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What Is the Semiconductor Front-End Process? A Materials Simulation Guide to Deposition, Lithography, Etching, and CMP

Semiconductor devices are manufactured by forming nanoscale structures on silicon wafers. This involves repeating numerous processes such as film deposition, photolithography, etching, ion implantation, and planarization to create fine circuit structures on the wafer. In these manufacturing processes, the physical properties of the materials and the reactions occurring at the surface and interface significantly impact device performance and yield. In recent years, the micro-scale of devices has been a key factor.

Explainer Computational Chemistry

From Lab to Deployment: Three Computational Trends Shaping the Future of Industry at TechConnect 2026

Joshua Young

In early March, the Matlantis US team headed down to Raleigh, NC, for TechConnect World 2026. It’s one of the largest conferences in the country for applied innovation, covering everything from advanced materials and energy to sustainability and AI. While a conference like TMS (which we headed to right afterwards, see our report here

Conference Report

Three Computational Trends Reshaping Materials Science from TMS 2026

Qing-Jie Li

From March 15-19, 2026, the TMS 2026 Annual Meeting was held in San Diego under clear skies, bringing together thousands of materials scientists and engineers to share the latest research findings. Our Matlantis team also joined the circle of over 4,000 participants and presented our research results. Attending numerous sessions and directly interacting with leaders who are driving

Conference Report

AI pioneers materials development through computational chemistry

Bon Cho

This article is a blog post based on an article written by Zhang, Customer Success Engineer Matlantis Corporation, which was published in the February 2026 issue of the technical magazine "Monthly Material Stage" in the special feature "Improving the Efficiency of Materials Development Using AI and Automated Experiments." 1. Materials Development to Date Our lives have been enriched by numerous innovative materials. However,

DFT Molecular Dynamics Machine Learning Interatomic Potentials Explainer

Explainer : Why Did the AI Predict That ? Uncovering Atomic-Level Interpretability through PFP Descriptors and Shapley Values

Bon Cho

The online proceedings for the Spring Meeting of the Japan Society of Applied Physics have been published, and it feels like the academic conference season has finally arrived. I'm sure there are many people who have submitted their proceedings and are busy preparing their presentations, and many others who are looking at the published proceedings and planning to attend. At the 86th Autumn Meeting of the Japan Society of Applied Physics last year, I gave a presentation entitled "Interpretation of predictions at the atomic level by combining PFP descriptors and Shapley values."

Materials Informatics Explainer Computational Chemistry

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Accelerating free energy calculations using molecular dynamics with the MN-Core 2 deep learning processor

Free energy estimation using a diffusion model

Development of excited state neural network potential

Application of deep learning to chemical reactions

GraphNVP

Cheminformatics Bioinformatics Drug Discovery

High-speed X-ray crystallography using PFP

Materials informatics crystal structure prediction

High-speed crystal structure prediction using PFP and development of its efficient algorithm

Materials informatics crystal structure prediction

Generalization evaluation of machine learning scoring functions for structure-based virtual screening

Materials Informatics computational chemistry

Compound design using deep reinforcement learning

Chemoinformatics Bioinformatics

Universal Neural Network Potential for Materials Discovery

Materials Informatics Machine Learning Interatomic Potentials

Accelerating protein-compound docking simulations using GPUs