A software framework for data dimensionality reduction: application to chemical crystallography

Materials science research has witnessed an increasing use of data mining techniques in establishing process‐structure‐property relationships. Significant advances in high‐throughput experiments and computational capability have resulted in the generation of huge amounts of data. Various statistical methods are currently employed to reduce the noise, redundancy, and the dimensionality of the data to make analysis more tractable. Popular methods for reduction (like principal component analysis) assume a linear relationship between the input and output variables. Recent developments in non‐linear reduction (neural networks, self‐organizing maps), though successful, have computational issues associated with convergence and scalability. Another significant barrier to use dimensionality reduction techniques in materials science is the lack of ease of use owing to their complex mathematical formulations. This paper reviews various spectral‐based techniques that efficiently unravel linear and non‐linear structures in the data which can subsequently be used to tractably investigate process‐structure‐property relationships. In addition, we describe techniques (based on graph‐theoretic analysis) to estimate the optimal dimensionality of the low‐dimensional parametric representation. We show how these techniques can be packaged into a modular, computationally scalable software framework with a graphical user interface ‐ Scalable Extensible Toolkit for Dimensionality Reduction (SETDiR). This interface helps to separate out the mathematics and computational aspects from the materials science applications, thus significantly enhancing utility to the materials science community. The applicability of this framework in constructing reduced order models of complicated materials dataset is illustrated with an example dataset of apatites described in structural descriptor space. Cluster analysis of the low‐dimensional plots yielded interesting insights into the correlation between several structural descriptors like ionic radius and covalence with characteristic properties like apatite stability. This information is crucial as it can promote the use of apatite materials as a potential host system for immobilizing toxic elements

A universal interatomic potential for perovskite oxides

With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parameterized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parameterized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental phase transition sequences for several markedly different ferroelectric oxides, including a 6-element ternary solid solution, Pb(In$_{1/2}$Nb$_{1/2}$)O$_3$--Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$--PbTiO$_3$. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD.Comment: 18 pages, 4 figure

Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner

Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexity with respect to system sizes leads to prohibitive computational costs when the simulation contains over a few hundred atoms in practice. We present an In-Distribution substructure Embedding Active Learner (IDEAL) to enable efficient simulation of large complex systems with quantum accuracy by maintaining a machine learning force field (MLFF) as an accurate surrogate to the first principles methods. By extracting high-uncertainty substructures into low-uncertainty atom environments, the active learner is allowed to concentrate on and learn from small substructures of interest rather than carrying out intractable quantum chemical computations on large structures. IDEAL is benchmarked on various systems and shows sub-linear complexity, accelerating the simulation thousands of times compared with conventional active learning and millions of times compared with pure first principles simulations. To demonstrate the capability of IDEAL in practical applications, we simulated a polycrystalline lithium system composed of one million atoms and the full ammonia formation process in a Haber-Bosch reaction on a 3-nm Iridium nanoparticle catalyst on a computing node comprising one single A100 GPU and 24 CPU cores

Leveraging Language Representation for Material Recommendation, Ranking, and Exploration

Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. While there is enormous progress towards learning the structure to property relationship of materials, methods that allow for general representations of crystals to effectively explore the vast material search space and identify high-performance candidates remain limited. In this work, we introduce a material discovery framework that uses natural language embeddings derived from material science-specific language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that, given a query material, first recalls candidates based on representational similarity, and ranks the candidates based on target properties through multi-task learning. The contextual knowledge encoded in language representations is found to convey information about material properties and structures, enabling both similarity analysis for recall, and multi-task learning to share information for related properties. By applying the discovery framework to thermoelectric materials, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces, including halide perovskite, delafossite-like, and spinel-like structures. By leveraging material language representations, our framework provides a generalized means for effective material recommendation, which is task-agnostic and can be applied to various material systems

Thermal conductivity enhancement of graphene polymer composites through edge functionalization and expansion of graphite

In this work, we report an ultra-high enhancement of 4030% in thermal conductivity of polyetherimide/graphene nanocomposite (k = 9.5 Wm-1K-1) prepared through the use of expanded graphite (EG) with hydrogen peroxide as an intercalating agent at 10 weights% composition (k of pure polyetherimide ~ 0.23 Wm-1K-1). This value represents the highest thermal conductivity ever measured in a polymer composite at this low filler loading and is more than a factor of 2 higher relative to earlier reported results. This ultra-high thermal conductivity value is found to be due to an expanded graphite mediated interconnected graphene network throughout the composite, establishing a percolative environment that enables highly efficient thermal transport in the composite. Comparative studies were also performed using sodium chlorate as an intercalating agent. At 10 wt% composition, sodium chlorate intercalated expanded graphite was found to lead to a smaller enhancement of 2190% in k of composite. These results highlight the distinct advantage of hydrogen peroxide as an intercalating agent in enhancing thermal conductivity. Detailed characterization performed to elucidate this advantage, revealed that hydrogen peroxide led to primarily edge oxidation of graphene sheets within expanded graphite, leaving the basal plane intact, thus preserving the ultra-high in-plane thermal conductivity of ~ 2000 Wm-1K-1. Sodium chlorate, on the other hand, led to a higher degree of oxidation, with a large number of oxygen groups on basal plane of graphene, dramatically lowering its in-plane thermal conductivity. To directly shed light on the effect of intercalating agents on thermal conductivity of graphene itself, we prepared expanded graphite paper by compressing expanded graphite particles together. Thermal diffusivity of hydrogen-peroxide prepared expanded graphite paper was measured to be 9.5 mm2/s while that of sodium chlorate case measured to be 6.7 mm2/s, thus directly confirming the beneficial impact of hydrogen peroxide on k of graphene itself. This study is the first to address the role of intercalating agents on k of expanded graphite/polymer composites and has led to the discovery of hydrogen peroxide as an effective intercalating agent for achieving ultra-high thermal conductivity values. The work is also the first to address the comparison between edge and basal plane functionalization of graphene for enhancement of k of graphene-nanoplatelet /polyetherimide (GnP/PEI) composites. Graphene nanoplatelets (GnPs) comprise of multiple layers of graphene stacked parallel to each other. Edge functionalization enables the advantage of coupling the edges of all sheets of GnP with the embedding polymer, thus enabling the entire nanoplatelet to efficiently conduct heat through the composite. Basal-plane functionalization only couples the outermost layers of GnP with the polymer, thus causing only part of the nanoplatelet to be effective in conducting heat. Another very important advantage of edge-functionalization lies in leaving the basal plane of graphene intact. This preserves the ultra-high in-plane k of graphene (k~ 2000 Wm-1K-1). Basal plane functionalization, on the other hand, introduces a large number of defects in the basal plane of graphene dramatically lowering its intrinsic k value. Molecular dynamics simulations have revealed that even 5% functionalization of the basal plane can lower graphene thermal conductivity by as much as 90%. In this work, we experimentally realized the outlined advantages of edge-functionalization on the enhancement of k. Edge functionalization was achieved by oxidizing graphene with an excess of carboxyl groups through use of sulfuric acid, sodium chlorate and hydrogen peroxide. Carboxyl groups are known to preferentially attach to edges of graphene leading to edge oxidation. Basal plane oxidation was achieved through Hummer’s method by using sulfuric acid and potassium permanganate. Measurements reveal edge-oxidized graphene to enhance composite k by 18%, while basal-plane oxidized graphene reduced composite k by 57% at 10 wt% composition, clearly outlining the advantage of edge-functionalization on enhancement of thermal conductivity. Detailed characterization was performed to confirm edge versus basal plane oxidation. X-ray photoelectron spectroscopy showed greater fraction of carboxyl groups in edge-oxidized graphene, while basal plane oxidized graphene had larger fraction of hydroxyl/epoxy oxygen groups. 2D Raman mapping was used to obtain ID/IG ratios separately on edge and basal plane of GnPs. Edge oxidized graphene demonstrated higher ID/IG ratio on edge, while basal plane oxidized graphene demonstrated higher ID/IG ratio on basal plane. These studies for the first time, comprehensively demonstrate that edge functionalization can lead to superior thermal conductivity enhancement. Unique breakthroughs outlined in this thesis will lead to promising new avenues to achieve next-generation ultra-high thermal conductivity polymer-graphene nanocomposites

Intermetallic compounds in heterogeneous catalysis - a quickly developing field

The application of intermetallic compounds for understanding in heterogeneous catalysis developed in an excellent way during the last decade. This review provides an overview of concepts and developments revealing the potential of intermetallic compounds in fundamental as well as applied catalysis research. Intermetallic compounds may be considered as platform materials to address current and future catalytic challenges, e.g. in respect to the energy transition

Discovering the cover: molecular imaging of Populus trichocarpa leaf surface by FT-IR spectroscopy and mass spectrometry techniques

All terrestrial plants are covered by cuticle and its most outer layer is called epicuticular waxes (EWs). This layer forms an actual leaf surface, therefore is a first line of defence against any environmental stress. Despite its crucial role in plant survival, for decades leaf surface was studied without required selectivity. In the present research, the leaf surface was investigated using selective sampling methods and molecular imaging tools: (1) MALDI-TOF-MS (matrix assisted laser/desorption ionization), (2) TOF-SIMS (time-of flight secondary ion) mass spectrometry imaging, (3) FT-IR (Fourier transform infrared) as well as (4) Raman spectroscopy imaging. These tools provide molecular specificity and spatially resolved information. The results were complemented with GC-MS, SEM (scanning electron microscopy), behavioral experiments and statistical analysis. The first sequenced tree, Populus trichocarpa, along with the leaf beetle Chrysomela populi were chosen as a model system. This system represents naturally occurring interaction between a specialist herbivore and its host plant. Following aspects were investigated: (1) Characterization of structure and chemical composition of leaf surface (2) Investigation of role of EW layer in the host recognition process (3) Analysis of wound-healing processes on the leaf surface in response to insect infestation (4) Distribution of leaf surface constituents with high resolution imaging techniques. Results allow to conclude, that: (1) Higly non polar aliphatic compounds detected on the leaf surface of P. trichocarpa play protective role rather than informative (2) EW layer lack compounds that would be necessary in the host recognition process (3) EWs are involved in early stage wound-healing process by their deposition on the injury area (4) Leaf surface compounds co-aggregate and form two distribution patterns, possibly transport of EW constituents depends on their chain length

Grafting luminescent metal-organic species into mesoporous MCM-41 silica from europium(III)tetramethylheptanedionate, Eu(thd)3

Mixed systems with Eu(III) β-diketonates as optically active guest species, and mesoporous silicas MCM-41 as a host matrix have been investigated. The grafting of europium(III) onto the inner walls of unmodified MCM-41 has been achieved starting from Eu(thd)3 (thd = 2,2,6,6- tetramethyl-3,5-heptanedionate), using two routes: wet impregnation (WI) at room temperature,and chemical vapour infiltration (CVI) at 185 °C. In received hybrids, denoted Eu(thd)x@MCM- 41, the same maximum yield [Eu]/[Si] = 8.2 at% on average has been achieved with either methods. The molar ratio x = [thd]/[Eu] is 0.6 on average for WI samples, and 1.5 for CVI samples. In the latter, higher contents in thd compensate lower contents in silanols with respect to the former. Rationalizing the possible bonds exchanged at the silica surface leads to a great diversity of possible co-ordination schemes according to the expression Σ[Si(OH)nx (O)xEu(thd)3-x] (where Σ means that surface species are considered). Chromophore neutral ligands phenanthroline (phen) or bipyridine (bipy) have been added to induce efficient Eu3+ luminescence under 270–280 nm excitation, via the antenna effect. For the most favourable case, (phen)yEu(thd)x@MCM-41, the emission intensity at 612 nm under excitation at 270 nm is 2/3 that for the genuine heteroleptic complex Eu(thd)3(phen). Moreover the hybrid material is stable up to 440 °C