27 papers · ranked by Valyu relevance
Authors not listed
Machine learning has become a pivotal tool in materials discovery, but conventional data-driven models often behave as “black boxes” limited by the scope of their training data. In this perspective, we outline the current status of machine learning-driven materials discovery and argue that incorporating scientific…
Joseph H. Montoya, Kirsten T. Winther, Raul A. Flores, Thomas Bligaard + 2 more
'Thomas Bligaard' 'Jens S. Hummelshøj' 'Muratahan Aykol'] We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments…
Anthony K. Cheetham, Ram Seshadri
Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery Authors: ['Anthony K. Cheetham' 'Ram Seshadri'] The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new…
Sterling Baird, Tran Diep, Taylor Sparks
We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several…
Sterling Baird, Tran Diep, Taylor Sparks
We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several…
Michael Alverson, Sterling Baird, Ryan Murdock, Taylor Sparks
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal…
Yuki Inada, M. Fujioka, Haruhiko Morito, Tohru Sugahara + 2 more
'Hisanori Yamane' 'Yukari Katsura'] When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for…
Muratahan Aykol, Vinay I. Hegde, Linda Hung, Santosh K. Suram + 3 more
'Patrick Herring' 'Chris Wolverton' 'Jens S. Hummelshøj'] Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on…
Sean M. Stafford, Alexander A. Aduenko, Marcus Djokic, Yu-Hsiu Lin + 1 more
'José L. Mendoza-Cortés'] We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that…
Imran, Faiza Qayyum, Do-Hyeun Kim, Seon-Jong Bong + 3 more
'Yo-Han Choi' 'Łukasz Sadowski'] Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for…
Authors not listed
Although all molecular assemblies show some degree of flexibility, the past decade has shown that there is a higher propensity among framework materials to display large-scale dynamic behavior. Beyond the seminal discoveries of the important flexibility of metal–organic frameworks (MOFs), covalent organic frameworks…
Anthony K. Cheetham, Ram Seshadri, Fred Wudl
Functional materials impact every area of our lives ranging from electronic and computing devices to transportation and health. In this Perspective, we examine the relationship between synthetic discoveries and the scientific breakthroughs that they have enabled. By tracing the development of some important examples…
Daniel W. Davies, Keith T. Butler, Adam J. Jackson, Andrew Morris + 3 more
'Andrew Morris' 'Jarvist\xa0M. Frost' 'Jonathan\xa0M. Skelton' 'Aron Walsh'] Title: Summary Forming a four-component compound from the first 103 elements of the periodic table results in more than 1012 combinations. Such a materials space is intractable to high-throughput experiment or first-principle computation. We…
Authors not listed
High-throughput computational tools and generative AI models aim to revolutionise materials discovery by enabling the rapid prediction of novel inorganic compounds. However, these tools face persistent challenges with modelling compounds where multiple elements occupy the same crystallographic site, often leading to…
Josh Leeman, Yuhan Liu, Joseph Stiles, Scott Lee + 3 more
Materials discovery lays the foundation for many technological advancements. Predicting and discovering new materials are not simple tasks. We here outline some basic principles of solid-state chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery. Using the recent work…
Authors not listed
Data-driven strategies are reshaping computational materials design by accelerating the prediction of novel compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and…
Caichao Ye, Tao Feng, Weishu Liu, Wenqing Zhang
New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively…
Daniel Shechtman
Why were quasi-periodic materials not discovered before 1982? For 70 years, hundreds of thousands of crystals were discovered and analyzed by X-ray crystallographers, and not one saw quasi-periodic materials. Quasi-periodic materials are not rare. There are hundreds upon hundreds of them. A partial list of some of the…
Xiaodong Zeng, Jiahao Qiu, Xin Zhao, Kangfei Liu + 9 more
The exponential growth of scientific publications presents opportunities for researchers to identify valuable knowledge, especially in the highly interdisciplinary field --- biomaterials, where exploiting possible connections between unmet clinical needs and materials properties from literatures is crucial. However…
Aliaksei Vasilevich, Aurélie Carlier, David A. Winkler, Shantanu Singh + 1 more
Natural evolution tackles optimization by producing many genetic variants and exposing these variants to selective pressure, resulting in the survival of the fittest. We use high throughput screening of large libraries of materials with differing surface topographies to probe the interactions of implantable device…
Yewei Xiao, Xi Zeng, Zhaoxiang Yang, Junwei Gu + 22 more
Artificial intelligence is increasingly used to accelerate scientific discovery, but most successful frameworks operate within well-defined molecular, protein or materials spaces. Living materials present a more formidable computational problem because functions emerge from context dependent coupling among cells…
Tao Tao Qiang, Honghong Gao
Materials have been central to the growth, prosperity, security, and life quality of humans since the very beginning of history. Computational materials science has gained unprecedented progress, since the concept of materials science was introduced into university teaching for common usage in USA in the 1950s. More…
P. C. Canfield
This review presents a survey of, and guide to, New Materials Physics research. It begins with an overview of the goals of New Materials Physics and then presents important ideas and techniques for the design and growth of new materials. An emphasis is placed on the use of compositional phase diagrams to inform and…
Cristina Izquierdo-Lozano, Marrit M.E. Tholen, Valentina Girola, Anna Świetlikowska + 3 more
Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored…
Mathias Peirlinck, Kevin Linka, Juan A. Hurtado, Ellen Kuhl
Constitutive modeling is the cornerstone of computational and structural mechanics. In a finite element analysis, the constitutive model is encoded in the material subroutine, a function that maps local strains onto stresses. This function is called within every finite element, at each integration point, within every…
Motoko Kotani, Susumu Ikeda
Our world is transforming into an interacting system of the physical world and the digital world. What will be the materials science in the new era? With the rising expectations of the rapid development of computers, information science and mathematical science including statistics and probability theory, ‘data-driven…
Dan Xue, Mingming Xu, Michael D. Madden, Xiaoying Lian + 15 more
Discovery of cancer immunogenic chemotherapeutics represents an emerging, highly promising direction for cancer treatment that uses a chemical drug to achieve the efficacy of both chemotherapy and immunotherapy. Herein we report a high-throughput screening platform and the subsequent discovery of a new class of cancer…