1.8k citations · 2.6k across the 21 of their papers we have counts for
23 papers
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
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…
Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features
Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz +9
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model wa…
Accelerating Communications in Federated Applications with Transparent Object Proxies
J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward +5
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 remot…
Workflows Community Summit 2022: A Roadmap Revolution
Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala +102
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex s…
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson +7
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 supe…
Quantifying the performance of machine learning models in materials discovery
Christopher K. H. Borg, Eric S. Muckley, Clara Nyby +4
The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or…
Rapid Production of Accurate Embedded-Atom Method Potentials for Metal Alloys
Elan J. Weiss, Logan Ward, Christian Oberdorfer +4
A critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the limited availability of suitable interatomic potentials. Here, we introduce the…
Mapping Thermoelectric Transport in a Multicomponent Alloy Space
Ramya Gurunathan, Suchismita Sarker, Christopher K. H. Borg +4
Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity through large compositional disorder. However, h…
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Logan Ward, Ganesh Sivaraman, J. Gregory Pauloski +9
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use…
Principles of the Battery Data Genome
Logan Ward, Susan Babinec, Eric J. Dufek +24
Electrochemical energy storage is central to modern society -- from consumer electronics to electrified transportation and the power grid. It is no longer just a convenience but a…
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
Jenna Bilbrey, Logan Ward, Sutanay Choudhury +2
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-valid…
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury +2
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative mod…
AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
Zhi Hong, J. Gregory Pauloski, Logan Ward +3
Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A…
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
Sutanay Choudhury, Jenna A. Bilbrey, Logan Ward +5
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and…
A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
Howie Joress, Brian L. DeCost, Suchismita Sarker +7
Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid…
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Dipendra Jha, Logan Ward, Zijiang Yang +5
Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applyi…
Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations
Logan Ward, Ben Blaiszik, Ian Foster +3
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we ass…
A Data Ecosystem to Support Machine Learning in Materials Science
Ben Blaiszik, Logan Ward, Marcus Schwarting +5
Facilitating the application of machine learning to materials science problems will require enhancing the data ecosystem to enable discovery and collection of data from many source…
Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction
Maximilian Amsler, Logan Ward, Vinay I. Hegde +3
We report the computational investigation of a series of ternary XYZ and XYZ compounds with X={Mg, Ca, Sr, Ba}, Y={P, As, Sb, Bi}, and Z={S, Se, Te}. The compos…
DLHub: Model and Data Serving for Science
Ryan Chard, Zhuozhao Li, Kyle Chard +7
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermo…
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
Logan Ward, Ankit Agrawal, Alok Choudhary +1
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior example…
Structural evolution and kinetics in Cu-Zr Metallic Liquids
Logan Ward, Dan Miracle, Wolfgang Windl +2
The atomic structure of the supercooled liquid has often been discussed as a key source of glass formation in metals. The presence of icosahedrally-coordinated clusters and their t…
Rapid Production of Accurate Embedded-Atom Method Potentials for Metal Alloys
Logan Ward, Anupriya Agrawal, Katharine M. Flores +1
The most critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the availability of suitable interatomic potentials. In this work, we demonst…