The Energy Technologies and Systems Division at Lawrence Berkeley National Laboratory is seeking a Postdoctoral Scholar to join the Jain...

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Connecting 0 K density functional theory (DFT) energies to finite-temperature, finite-pressure synthesis conditions is a well-established thermodynamic formalism, yet quantified guidance on when and how to accurately calibrate these results to experimental chemical potentials in practice remains sparse. In this work, we systematically benchmark a complete workflow─the virtual furnace─that constructs effective oxygen chemical potentials (μO2) for common synthesis atmospheres (air, Ar, H2, CO) as functions of temperature and partial pressure and propagates quantified errors from formation enthalpies through reaction energies to critical chemical potentials. Applying this workflow to 11 binary oxides, we find that “gas-only” thermal corrections are sufficient at low temperatures and for Group II oxides across all temperatures, while solid-phase vibrational contributions become critical at elevated temperatures for transition metal oxides. We quantify this threshold through the ratio |ΔSsolid/ΔStotal| as a practical guide for when phonon calculations are warranted. Predicted critical reduction temperatures and oxygen chemical potentials show strong agreement with industrial practice and experimental data, with typical errors of 150–250 °C for most oxides. This benchmarked workflow provides practitioners with explicit, quantified guidance for predictive modeling of phase stability and synthesis condition design.

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Scientific Data - Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model

The SCGSR program provides supplemental awards to outstanding U.S. graduate students to pursue part of their graduate thesis research at a DOE laboratory/facility in areas that address scientific chal...

We are looking for:PhD in Materials Science, Chemistry, or a related field; Demonstrable strong Python programming (programming portfolio...

Microscale control of thermal conductivity in Sb2S3 is demonstrated via laser-induced rotating lattice crystals. Thermal conductivity imaging reveals marked thermal transport anisotropy, with the c a...

Becker et al., (2025). hashin_shtrikman_mp: a package for the optimal design and discovery of multi-phase composite materials. Journal of Open Source Software, 10(114), 8412, https://doi.org/10.21105/...

npj Computational Materials - Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

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Lawrence Berkeley National Lab’s (LBNL) Energy Storage & Distributed Resources Division has an opening for a Postdoctoral Scholar in AI-Driven...

Computational catalysis and machine learning applications to water treatment technologies, Wang, Duo, Xie, Ao, Ma, Shengcun, Tong, Wei, Zou, Shiqiang, Jain, Anubhav

Materials design and informatics have become increasingly prominent over the past several decades. Using the Materials Project as an example, this Perspective discusses how properties are calculated a...

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of...

Riebesell et al. introduce Matbench Discovery, a framework to compare machine learning models used to identify stable crystals. Out of several architectures, they find that universal interatomic poten...