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XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science Open
Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models are impractical for real-world applications where atomic s…
Text2Struc: Programmatic Crystal Structure Generation with Fine-Tuned Large Language Models Open
Accelerating computational materials science relies not only on hardware advances but also on software that increases the ease of working with the relevant abstractions. Creation and manipulation of crystal structures is a part of many rou…
De novo design of polymer electrolytes using GPT-based and diffusion-based generative models Open
Solid polymer electrolytes offer promising advancements for next-generation batteries, boasting superior safety, enhanced specific energy, and extended lifespans over liquid electrolytes. However, low ionic conductivity and the vast polyme…
A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes Open
We introduce a computational materials discovery framework that integrates conditional generation, molecular dynamics simulations, evaluation, and feedback components to design polymer electrolytes with improved ionic conductivity.
UniMat: Unifying Materials Embeddings through Multi-modal Learning Open
Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-based synthesis conditions. The advancements in multi-modal…
An electrochemical series for materials Open
The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which ar…
An Electrochemical Series for Materials Open
The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which ar…
An Electrochemical Series for Materials Open
The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which ar…
De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models Open
Solid polymer electrolytes hold significant promise as materials for next-generation batteries due to their superior safety performance, enhanced specific energy, and extended lifespans compared to liquid electrolytes. However, the materia…
A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model Open
In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolytes. The platform integrates three core components: a condit…
An Electrochemical Series for Materials Open
The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which ar…
The Role of Reference Points in Machine-Learned Atomistic Simulation Models Open
This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models, widely used in ato…
Novel inorganic crystal structures predicted using autonomous simulation agents Open
We report a dataset of 96640 crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery).…
Novel inorganic crystal structures predicted using autonomous simulation agents Open
We report a dataset of 96,962 new crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discov…
A Universal Machine Learning Model for Elemental Grain Boundary Energies Open
The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric …
camd2022.tar.gz Open
This dataset is composed of autonomously discovered crystal structures from CAMD. 96640 distinct crystal structures with accompanying formation energy. 26,435 distinct formulas, 1,457 distinct element combinations (chemical systems). 26826…
Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning Open
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive den…
A Critical Review of Machine Learning of Energy Materials Open
Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find ma…
View article: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Open
Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both m…