Publications

A grand challenge of materials science is predicting synthesis pathways for novel compounds. Data-driven approaches have made significant progress in predicting a compound’s synthesizability; however, some recent attempts ignore phase stability information. Here, we combine thermodynamic stability calculated using density functional theory with composition-based features to train a machine learning model that predicts a material’s […]

Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity κL through large compositional disorder. However, here it is shown that for a given mechanism, such as mass contrast phonon scattering, κL will be minimized along the binary alloy with highest mass contrast, such that adding an intermediate mass atom to increase […]

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, mesoscale, and continuum […]

Catalysts are the backbone of industrial chemistry. They speed up reactions and make possible what was once impossible. Molecular recycling, room temperature electrochemical reactions to make ammonia, carbon capture – all key to our future and all dependent on catalysts. Unfortunately, catalysis is hard! Reactions have many moving parts all happening at once. There is […]

Corrosion is an electrochemical phenomenon. It can occur via different modes of attack, each having its own mechanisms, and therefore there are multiple metrics for evaluating corrosion resistance. In corrosion resistant alloys (CRAs), the rate of localized corrosion can exceed that of uniform corrosion by orders of magnitude. Therefore, instead of uniform corrosion rate, more […]

The development of transformative technologies for mitigating our global environmental and technological challenges will require significant innovation in the design, development, and manufacturing of advanced materials and chemicals. To achieve this innovation faster than what is possible by traditional human intuition-guided scientific methods, we must transition to a materials informatics-centered paradigm, in which synergies between […]

The design of chemical formulations is a challenging, high-dimensional problem. In typical formulations, tens of thousands of ingredients are available for use, yet only a tiny fraction end up in a given formulation. Deformulation, the problem of reverse engineering the precise amounts of each ingredient starting from just a list of ingredients, is similarly challenging […]

Lu-doped zirconia (LuDZ) was reported in the 1960s to be a poor oxygen ion conductor. However, we identified this system as worthy of reexamination based on the results of a machine learning-based screen for promising oxygen ion conductors, further supported by a physical argument: because Lu is the heaviest and smallest of the lanthanides, it should offer the possibility of […]

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work […]

Over the past several years, the field of materials informatics has grown dramatically. Applications of machine learning (ML) and artificial intelligence (AI) to materials science are now commonplace. As materials informatics has matured from a niche area of research into an established discipline, distinct frontiers of this discipline have come into focus, and best practices […]