Materials Genome Initiative Research Articles

Active learning for the power factor prediction in diamond-like thermoelectric materials

The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry.

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry.

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows

Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the materials genome initiative (MGI) is most effectively pursued within the vision of a materials innovation ecosystem that spans across conventional engineering, science, and computing disciplines. The ICME foundation integrates principles of materials science with computational methods, including increasing reliance on modern data science methods that are savvy to digital information that recognizes hierarchical material structure and the need for correlative relations for process-structure and structure-property relations. We consider gaps in academic research and education programs related to systems engineering, uncertainty quantification of both experiments and computation, and data science methods. Barriers to the introduction of materials data science are discussed, as well as opportunities for innovation in educating the future MGI and ICME workforce.

Improvement of the oxidation resistance of silicon‐containing arylacetylene resins upon the introduction of carbazoles

AbstractSilicon‐containing arylacetylene resins (PSAs) can be used in high temperature environment due to their excellent thermal stability. However, their high temperature oxidation is still bottle‐neck for further application. Herein, Materials Genome Initiative (MGI) was utilized to identify a target monomer, 3,6‐diethynylcarbazole (DEC), to design new PSAs with enhanced antioxidant properties and heat resistance. After incorporation of DEC, the thermal curing behavior observed using differential scanning calorimetry (DSC), fourier transform infrared (FTIR) and thermogravimetric analysis (TGA) revealed that obtained silicon‐containing carbazolylacetylene resins (PSA‐VBC) can exhibit hydroamination reaction to decrease the initial curing temperature. And the temperature at 5% weight loss (Td5) occurred in cured copolymers ranged from 665°C to 691°C, which showed excellent heat resistance. Moreover, the oxidation behavior of cured resins was investigated by thermogravimetric/derivative thermogravimetry (TG/DTG), X‐ray photoelectron spectroscopy (XPS) and scanning electron microscope (SEM). Upon the incorporation of DEC, the thermal oxidation decomposition temperature of PSA‐VBC were 100°C higher than those observed for PSAs, which also proved by XPS analysis results that the oxygen content of PSA‐VBC solidified oxide was lower than that of PSAs. In addition, the surface morphology of cured PSA‐VBC resins still maintained integrity after oxidation at 400°C for 2 hr, which showed excellent oxidation resistance.

Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design.

With the advent of the materials genome initiative (MGI) in the United States and a similar focus on materials data around the world, a number of materials data resources and associated vocabularies, tools, and repositories have been developed. While the majority of systems focus on slices of computational data with an emphasis on metallic alloys, NanoMine is an open source platform with the goal of curating and storing widely varying experimental data on polymer nanocomposites (polymers doped with nanoparticles) and providing access to characterization and analysis tools with the long-term objective of promoting facile nanocomposite design. Data on over 2500 samples from the literature and individual laboratories has been curated to date into NanoMine, including 230 samples from the papers bound in this virtual issue. This virtual issue represents an experiment of the flexibility of the data repository to capture the unique experimental metadata requirements of many data sets at one time and to challenge the authors to participate in the curation of their research data associated with a given publication. In principle, NanoMine offers a FAIR platform in which data published in papers becomes directly Findable and Accessible via simple search tools, with open metadata standards that are Interoperable with larger materials data registries, and allows easy Reuse of data, e.g. benchmarking against new results. Our hope is that with time, platforms such as this one could capture much of the newly published data on materials and form nodes in an interconnected materials data ecosystem which would allow researchers to robustly archive their data, add to the growing body of readily accessible data, and enable new forms of discovery by application of data analysis and design tools.

Research status of the rare and precious metals’ Materials Genome Initiative

The core philosophy of Materials Genome Initiative (MGI) is the transition of the way of new materials design from the traditional “trial-and-error” approach to the in-silico materials design approach which employs intensive computing and material informatics. In June 2011, President Barack Obama launched MGI alongside the Advanced Manufacturing Partnership to help businesses discover, develop and deploy new materials twice as fast. In this paper, the concept of rare and precious genome is presented first, followed by the progress of MGI. After that, we focus on the research status of the rare and precious metals’ MGI including the computational tools, the high-throughput experimental methodologies and the rare and precious metals database. We also introduce the application of MGI in the development of rare and precious metal materials, outline the remaining fundamental challenges and present an outlook on the future of the rare and precious metals’ MGI.

The Impact of Creating the Next-Generation Materials Genome Initiative Workforce

The Impact of Creating the Next-Generation Materials Genome Initiative Workforce

(Invited) High-Throughput Synthesis, Evaluation, Analysis, and Data Visualization Technology for Exploring Multi-Components Functional Oxides Powder

In the sessions of Materials Genome Initiative (MGI) and Materials Informatics (MI) at recent international and domestic conferences, majority of research presentations are on material prediction using a combination of computational chemistry and machine learning. On the other hand, the contents incorporating high-throughput synthesis and evaluation experiments are only occasionally seen. It is also true that the number of samples that can be handled per hour is small compared to the former data science.However, if a series of experimental processes linked to synthesis, evaluation, and analysis is made user-friendly and high-throughput, it is possible to efficiently generate multi-condition and multi-component data in a unified experimental environment such as starting materials. As a result, even when creating a data set by extracting text from a paper, the data shortage can be supplemented with experimental data.Unlike thin films and polymers, powder synthesis is a heterogeneous reaction that requires the use of a wet process to perform high-throughput experiments. We have hitherto developed powder synthesis apparatus equipped with a micropump for solution dispensing, and high-throughput exploration system for powder and thin-film library based on electrostatic spray deposition consisted by combining multiple syringe pumps and a high-voltage power supply. By using these system, we have been exploring multi-component cathode materials for lithium ion battery and thermoelectric materials. In addition, recently, high-throughput experiments in a high-pressure environment of 200 MPa and 500ºC are possible.If the time required for physical property evaluation and data analysis is the same as before, even if only the synthesis based on experiments becomes high throughput, the attractiveness of MI will be weakened. Therefore, it is necessary to continue to develop jigs and software for efficient evaluation and analysis of huge libraries obtained in high-throughput experiments.In this presentation, we will introduce the exploration for A-site and B-site substitutes of perovskite-type CaMnO3, which is expected as an n-type thermoelectric material. In the preliminary search, it was confirmed that Ca1-x Bi x Mn1-y Ni y O3 contributes to the improvement of thermoelectric power by substituting elements at each site. As the next step, the amount of Bi substitution was determined to be within 10% from the combination of high-throughput synthesis and high-throughput Seebeck coefficient measurement. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. We also developed software that automates Rietveld analysis, although we need basic knowledge to set up an initial structural model. As a result, we have made it easier to generate data sets for visualizing crystallographic data and physical property data at once. In the obtained Bi-substituted CaMnO3 powder, when Bi was 8% or less, the conductivity and power factor increased due to the increase in carrier concentration. At Bi8% and higher, PF reaches its limit value, which involves a large change in the MnO6 octahedral plane bond distances Mn-O1 (1) and Mn-O1 (2), that is, a significant increase in octahedral distortion. High-throughput experiments including the collection of crystallographic information through the development of these technologies are expected to be an effective tool for future machine learning.

Development of database and information management system for data-driven materials design

With the progress of data-driven materials design, the scale and quality of datasets for intelligence algorithms such as machine learning and data mining have actually become the bottleneck for the practical benefit. The massive experimental data reported in the literatures and those unpublished are significant treasure trove for producing high-quality datasets. However, there has been a lack of specific database or information management system for storing and managing data as well as generating high-quality datasets. Using the Sm-Co alloy system as an example, which is a typical representative of the high-temperature permanent magnetic materials, we established an intelligent database and the corresponding information management system, where the data acquisition, data annotation, data extraction and data format conversion are flexibly integrated. We set up correlation models for the elemental composition, phase constitution, crystal structure, processing, measurement, and properties of the materials. From these models, the collected data are structured and the logical associations of related data are explicit. Thus, high-quality datasets can be produced from the developed system according to certain requirements for materials design. In the developed information management system, the online data annotation by multi-user has been realized. Moreover, the system is advanced in high-efficiency data entry and unifying data format, and precise search for redundant data. It has been confirmed that the present specific database and information management system play an important role in the data-driven materials design, and are promising in the applications in the fields of materials genome initiative and materials informatics.

A Design-to-Device Pipeline for Data-Driven Materials Discovery

The world needs new materials to stimulate the chemical industry in key sectors of our economy: environment and sustainability, information storage, optical telecommunications, and catalysis. Yet, nearly all functional materials are still discovered by "trial-and-error", of which the lack of predictability affords a major materials bottleneck to technological innovation. The average "molecule-to-market" lead time for materials discovery is currently 20 years. This is far too long for industrial needs, as highlighted by the Materials Genome Initiative, which has ambitious targets of up to 4-fold reductions in average molecule-to-market lead times. Such a large step change in progress can only be realistically achieved if one adopts an entirely new approach to materials discovery. Fortunately, a fundamentally new approach to materials discovery has been emerging, whereby data science with artificial intelligence offers a prospective solution to speed up these average molecule-to-market lead times.This approach is known as data-driven materials discovery. Its broad prospects have only recently become a reality, given the timely and major advances in "big data", artificial intelligence, and high-performance computing (HPC). Access to massive data sets has been stimulated by government-regulated open-access requirements for data and literature. Natural-language processing (NLP) and machine-learning (ML) tools that can mine data and find patterns therein are becoming mainstream. Exascale HPC capabilities that can aid data mining and pattern recognition and also generate their own data from calculations are now within our grasp. These timely advances present an ideal opportunity to develop data-driven materials-discovery strategies to systematically design and predict new chemicals for a given device application.This Account shows how data science can afford materials discovery via a four-step "design-to-device" pipeline that entails (1) data extraction, (2) data enrichment, (3) material prediction, and (4) experimental validation. Massive databases of cognate chemical and property information are first forged from "chemistry-aware" natural-language-processing tools, such as ChemDataExtractor, and enriched using machine-learning methods and high-throughput quantum-chemical calculations. New materials for a bespoke application can then be predicted by mining these databases with algorithmic encodings of relationships between chemical structures and physical properties that are known to deliver functional materials. These may take the form of classification, enumeration, or machine-learning algorithms. A data-mining workflow short-lists these predictions to a handful of lead candidate materials that go forward to experimental validation. This design-to-device approach is being developed to offer a roadmap for the accelerated discovery of new chemicals for functional applications. Case studies presented demonstrate its utility for photovoltaic, optical, and catalytic applications. While this Account is focused on applications in the physical sciences, the generic pipeline discussed is readily transferable to other scientific disciplines such as biology and medicine.

ASM News for February and March 2020

Abstract News about ASM members, chapters, events, awards, conferences, affiliates, and other society activities. Topics include: ASM affiliate societies name committee chairs for the 2019-2020 term; ASM President Zi-Kui Liu discusses the ASM response to Materials 4.0 and the Materials Genome Initiative; progress report on the ASM strategic plan; professional profile for Danielle Cote.

Intellectual Community as a Bridge of Interdisciplinary Graduate Education in Materials Data Science

AbstractRecognizing materials development was advancing slower than technological needs, the 2011 the Materials Genome Initiative (MGI) advocated interdisciplinary approaches employing an informatics framework in materials discovery and development. In response, an interdisciplinary graduate program, funded by the National Science Foundation, was designed at the intersection of materials science, materials informatics, and engineering design, aiming to equip the next generation of scientists and engineers with Material Data Science. Based on the 4- year implementation experience, this report demonstrates how intellectual communities bridge students interdisciplinary learning processes and support a transition from disciplinary grounding to interdisciplinary learning and research. We hope this training model can benefit other interdisciplinary graduate programs, and produce a more productive and interdisciplinary materials workforce.

Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

ABSTRACTSince the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects.

First-principles calculations of electronic structure and optical and elastic properties of the novel ABX3-type LaWN3 perovskite structure.

The development of ABX3-type advanced perovskite materials has become a focus for both scientific researchers and the material genome initiative (MGI). In addition to the traditional perovskite ABO3 and halide perovskite ABX3, LaWN3 is discovered as a new ABX3-type advanced perovskite structure. The elastic and optical properties of this novel LaWN3 structure are systematically studied via DFT. Based on the calculated elastic constants, the bulk modulus, shear modulus, Young's modulus and Pugh modulus ratio are precisely obtained. Results show that (1) LaWN3 is an indirect bandgap semiconductor with a hybrid occuring near the Fermi level and the main contributions are La-d, W-d and N-p. (2) LaWN3 has a certain ductility. The optical constants, such as absorption spectrum, energy-loss spectrum, conductivity, dielectric function, reflectivity and refractive index, are analyzed and the static dielectric constant is 10.98 and the refractivity index is 3.31. (3) The optical constants of LaWN3 are higher than those of other existing ABX3-type materials, showing very promising application as a functional perovskite in the future. The existence of this stable LaWN3 structure might widen the perovskite material's application, such as in photodetectors, light-emitting diodes, perovskite solar cells, fuel cells and so on.

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

The success of the Materials Genome Initiative has led to opportunities for data-driven approaches for materials discovery. The recent development of Polymer Genome (PG), which is a machine learning (ML) based data-driven informatics platform for polymer property prediction, has significantly increased the efficiency of polymer design. Nevertheless, continuous expansion of the ‘training data’ is necessary to improve the robustness, versatility, and accuracy of the ML predictions. Accurate prediction of polymer properties, such as glass transition temperature (Tg), is advantageous for the design of polymers, particularly for high temperature applications. We hypothesized that by adding more data with increased chemical diversity to the dataset, the predictive capabilities of the PG model would improve. In order to test the performance and transferability of the predictive model for Tg (previously trained on a dataset of 450 polymers), we have carefully collected additional experimental Tg data for 871 polymers from multiple data sources. The Tg values predicted by the present PG models for the polymers in the newly collected dataset were compared directly with the experimental Tg to estimate the accuracy of the present model. Using the full dataset of 1321 polymers, a new ML model for Tg was built following past work. The root mean square error (RMSE) of prediction for the extended dataset, when compared to the earlier one, decreased to 27 K from 57 K, thereby supporting our initial hypothesis that increasing the dataset would improve the predictions. To further improve the performance of the Tg prediction model, we are continuing to accumulate new data and exploring new ML approaches.

Development of the Multifactor Computational Models of the Solid Propellants Combustion by Means of Data Science Methods. Propellant Combustion Genome Conception

The results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models of the solid propellants (SP) combustion that solve the direct and inverse tasks are presented. The own analytical platform Loginom was used for the models creation. The models of combustion of double based SP with such nano additives as metals, metal oxides, termites were created by means of experimental data published in scientific literature. The goal function of the models were burning rate (direct tasks) as well as propellants composition (inverse tasks). The basis (script) of a creation of Data Warehouse of SP combustion was developed. The Data Warehouse can be supplemented by new experimental data and metadata in automated mode and serve as a basis for creating generalized combustion models of SP and thus the beginning of work in a new direction of combustion science, which the authors propose to call "Propellant Combustion Genome" (by analogy with a very famous Materials Genome Initiative, USA). "Propellant Combustion Genome" opens wide possibilities for accelerate the advanced propellants development Genome" opens wide possibilities for accelerate the advanced propellants development.

Mesoscale simulation research on the homogenized elasto-plastic behavior of FeCrAl alloys

Iron chromium aluminum (FeCrAl) alloys have been considered as a promising candidate in nuclear power applications. In order to accelerate the advanced fabrication of these alloys, it is necessary to perform numerical simulations to predict the macroscale mechanical properties for FeCrAl alloys. In this study, modified crystal plasticity models and stress update algorithm are proposed to describe the deformation behavior of single crystals. Computational homogenization methods for polycrystalline aggregates are provided to obtain the macroscopic properties from the mesoscale stress and strain fields in the representative volume element (RVE). The involved models and algorithms are implemented in ABAQUS with a self-written VUMAT subroutine, and validated with experimental data. The homogenized elasto-plastic curves for FeCrAl alloys are numerically obtained, which match well with the experimental results for different alloy compositions, grain sizes and processing conditions. The relations of the shear-rate model parameters with the compositions, microstructures and processing conditions of FeCrAl alloys are discussed. The research results indicate that the phenomenological shear-rate model and the modified crystal plasticity models can be adopted in Materials Genome Initiative (MGI) to achieve the multi-scale simulation of polycrystalline mechanical performances. The simulation results could also provide a reference for the optimization of FeCrAl alloys.

First Principle Material Genome Approach for All Solid‐State Batteries

Due to ever‐increasing concern about safety issues in using alkali metal ionic batteries, all solid‐state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high‐performance ASSBs lies in delivering ultra‐fast ionic conductors that are compatible with both alkali anodes and high‐voltage cathodes. Such a challenging task cannot be fulfilled, without solid understanding covering materials stability and properties, interfacial reactions, structural integrity, and electrochemical windows. Here in this work, we will review recent advances on fundamental modeling in the framework of material genome initiative based on the density functional theory (DFT), focusing on solid alkali batteries. Efforts are made in offering a dependable road chart to formulate competitive materials and construct “better” batteries.