Related papers: Accelerating Multi-Objective Collaborative Optimiz…
High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly…
In this paper we focus on analyzing the thermal modality of tactile sensing for material recognition using a large materials database. Many factors affect thermal recognition performance, including sensor noise, the initial temperatures of…
Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development. Existing machine learning models have focused on…
A thermoelectric device is a heat engine that directly converts heat into electricity. Many materials with a high figure of merit ZT have been discovered in anticipation of a high thermoelectric efficiency. However, there has been a lack of…
Two dimensional materials are attracting new research for optoelectronics and spintronics due to their unique physical properties. A wide range of emerging spintronic devices are achieved from parent and doped two dimensional materials.…
Enhancing the dimensionless figure of merit zT is central to developing better thermoelectric materials and advancing thermoelectric generation technology. However, the intrinsic interdependence between electrical conductivity, the Seebeck…
We explore the possibility to perform an in-situ transmission electron microscopy (TEM) thermoelectric characterization of materials. A differential heating element on a custom in-situ TEM microchip allows to generate a temperature gradient…
Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories. Scattering experiments are central to verifying the atomic…
Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy…
With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the…
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan,…
The machine learning based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often…
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery…
Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-initio calculations, which are only feasible for very simple…
Understanding the physics of structurally and chemically complex transition-metal oxide and polyanionic materials such as those used for battery electrodes is challenging, even at the level of pristine compounds. Yet these materials are…
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with…
Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and…
Calibration of dynamic models to data is an important step in building building digital twins of HVAC equipment, thermal loads and control systems. Sometimes, when a model fails to calibrate to data, a possible cause is that the model has…
Inhomogeneously doped thermoelectric nanomaterials with a delta-function electronic density of states can operate with Carnot efficiency in the absence of phonon heat leaks. Here we self-consistently calculate the efficiency and power from…
Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we…