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The effect of melting in planetary mantles plays a key role in their thermo-chemical evolution. Because of the laterally heterogeneous nature of melting, 3D numerical simulations are in principle necessary prohibiting us from exploring wide…
Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive…
Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes and structures. In the current study,…
Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials and humans and robots at human body…
Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of…
Thermal diffusivity measurements of samples transmitting thermal radiation require adjustments to the data treatment procedures in laser flash analysis. Conventionally, an unconstrained diathermic model is used. Current results show that…
This short paper presents the potential of using machine learning to predict materials behaviour in the context of hydrogen interaction with steel. Effort has been made to understand the quality, and amount of data needed to get improved…
Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (T_m) and glass transition temperature (T_g)…
Speculative decoding stands as a pivotal technique to expedite inference in autoregressive (large) language models. This method employs a smaller draft model to speculate a block of tokens, which the target model then evaluates for…
Simulated annealing is an effective and general means of optimization. It is in fact inspired by metallurgy, where the temperature of a material determines its behavior in thermodynamics. Likewise, in simulated annealing, the actions that…
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such…
The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and…
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active-learner, which is…
Our paper presents a non-destructive thermal transient measurement method that is able to reveal differences even in the micron size range of MEMS structures. Devices of the same design can have differences in their sacrificial layers as…
Oxide scale formed on the surface of steel products during high-teperature processes is studied as a composite material, the main solid components of which, in general, are wustite, magnetite, hematite and metallic iron. To estimate the…
The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some…
The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional thermal conductivity polymers…
With the advances in materials and integration of electronics and thermoelectrics, the demand for novel crystalline materials with ultimate high/low thermal conductivity is increasing. However, search for optimal thermal materials is…
The thermal characteristics of silicon between 15 and 300 deg K are investigated by applying a computer program on the solution of the differential heat diffusion equation. The computer model is linked to high-purity silicon through a set…
Temperature is a fundamental regulator of chemical and biochemical kinetics, yet capturing nonlinear thermal effects directly from experimental data remains a major challenge due to limited throughput and model flexibility. Recent advances…