The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design

Abstract

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of 40,000 materials and 1 million calculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields in JARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

References in corpus (14)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• BoltzTraP. A code for calculating band-structure dependent quantities
• Wannier90: A Tool for Obtaining Maximally-Localised Wannier Functions
• WannierTools: An open-source software package for novel topological materials
• Electronic Structure Calculations with Dynamical Mean-Field Theory: A Spectral Density Functional Approach
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
• Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
• Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
• High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
• Elastic properties of bulk and low-dimensional materials using Van der Waals density functional
• MPInterfaces: A Materials Project based Python Tool for High-Throughput Computational Screening of Interfacial Systems
• Spin-orbit spillage as a measure of band inversion in insulators

Cited by in corpus (78)

• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
• Materials Cloud, a platform for open computational science
• Data-Driven Design for Metamaterials and Multiscale Systems: A Review
• A critical examination of robustness and generalizability of machine learning prediction of materials properties
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• On the redundancy in large material datasets: efficient and robust learning with less data
• OPTIMADE, an API for exchanging materials data
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Uncertainty Prediction for Machine Learning Models of Material Properties
• Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
• Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
• Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
• AI-driven inverse design of materials: Past, present and future
• High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors
• Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
• Probing out-of-distribution generalization in machine learning for materials
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models
• Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
• High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments
• Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
• ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
• Systematic DFT+U and Quantum Monte Carlo benchmark of magnetic two-dimensional (2D) CrX${}_3$ (X = I, Br, Cl, F)
• A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
• Two-dimensional Weyl points and nodal lines in pentagonal materials and their optical response
• Formula graph self-attention network for representation-domain independent materials discovery
• Density Functional Theory based Electric Field Gradient Database
• InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
• Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
• Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
• Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table
• Hardness and fracture toughness models by symbolic regression
• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
• Predicting Anomalous Quantum Confinement Effect in van der Waals Materials
• Optimal pre-train/fine-tune strategies for accurate material property predictions
• Correlation Driven Phonon Anomalies in Bulk FeSe
• AtomVision: A machine vision library for atomistic images
• Screening Mixed-Metal Sn${}_2$M(III)Ch${}_2$X${}_3$ Chalcohalides for Photovoltaic Applications
• Data-driven study of composition-dependent phase compatibility in NiTi shape memory alloys
• An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys
• Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions
• Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
• Data-Driven Materials Discovery and Synthesis using Machine Learning Methods
• DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
• Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning
• The JARVIS Infrastructure is All You Need for Materials Design
• Accelerating Material Property Prediction using Generically Complete Isometry Invariants
• Quantum Computation for Predicting Electron and Phonon Properties of Solids
• Material Property Prediction using Graphs based on Generically Complete Isometry Invariants
• Materials Expert-Artificial Intelligence for Materials Discovery
• Predicting Solid State Material Platforms for Quantum Technologies
• Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions
• SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
• Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures
• Automated Extraction of Material Properties using LLM-based AI Agents
• Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends
• mkite: A distributed computing platform for high-throughput materials simulations
• A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
• ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials Science
• Lean CNNs for mapping electron charge density fields to material properties
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
• Promoting and imaging intervalley coherent order in rhombohedral tetralayer graphene on MoS2
• Spectral Operator Representations
• Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
• CateCom: a practical data-centric approach to categorization of computational models
• Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
• Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
• Self-Consistent Coulomb Interactions from Constrained Dynamical Mean-Field Theory
• M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
• A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks