Logan Ward
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View article: Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Open
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across d…
View article: MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Open
We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance computing (HPC) systems. MOFA addresses key challenges in inte…
View article: Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows Open
The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 111 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused…
View article: Employing artificial intelligence to steer exascale workflows with colmena
Employing artificial intelligence to steer exascale workflows with colmena Open
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive…
View article: Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows Open
The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 111 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused…
View article: Employing Artificial Intelligence to Steer Exascale Workflows with Colmena
Employing Artificial Intelligence to Steer Exascale Workflows with Colmena Open
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive…
View article: Object Proxy Patterns for Accelerating Distributed Applications
Object Proxy Patterns for Accelerating Distributed Applications Open
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optim…
View article: <i>E</i><sub>min</sub>: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability
<i>E</i><sub>min</sub>: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability Open
Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple yet accurate thermochemi…
View article: Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision Open
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM),…
View article: E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability
E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability Open
Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple, yet accurate thermochem…
View article: Foundry-ML - Software and Services to Simplify Accessto Machine Learning Datasets in Materials Science
Foundry-ML - Software and Services to Simplify Accessto Machine Learning Datasets in Materials Science Open
Schmidt et al., (2024). Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Journal of Open Source Software, 9(93), 5467, https://doi.org/10.21105/joss.05467
View article: Machine learning and TDDFT software for stopping power computation
Machine learning and TDDFT software for stopping power computation Open
(SF-24-012) Stopping power describes the rate that a material slows radiation particles passing through it and is useful in designing many technologies. Few organizations can perform new measurements, which require significant resources an…
View article: Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’
Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’ Open
Reproducing results from a foundational materials informatics tool (magpie) is difficult and in this study, a failure. This failure yields tangible suggestions to promote easy adoption and trust of materials informatics in the future.
View article: Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision Open
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM),…
View article: A case study of multi-modal, multi-institutional data management for the combinatorial materials science community
A case study of multi-modal, multi-institutional data management for the combinatorial materials science community Open
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require…
View article: Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning
Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning Open
Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear c…
View article: GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics Open
We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSL…
View article: Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials' Open
The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open-source data and tools to propel the field. Despite the in…
View article: DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies Open
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across …
View article: Computer Vision-aided <i>in situ</i> TEM Studies of Microstructure Evolution under Irradiation
Computer Vision-aided <i>in situ</i> TEM Studies of Microstructure Evolution under Irradiation Open
Materials used in nuclear reactors are exposed to irradiation that causes detrimental microstructural modification such as the formation of point defects, defect clusters, voids, dislocation loops, segregation, and precipitation.In situ TE…
View article: Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning
Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning Open
In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528-4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol c…
View article: 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon Open
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. Thi…
View article: Accelerating Communications in Federated Applications with Transparent Object Proxies
Accelerating Communications in Federated Applications with Transparent Object Proxies Open
Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remote accelerators despite added latency. Workflow and cloud-ho…
View article: Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources Open
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specializ…