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Artificial neural network modeling does not need to consider the mechanism. It can map the implicit relationship between input and output and predict the performance of the system well. At the same time, it has the advantages of…

Chemical Physics · Physics 2021-04-06 Jinwei Lu , Ningrui Zhao

Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this…

Soft Condensed Matter · Physics 2026-02-20 Rigoberto Advincula , Jihua Chen

Polymers are attractive in applications like flexible electronics and thermal interface materials due to their mechanical compliance and processability. However, conventional polymers have low thermal conductivity (TC), limiting their heat…

Materials Science · Physics 2026-03-25 Yuhan Liu , Jiaxin Xu , Renzheng Zhang , Meng Jiang , Tengfei Luo

The potential of machine learning to automate and control nonlinear, complex systems is well established. These same techniques have always presented potential for use in the investment arena, specifically for the managing of equity…

Portfolio Management · Quantitative Finance 2011-10-18 Evan Hurwitz , Tshilidzi Marwala

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for…

Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a…

Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision…

Machine Learning · Computer Science 2019-07-22 Mathias Kraus , Stefan Feuerriegel

We present an explicit theoretical framework for constructing artificial modular coils based solely on equilibrium properties, achieved through the formulation of a current potential defined on flux surfaces. We demonstrate that key…

Plasma Physics · Physics 2026-04-15 Eduardo Rodriguez , Wrick Sengupta

Predictive materials synthesis is the primary bottleneck in realizing new functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error approaches and there are…

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards…

Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…

Materials Science · Physics 2022-08-15 Michele Ceriotti

Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…

Materials Science · Physics 2019-05-24 Rajendra P. Joshi , Jesse Eickholt , Liling Li , Marco Fornari , Veronica Barone , Juan E. Peralta

Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and…

Neural and Evolutionary Computing · Computer Science 2009-07-06 S. Butdee , Chaiwat Noomtong , Serge Tichkiewitch

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a…

Materials Science · Physics 2025-02-14 Hossein Tahmasbi , Kushal Ramakrishna , Mani Lokamani , Attila Cangi

We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set…

Machine Learning · Computer Science 2020-10-23 Nikolaos N. Vlassis , WaiChing Sun

Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approaches for generating potentials are promising; however neural…

Understanding and predicting the emergence of novel materials is a fundamental challenge in condensed matter physics, materials science and technology. With the rapid growth of materials databases in both size and reliability, the challenge…

Materials Science · Physics 2025-02-14 Jacopo Moi , Davide Spallarossa , Stefano Bonetti , Raffaella Burioni , Guido Caldarelli

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current…

Computational Engineering, Finance, and Science · Computer Science 2024-06-07 Tom Savage , Nausheen Basha , Jonathan McDonough , James Krassowski , Omar K Matar , Ehecatl Antonio del Rio Chanona

Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble…

Machine Learning · Statistics 2023-03-22 Zahra Zanjani Foumani , Mehdi Shishehbor , Amin Yousefpour , Ramin Bostanabad

Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials properties, and the fine-tuning of automated hardware protocols.…

Optimization and Control · Mathematics 2021-11-19 Matteo Aldeghi , Florian Häse , Riley J. Hickman , Isaac Tamblyn , Alán Aspuru-Guzik
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