348 citations · 1k across the 41 of their papers we have counts for
41 papers
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…
Closing the Loop in Epitaxy with Machine Learning: Joint Optimization of Growth and Geometry in On-Chip Lasers
Mihir R. Athavale, Stephen A. Church, Wei Wen Wong +4
Achieving device-to-device reproducibility is a critical bottleneck for scalable photonic integrated circuits, as subtle variations in bottom-up epitaxial growth and fabrication se…
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…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
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…
Accelerated Design of Microring Lasers with Multi-Objective Bayesian Optimization
Mihir R. Athavale, Ruqaiya Al-Abri, Stephen Church +5
On-chip coherent laser sources are crucial for the future of photonic integrated circuits, yet progress has been hindered by the complex interplay between material quality, device…
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning
Maung Thway, Jose Recatala-Gomez, Fun Siong Lim +2
Generative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor…
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…
Constructing Custom Thermodynamics Using Deep Learning
Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi +5
One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by know…
Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
Ji Wei Yoon, Adithya Kumar, Pawan Kumar +3
The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identifica…
Automated Electrokinetic Stretcher for Manipulating Nanomaterials
Beatrice W. Soh, Zi-En Ooi, Eleonore Vissol-Gaudin +2
In this work, we present an automated platform for trapping and stretching individual micro- and nanoscale objects in solution using electrokinetic forces. The platform can trap ob…
Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity
Daniil Bash, Frederick Hubert Chenardi, Zekun Ren +5
Functional composite thin films have a wide variety of applications in flexible and/or electronic devices, telecommunications and multifunctional emerging coatings. Rapid screening…