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Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.

The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and machine learning (ML) methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps., Comment: 31 pages, 9 figures

The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the \emph{sunk cost fallacy} by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for a number of carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can actually increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters.

Mixed Reality (MR) is becoming ubiquitous as it finds its applications in education, healthcare, and other sectors beyond leisure. While MR end devices, such as headsets, have low energy intensity, the total number of devices and resource requirements of the entire MR ecosystem, which includes cloud and edge endpoints, can be significant. The resulting operational and embodied carbon footprint of MR has led to concerns about its environmental implications. Recent research has explored reducing the carbon footprint of MR devices by exploring hardware design space or network optimizations. However, many additional avenues for enhancing MR's sustainability remain open, including energy savings in non-processor components and carbon-aware optimizations in collaborative MR ecosystems. In this paper, we aim to identify key challenges, existing solutions, and promising research directions for improving MR sustainability. We explore adjacent fields of embedded and mobile computing systems for insights and outline MR-specific problems requiring new solutions. We identify the challenges that must be tackled to enable researchers, developers, and users to avail themselves of these opportunities in collaborative MR systems., Comment: IEEE International Symposium on the Emerging Metaverse (ISEMV)

The discovery of materials that exhibit a metal-insulator transition (MIT) is key to the development of multiple types of novel efficient microelectronic and optoelectronic devices. However, identifying MIT materials is challenging due to a combination of high computational cost of electronic structure calculations needed to understand their mechanism, the mechanisms' complexity, and the labor-intensive experimental validation process. To that end, we use a machine learning classification model to rapidly screen a high-throughput crystal structure database to identify candidate compounds exhibiting thermally-driven MITs. We focus on three candidate oxides, Ca$_2$Fe$_3$O$_8$, CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and identify their MIT mechanism using high-fidelity density functional theory calculations. Then, we provide a probabilistic estimate of which synthesis reactions may lead to their realization. Our approach couples physics-informed machine learning, density functional theory calculations, and machine learning-suggested synthesis to reduce the time to discovery and synthesis of new technologically relevant materials.

This paper introduces MatKG, a novel graph database of key concepts in material science spanning the traditional material-structure-property-processing paradigm. MatKG is autonomously generated through transformer-based, large language models and generates pseudo ontological schema through statistical co-occurrence mapping. At present, MatKG contains over 2 million unique relationship triples derived from 80,000 entities. This allows the curated analysis, querying, and visualization of materials knowledge at unique resolution and scale. Further, Knowledge Graph Embedding models are used to learn embedding representations of nodes in the graph which are used for downstream tasks such as link prediction and entity disambiguation. MatKG allows the rapid dissemination and assimilation of data when used as a knowledge base, while enabling the discovery of new relations when trained as an embedding model., Comment: Spotlighted paper for AI4Mat workshop in NeurIPS 2022

A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this work, we propose a reinforcement learning (RL) approach to inverse inorganic materials design, which can identify promising compounds with specified properties and synthesizability constraints. Our model learns chemical guidelines such as charge and electronegativity neutrality while maintaining chemical diversity and uniqueness. We demonstrate a multi-objective RL approach, which can generate novel compounds with targeted materials properties including formation energy and bulk/shear modulus alongside a lower sintering temperature synthesis objectives. Using this approach, the model can predict promising compounds of interest, while suggesting an optimized chemical design space for inorganic materials discovery., Comment: NeurIPS AI4Mat Workshop 2022

Density is one of the most commonly measured or estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science and sustainable cements. Here, two types of machine learning (ML) models (i.e., random forest (RF) and artificial neural network (ANN)) have been developed to predict the room-temperature density of glasses in the compositional space of CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O-MnO (CMASTFNKM), based on ~2100 data points mined from ~140 literature studies. The results show that the RF and ANN models give accurate predictions of glass density with R2 values, RMSE, and MAPE of ~0.96-0.98, ~0.02-0.03 g/cm3 and ~0.59-0.79%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ~0.28-0.91, ~0.05-0.15 g/cm3, and ~1.40-4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO-Al2O3-SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ~0.90) with the average metal-oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density-composition relationships from these models (for selected compositional subspaces) suggests that the ANN model exhibits a certain level of transferability (i.e., ability to extrapolate to compositional space not (or less) covered in the database) and captures known features including the mixed alkaline earth effects for (CaO-MgO)0.5-(Al2O3-SiO2)0.5 glasses., Comment: 41 pages, 10 figures, 6 Tables

Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration., Comment: NLP+HCI Workshop at NAACL 2022

Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by chemists' prior knowledge and experience, built upon experimental trial-and-error that is both time and resource consuming. Recent developments in natural language processing (NLP) have enabled large-scale text mining of scientific literature, providing open source databases of synthesis information of synthesized compounds, material precursors, and reaction conditions (temperatures, times). In this work, we employ a conditional variational autoencoder (CVAE) to predict suitable inorganic reaction conditions for the crucial inorganic synthesis steps of calcination and sintering. We find that the CVAE model is capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route (solid-state, sol-gel) that are present in the inorganic synthesis space. Moreover, the CVAE can generalize well to unseen chemical entities and shows promise for predicting reaction conditions for previously unsynthesized compounds of interest., Comment: Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)

Establishing the composition-structure-property relationships for amorphous materials is critical for many important natural and engineering processes, including the dissolution of highly complex volcanic glasses. In this investigation, we performed force field molecular dynamics (MD) simulations to generate detailed structural representations for ten natural CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O glasses with compositions ranging from rhyolitic to basaltic. Based on the resulting atomic structural representations at 300 K, we have calculated the partial radial distribution functions, nearest interatomic distances and coordination number, which are consistent with the literature data on silicate-based glasses. Based on these structural attributes and classical bond valence models, we have introduced a novel structural descriptor, i.e., average metal-oxygen (M-O) bond strength parameter, which has captured the log dissolution rates of the ten glasses at both acidic and basic conditions (based on literature data) with R2 values of ~0.80-0.92 based on linear regression. This structural descriptor is seen to outperform several other structural descriptors also derived from MD simulation results, including the average metal oxide dissociation energy, the average self-diffusion coefficient of all the atoms at their melting points, and the energy barrier of self-diffusion. Furthermore, we showed that the MD-derived descriptors generally exhibit better predictive performance than the degree of depolymerization parameter commonly used to describe glass and mineral reactivity. The results suggest that the structural descriptors derived from MD simulations, especially the average M-O bond strength parameter, are promising structural descriptors for connecting composition with dissolution rates of highly complex natural glasses., Comment: 49 pages, 13 figures

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely on featurization of materials composition, however, whether the exclusive use of structural knowledge in such models has the capacity to make comparable predictions remains unknown. Here we employ a deep neural network model to decode structure-property relationships in crystalline materials without explicitly considering chemical compositions. The focus is on classification of crystal systems, mechanical elasticity, electronic band gap, and phase stability. Our model utilizes a three-dimensional (3D) momentum space representation of structure from elastic x-ray scattering theory that exhibits rotation and permutation invariance. We perform novel ablation studies to help interpret the model performance by perturbing the physically meaningful input features (i.e., the diffraction patterns) instead of tuning the architecture of the learning model as in conventional ablation methods. We find that the spatial symmetry of the 3D diffraction patterns, which reflects crystalline symmetry operations, is more important than the diffraction intensities contained within for the model to make a successful classification. Our work showcases the potential of using statistical learning models to help understand materials physics, rather than performing predictive and generative tasks as in most materials informatics research. We also argue that learning the crystal structure genome in a chemistry-agnostic manner demonstrates that some crystal structures inherently host high propensities for optimal materials properties, which enables the decoupling of structure and composition for future codesign of multifunctionality., Comment: 14 pages, 6 Figures

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems., Comment: Accepted as a long paper at the Linguistic Annotation Workshop (LAW) at ACL 2019

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds., Comment: Added new funding support to the acknowledgments section in this version

Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis. Although interzeolite transformations enable selective crystallization, their design lacks predictions to connect framework similarity and experimental observations. Here, computational and theoretical tools are combined to data-mine, analyze and explain interzeolite relations. It is observed that building units are weak predictors of topology interconversion and insufficient to explain intergrowth. By introducing a supercell-invariant metric that compares crystal structures using graph theory, we show that topotactic and reconstructive (diffusionless) transformations occur only between graph-similar pairs. Furthermore, all known instances of intergrowth occur between either structurally-similar or graph-similar frameworks. Backed with exhaustive literature results, we identify promising pairs for realizing novel diffusionless transformations and intergrowth. Hundreds of low-distance pairs are identified among known zeolites, and thousands of hypothetical frameworks are connected to known zeolites counterparts. The theory opens a venue to understand and control zeolite polymorphism., Comment: Pre-peer review manuscript; 8 pages, 3 figures

Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research., Comment: NIPS Workshop on Machine Learning for Molecules and Materials