems-Based Pharma 4.0 Simulation, to Predict Process Operations and Product Specifications of Ternary Nanocrystalline Suspensions

Davidopoulou

Kachrimanis

2021

Pharmaceutics

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Comminution of BCS II APIs below the 1 μm threshold followed by solidification of the obtained nanosuspensions improves their dissolution properties. The breakage process reveals new crystal faces, thus creating altered crystal habits of improved wettability, facilitated by the adsorption of stabilizing polymers. However, process-induced transformations remain unpredictable, mirroring the current limitations of our atomistic level of understanding. Moreover, conventional equations of estimating dissolution, such as Noyes–Whitney and Nernst–Brunner, are not suitable to quantify the solubility enhancement due to the nanoparticle formation; hence, neither the complex stabilizer contribution nor the adsorption influence on the interfacial tension occurring between the water and APIs is accounted for. For such ternary mixtures, no numeric method exists to correlate the mechanical properties with the interfacial energy, capable of informing the key process parameters and the thermodynamic stability assessment of nanosuspensions. In this work, an elastic tensor analysis was performed to quantify the API stability during process implementation. Moreover, a novel thermodynamic model, described by the stabilizer-coated nanoparticle Gibbs energy anisotropic minimization, was structured to predict the material’s system solubility quantified by the application of PC-SAFT modeling. Comprehensively merging elastic tensor and PC-SAFT analysis into the systems-based Pharma 4.0 algorithm provided a validated, multi-level, built-in method capable of predicting the critical material quality attributes and corresponding key process parameters.

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Density functional theory predictions of the mechanical properties of crystalline materials

Abstract: The DFT-predicted mechanical properties of crystalline materials are crucial knowledge for their screening, design, and exploitation.

Cited by 66 publications

References 141 publications

Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review

Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Integrating Elastic Tensor and PC-SAFT Modeling with Systems-Based Pharma 4.0 Simulation, to Predict Process Operations and Product Specifications of Ternary Nanocrystalline Suspensions

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