Rational design of all organic polymer dielectrics

Abstract

To date, trial and error strategies guided by intuition have dominated the identification of materials suitable for a specific application. We are entering a data-rich, modelling-driven era where such Edisonian approaches are gradually being replaced by rational strategies, which couple predictions from advanced computational screening with targeted experimental synthesis and validation. Here, consistent with this emerging paradigm, we propose a strategy of hierarchical modelling with successive downselection stages to accelerate the identification of polymer dielectrics that have the potential to surpass 'standard' materials for a given application. Successful synthesis and testing of some of the most promising identified polymers and the measured attractive dielectric properties (which are in quantitative agreement with predictions) strongly supports the proposed approach to material selection.

Accelerated materials property predictions and design using motif-based fingerprints

“…Four types of fingerprints, namely f (0) , f (1) , f (2) , and f (3) , are discussed in the following subsections.…”

“…In the 3 rd -order fingerprint f (3) , the number of twobond catenation is represented, i.e., κ ≡ Ai-Bj-Ck. In particular, the definition (1) for f (3) κ≡Ai-Bj-Ck involves n Ai-Bj-Ck , which is the number of Ai-Bj-Ck sequences, or equivalently, the catenation of two bonds Ai-Bj and Bj-Ck.…”

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] This emerging trend is fueled by the availability and emergence of large materials databases, [16][17][18] as well as our ability to progressively accumulate materials data via high-throughput computations 19,20 and experiments. [16][17][18] Data-driven strategies aimed at rapid property predictions, and ultimately at rational or informed materials design, rely on exploiting the information content of past data, and using such information within heuristic or statistical interpolative learning models to provide estimates of properties of a new material.…”

Finding New Perovskite Halides via Machine Learning

“…A steady increase in computational power, accompanied by developments in quantum theory and algorithmic breakthroughs that allow for efficient yet accurate quantum mechanical computations, opens the door to computing properties of a wide range of materials that once seemed prohibitively expensive. As a result, high-throughput explorations of the vast chemical space are increasingly being pursued and have significantly aided our intuition and knowledge-base of material properties (Ceder et al, 2011;Jain et al, 2011;Yu and Zunger, 2012;Curtarolo et al, 2013;Pilania et al, 2013Pilania et al, , 2016Sharma et al, 2014;Balachandran et al, 2016;Kim et al, 2016;Mannodi-Kanakkithodi et al, 2016). Massive open source databases of materials properties (including electronic structure, thermodynamic, and structural properties) are now available on the web (Curtarolo et al, 2012;Computational Materials Repository, 2015;Materials Project -A Materials Genome Approach, 2015).…”

Broadband Dielectric Spectroscopic Characterization of Thermal Stability of Low-k Dielectric Thin Films for Micro- and Nanoelectronic Applications

“…12 Historically, the matching of low-k materials to applications has been guided by engineering information and intuition. 13 Since the electrical performance of the materials is the key issue in the evaluation of candidate low-k materials, 4 it is a common industry practice to screen different dielectric materials by their area-normalized capacitance (i.e., capacitance density), using techniques such as impedance measurements on metal-insulator-metal (MIM) capacitors, interline capacitance, dielectric strength, and breakdown voltage. These extrinsic measurement quantities, which relate to the dielectric constant (k) and the thickness of the dielectric material, are evaluated assuming that the test devices are ideal, 14 and that any statistical variation is a consequence of the limited technological process control.…”