Conference Tools for 2027 TMS Annual Meeting & Exhibition

Login

Register as a New User

Help

Submit An Abstract

Propose A Symposium

Presenter/Author Tools

Organizer/Editor Tools

About this Abstract

Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Prediction and Screening of Transition Metal Nitrides for Ammonia Synthesis

Author(s)

Luka Mikek, Dallas R Trinkle

On-Site Speaker (Planned)

Luka Mikek

Abstract Scope

Synthesis of ammonia is of critical importance to applications in energy, industrial chemistry, and especially agriculture. Today, this synthesis is accomplished industrially using the energy-intensive Haber-Bosch process. Transition metal nitrides hold promise for a more energy-efficient synthesis in which the nitride material itself can be depleted and regenerated cyclically to act as the nitrogen source. In this study, we present a first-principles exploration of Co₃Mo₃N, an η-carbide-type nitride that has shown promise for this application. We examine the defect energies and phase stability over a broad range of relevant nitrogen stoichiometries in Co₃Mo₃N and use the results to develop screening criteria. We then apply these criteria to screen a broad range of other similar nitrides, both known and novel, using density functional theory. We identify several other materials, including Co₃W₃N, Mo₃Co₂FeN, and W₃Co₂FeN, as promising candidates for ammonia synthesis.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Modeling and Simulation,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Multi-Fidelity Machine-Learning Workflow for Accelerated Lithium-Ion Cathode Materials Development

Accelerating Quantum Materials Discovery with AI

Agapi-agents: An open-access agentic ai platform for accelerated materials design on atomgpt. org

Assessing, training, and fine-tuning foundation potentials for plasticity-relevant properties of metals

Beyond First Principles: Convergent and Thermodynamically Self-Consistent Atomistic Phase Diagrams from Interatomic Potentials

Capturing Spin-Disorder in Paramagnetic Austenitic Steels: A First-Principles Approach

Characterization of the Atomic-Scale Strengthening Mechanisms in Oxide Dispersion Strengthened Alloys at Elevated Temperatures

Closing the Loop on Superconductor Discovery

Collective motion of screw dislocations in α-Fe and its effect on Peierls stress

Computational discovery of synthesizable and hard multicomponent transition metal carbides

Crystal Structure Exploration Under Extreme Conditions

Defect-Controlled Carbon Transport in Zirconium Carbide under Charge, Temperature, and Pressure Extremes.

Digital material design guidelines for mitigation of alloy embrittlement

Effects of Chemical Fluctuations and Short-Range Order on Dislocation Behavior in Multi-Principal-Element Alloys

Enhancing of the Thermal Conductivity in Refractory-based High Entropy Alloys via Mott s-d Scattering Mechanism

ExactBO: GPU-Accelerated Certified Bayesian Optimization for Continuous and Constrained Materials Design

First-Principles and Graph Learning Framework for Discovery of Li-Ion Transport Pathways in Solid Electrolyte Interphases

First-principles mapping of defect energetics across the 211 MAX phase chemical space

First-Principles Transport in AlGaN/GaN HEMT-like Interfaces

First Principles Data Assisted Investigation of Spin Ordering in Spinel Ni1−xZnxFe2O4 Crystal

From DFT Defect Mechanisms to Transport-Scale Modeling of Hydrogen Isotopes in Li2O

Geometric Design of d- and i-wave Altermagnetism in Graphene Nanohole Superlattices

Humans-(less)-in-the-loop: moving toward autonomous materials design for metal-organic frameworks

Hysteresis in silicon-dominated anodes in lithium-ion batteries.

Integrating Physical Models, Specialized Databases, AI/ML Tools, and Self-Driving Labs to Understand the Synthesis of Inorganic Materials

Intermetallic Composition as a Variable in Alloy Design

Machine Learning-Assisted Design of High-Performance HOR Catalysts for Proton-Conducting Fuel Cells

Machine Learning Screening of Oxide Semiconductors for Energy-Efficient Back-End Transistors

Metadynamics-Accelerated Active Learning of Interatomic Potentials for InP Magic-Sized Cluster Nucleation

PhaseForge: Automated, Uncertainty-Quantified Construction of CALPHAD Thermodynamic Databases via Machine-Learning Interatomic Potentials and Bayesian Parameter Refinement

Physics-Grounded AI for Materials Discovery

Physics-Informed Discovery of Hydride Superconductors

Predicting plateau slope in metal hydrides from first-principles: hydrogen energy and charge distributions in TiMn₂-based alloys

Prediction and Screening of Transition Metal Nitrides for Ammonia Synthesis

Quantifying the Contributions of Strengthening Mechanisms in Refractory Complex Concentrated Alloys through Physics-Informed Statistical Learning.

Role of a Ni₁₃ Cocatalyst in Promoting CO₂ Activation and Hydrogenation on CaCu₃Ti₄O₁₂: A First-Principles Study

Size Effects on Chemical Ordering in High-Entropy MXenes: A First-Principles Validated Atomistic Study

Stabilizing Titanium Carbide MXenes in Water with Montmorillonite Heterostructures: Ab Initio Molecular Dynamics Study

Thermodynamic models for the epitaxial growth of oxide solid solutions

Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement