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The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor, most innovation has been slowly driven by experimental means. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy– entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries.

High entropy materials have recently attracted significant interest due to their appealing mechanical, catalytic, and electronic properties. High-entropy ceramics consist of an ordered anion sublattice of carbon, nitrogen or oxygen and a disordered cation sublattice maximizing configurational entropy by randomly occupying it by five or more cation species (transition metal elements). The reliable computational modelling of such systems can be realized by the partial occupation algorithm [1] implemented within the AFLOW software for materials design [2,3] by expanding the disordered system into a large set of ordered structures. These cells can then be treated by high-throughput ab initio calculations. For the actual realization of high-entropy materials, predictive synthesizability descriptors such as the entropy-forming ability (EFA) [4] are needed. We present here results on several high-entropy ceramic candidates, apply different synthesizability descriptors, and study their electronic and mechanical properties. [1] K. Yang et al., Chem. Mater. 28, 6484 (2016). [2] C. Oses et al., Comput. Mater. Sci. 217, 111889 (2023). [3] M. Esters et al., Comput. Mater. Sci. 216, 111808 (2023). [4] P. Sarker et al., Nat. Commun. 9, 4980 (2018).

The formation enthalpy, quantifying the enthalpy of a compound with respect to its elemental references, is a key parameter for predicting the thermodynamic stability of materials thus enabling data-driven materials design. Although for instance zero-point vibrational and thermal contributions to the formation enthalpy can be quite substantial reaching absolute values of up to ∼ 50 meV/atom for ionic systems such as oxides, they are often neglected in ab initio workflows. Here, we first calculate the thermal and zero-point contributions accurately from a quasi-harmonic Debye model. At room temperature, they largely cancel each other due to the different bond stiffness of compound and references reducing the total vibrational contribution to maximally ∼ 20 meV/atom [1]. Moreover, the vibrational contributions can be parametrized within the coordination corrected enthalpies (CCE) method completely eliminating the need to compute these terms explicitly. On this basis, using only 0 K ab initio data as input, a workflow can be designed providing access to formation enthalpies at different temperatures from the AFLOW-CCE tool [2]. [1] R. Friedrich et al., npj Comput. Mater. 5, 59 (2019). [2] R. Friedrich et al., Phys. Rev. Mater. 5, 043803 (2021).

dataset to calculate DEED; code of the algorithm to perform the DEED calculation

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.

To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it — all while considering the diversity of users’ experience levels and usage needs. The recently formulated FAIR principles (Findable, Accessible, Interoperable, and Reusable) establish a common framework to aid these efforts. This article describes aflow.org, a web ecosystem developed to provide FAIR-compliant access to the AFLOW databases. Graphical and programmatic retrieval methods are offered, ensuring accessibility for all experience levels and data needs. aflow.org goes beyond data-access by providing applications to important features of the AFLOW software [1], assisting users in their own calculations without the need to install the entire high-throughput framework. Outreach commitments to provide AFLOW tutorials and materials science education to a global and diverse audiences will also be presented.

BACKGROUND: Less than 5% of medulloblastoma (MB) patients survive following failure of contemporary radiation-based therapies. Understanding the molecular drivers of medulloblastoma relapse (rMB) will be essential to improve outcomes. Initial genome-wide investigations have suggested significant genetic divergence of the relapsed disease. METHODS: We undertook large-scale integrated characterization of the molecular features of rMB-molecular subgroup, novel subtypes, copy number variation (CNV), and driver gene mutation. 119 rMBs were assessed in comparison with their paired diagnostic samples (n = 107), alongside an independent reference cohort sampled at diagnosis (n = 282). rMB events were investigated for association with outcome post-relapse in clinically annotated patients (n = 54). RESULTS: Significant genetic evolution occurred over disease-course; 40% of putative rMB drivers emerged at relapse and differed significantly between molecular subgroups. Non-infant MBSHH displayed significantly more chromosomal CNVs at relapse (TP53 mutation-associated). Relapsed MBGroup4 demonstrated the greatest genetic divergence, enriched for targetable (eg, CDK amplifications) and novel (eg, USH2A mutations) events. Importantly, many hallmark features of MB were stable over time; novel subtypes (>90% of tumors) and established genetic drivers (eg, SHH/WNT/P53 mutations; 60% of rMB events) were maintained from diagnosis. Critically, acquired and maintained rMB events converged on targetable pathways which were significantly enriched at relapse (eg, DNA damage signaling) and specific events (eg, 3p loss) predicted survival post-relapse. CONCLUSIONS: rMB is characterised by the emergence of novel events and pathways, in concert with selective maintenance of established genetic drivers. Together, these define the actionable genetic landscape of rMB and provide a basis for improved clinical management and development of stratified therapeutics, across disease-course.

Materials databases such as AFLOW [1] leverage ab initio calculations for autonomous materials design. The predictive power critically relies on accurate formation enthalpies — quantifying the thermodynamic stability of a system. For ionic materials such as oxides and nitrides, standard DFT leads to errors of several hundred meV/atom [2,3]. We have recently developed the "coordination corrected enthalpies" (CCE) method yielding highly accurate room temperature formation enthalpies with mean absolute errors down to 27 meV/atom [3]. Here, we introduce AFLOW-CCE [4] — our implementation of CCE into the AFLOW framework. It provides a tool where users can input a structure file and receive the CCE corrections, or even the CCE formation enthalpies if pre-calculated LDA, PBE or SCAN values are provided. The implementation features a command line tool, a web interface, and a Python environment. [1] S. Curtarolo et al., Comput. Mater. Sci. 58, 218 (2012). [2] V. Stevanović et al., Phys. Rev. B 85, 115104 (2012). [3] R. Friedrich et al., npj Comput. Mater. 5, 59 (2019). [4] R. Friedrich et al., Phys. Rev. Mater. 5, 043803 (2021).

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century., Peer Reviewed