activity
20132026
most citedAccelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

348 citations · 1k across the 41 of their papers we have counts for

collaborators
Showing 2025Show all

8 papers · 1 filter

physics.soc-ph2025

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…

cond-mat.mtrl-sci2025★ 2 cited

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…

cond-mat.mtrl-sci2025

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,…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025★ 1 cited

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…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025★ 1 cited

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…

cond-mat.mtrl-sci2025★ 3 cited

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…