348 citations · 1k across the 41 of their papers we have counts for
28 papers · 1 filter
PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
Zekun Ren, Hongzhao Tan, Jiaen Yee +1
Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (G…
Virp: neural network-accelerated prediction of physical properties in site-disordered materials
Andy Paul Chen, Martin Hoffmann Petersen, Kedar Hippalgaonkar
Among metallic alloys, ceramics, and even common compounds such as water ice, it is usual to find materials in which crystalline order is expressed as a probability. In such cases,…
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen +11
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantial…
Generative design of inorganic materials
Jose Recatala-Gomez, Haiwen Dai, Zhu Ruiming +9
Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are ef…
Navigating Order-(Dis)Order Family Trees via Group-Subgroup Transitions
Shuya Yamazaki, Yuyao Huang, Martin Hoffmann Petersen +2
As closed-loop materials discovery systems scale to produce millions of candidate compounds, the credibility of the novelty they reward becomes a critical concern. Novelty is commo…
SWORD: Symmetry and Wyckoff-sequence of Ordered and Disordered crystals
Yuyao Huang, Wei Nong, Shuya Yamazaki +4
Novelty in materials discovery requires candidates to be distinct, non-redundant, and thermodynamically plausible. While crystallographic databases continue to expand in both size…
Importance of Configurational Charge Ordering for Machine Learning Interatomic Potentials
Martin Hoffmann Petersen, Steen Lysgaard, Arghya Bhowmik +2
Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly…
Dis-GEN: Disordered crystal structure generation
Martin Hoffmann Petersen, Ruiming Zhu, Haiwen Dai +6
A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a resu…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Data-Driven Design-Test-Make-Analyze Paradigm for Inorganic Crystals: Ultrafast Synthesis of Ternary Oxides
Haiwen Dai, Matthew J. McDermott, Andy Paul Chen +19
Data-driven methodologies hold the promise of revolutionizing inorganic materials discovery, but they often face challenges due to discrepancies between theoretical predictions and…
Direct Joule-Heated Non-Equilibrium Synthesis Enables High Performing Thermoelectrics
Chenguang Zhang, Jose Recatala-Gomez, Zainul Aabdin +20
High-throughput synthesis of bulk inorganic materials is crucial for accelerating functional materials discovery but is hindered by slow, energy-intensive solid-state methods. We i…
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…
Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning
Shuya Yamazaki, Wei Nong, Ruiming Zhu +3
Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerg…
Wyckoff Transformer: Generation of Symmetric Crystals
Nikita Kazeev, Wei Nong, Ignat Romanov +4
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization beh…
Exploring material compositions for synthesis using oxidation states
Maung Thway, Andy Paul Chen, Haiwen Dai +9
Recent advances in machine learning techniques have made it possible to use high-throughput screening to identify novel materials with specific properties. However, the large numbe…
WyCryst: Wyckoff Inorganic Crystal Generator Framework
Ruiming Zhu, Wei Nong, Shuya Yamazaki +1
Generative design marks a significant data-driven advancement in the exploration of novel inorganic materials, which entails learning the symmetry equivalent to the crystal structu…
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
Qiaohao Liang, Aldair E. Gongora, Zekun Ren +12
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and hig…
Electronic transport descriptors for the rapid screening of thermoelectric materials
Tianqi Deng, Jose Recatala-Gomez, Masato Ohnishi +11
The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way t…
Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies
Chee Kwan Gan, Yun Liu, Tze Chien Sum +1
Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals. It involves displacing atoms by a small amount in order to calcula…
Machine-learning assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors
Yijie Xu, Yun Zong, Kedar Hippalgaonkar
Solid state lithium- and sodium-ion batteries utilize solid ionicly conducting compounds as electrolytes. However, the ionic conductivity of such materials tends to be lower than t…
Hassle-free Approach to Thermal Transport Measurements Using Spatial-Temporal Temperature Data
Ding Ding, Kedar Hippalgaonkar
Nanoscale engineering and novel materials have created interesting effects in thermal transport. Thermal conductivity can now be different due to physical and heating sizes. Also,…
Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges
Hang Zhang, Kedar Hippalgaonkar, Tonio Buonassisi +3
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research…
Graph Convolutional Neural Networks for Polymers Property Prediction
Minggang Zeng, Jatin Nitin Kumar, Zeng Zeng +3
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design. In this work, we apply graph convolutional neural network…
Polymer morphology and interfacial charge transfer dominate over energy-dependent scattering in organic-inorganic thermoelectrics
Pawan Kumar, Edmond W. Zaia, Erol Yildirim +4
Hybrid (organic-inorganic) materials have emerged as a promising class of thermoelectric materials, achieving power factors exceeding those of either constituent. The mechanism of…